Department of Electrical and Computer Engineering
Jelena Kovačević, Head
Diana Marculescu, Associate Head

http://www.ece.cmu.edu/

The field of electrical and computer engineering encompasses a remarkably diverse and fertile set of technological areas, including analog and digital electronics, computer architecture, computer-aided design and manufacturing of VLSI/ULSI circuits, intelligent robotic systems, computer-based control systems, telecommunications and computer networking, wireless communication systems, signal and information processing and multimedia systems, solid state physics and devices, microelectromechanical systems (MEMS), electromagnetic and electromechanical systems, data storage systems, embedded systems, distributed computing, mobile computing, real-time software, digital signal processing, and optical data processing. The extraordinary advances in the field during the last fifty years have impacted nearly every aspect of human activity. These advances have resulted not only in advanced computer systems but also in consumer products such as “smart” cars, programmable dishwashers and other home appliances, cell phones and mobile computing systems, video games, home security systems, advanced medical systems for imaging, diagnosis, testing and monitoring. Systems and products such as these serve to enhance our quality of life and have also served as the basis for significant economic activity. In short, the field of electrical and computer engineering has become central to society as we know it.

The Department of Electrical and Computer Engineering at Carnegie Mellon is actively engaged in education and research at the forefront of these new technologies. Because of the diverse and broad nature of the field and the significant growth in knowledge in each of its sub areas, it is no longer possible for any single individual to know all aspects of electrical and computer engineering.  Nevertheless, it is important that all electrical and computer engineers have a solid knowledge of the fundamentals with sufficient depth and breadth. Society is placing increasing demands on our graduates to try their skills in new contexts.  It is also placing increasing value on engineers who can cross traditional boundaries between disciplines, and who can intelligently evaluate the broader consequences of their actions.  Our curriculum is designed to produce world-class engineers who can meet these challenges.

 

 

Educational Outcomes and Objectives

The B.S. in Electrical and Computer Engineering is a broad and highly flexible degree program structured to provide students with the smallest set of constraints consistent with a rich and comprehensive view of the profession. It is accredited by the Engineering Accreditation Commission of ABET, http://www.abet.org. Students are encouraged and stimulated to explore multiple areas of theory and application.  The Faculty of Electrical and Computer Engineering have adopted the following outcomes from ABET and have established the following objectives for the B.S. in Electrical and Computer Engineering curriculum:

Educational Outcomes

  1. An ability to apply knowledge of mathematics, science and engineering.
  2. An ability to design and conduct experiments, as well as to analyze and interpret data.
  3. An ability to design a system, component or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability and sustainability.
  4. An ability to function in multi-disciplinary teams.
  5. An ability to identify, formulate and solve engineering problems.
  6. An understanding of professional and ethical responsibilities.
  7. An ability to communicate effectively.
  8. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental and societal context.
  9. A recognition of the need for, and an ability to engage in life-long learning.
  10. A knowledge of contemporary issues.
  11. An ability to use the techniques, skills and modern engineering tools necessary for engineering practice.

ECE Education Objectives

The ECE program objectives are shown below. They represent our vision for what our students will be doing in their engineering careers five years after they have graduated. The principal behaviors we seek to foster in our students are expertise, innovation and leadership.
Our graduates will be:

Experts
  • They will solve problems by applying ECE fundamentals
  • Their solutions will reflect depth of understanding in their sophistication.
  • Their solutions will reflect breadth of understanding by drawing on multiple disciplines.
Innovators
  • They will demonstrate creativity in their engineering practice.
  • They will consider holistic systems-oriented approaches in their designs.
  • They will think strategically in their planning and execution.
Leaders
  • They will take initiative, and demonstrate resourcefulness.
  • They will collaborate in multidisciplinary teams.
  • They will be leaders in their organizations, their profession and in society.
Three dimensions of objectives for our graduates: Leaders, Experts, and Innovators

Three dimensions of objectives for our graduates.

 

Curriculum Overview

In addition to the Carnegie Institute of Technology general education and First Year requirements (143 units), the B.S. in Electrical and Computer Engineering requires: 15-122 Principles of Imperative Computation (10 units), Physics II (12 units), two math or science electives (18 units), a Probability and Statistics course (9 units), 109 units of Electrical and Computer Engineering coursework, and 2 math co-requisites (22 units). The remaining units needed to reach the 379 required to graduate are Free Electives (56 units).

The Electrical and Computer Engineering coursework is divided into the categories of Core, Area Courses, Coverage, and Capstone Design.  The Core consists of five courses (18-100 Introduction to Electrical and Computer Engineering, 18-220 Electronic Devices and Analog Circuits, 18-240 Structure and Design of Digital Systems, 18-213 Introduction to Computer Systems, and 18-290 Signals and Systems).  There are additional co-requisites: 18-202 Mathematical Foundations of Electrical Engineering21-127 Concepts of Mathematics and 33-142 Physics II for Engineering and Physics Students, that are required to be taken with the core.  These courses provide the fundamental knowledge-base upon which all other electrical and computer engineering courses are built. 

Students generally take 18-100 Introduction to Electrical and Computer Engineering during their first year, while they start the remaining courses in the Core in their sophomore year, ideally completing them by the end of the junior year. It is recommended that students do not take more than two core courses in the same semester.  Although the core courses (and their co-requisites) may be taken in any order, students generally first take the course in their primary area of interest, which gives added flexibility to later course selection in related areas.

Students are required to complete a seminar course during the fall semester of the sophomore year.  This course, 18-200 ECE Sophomore Seminar, introduces students to the many areas within ECE and helps them decide which areas are of primary interest to them.

To satisfy the ECE Area Courses Requirement, at least two Area courses must be completed from one of the following five principal areas in ECE (24 units): 

  • Device Sciences and Nanofabrication: Solid State Physics, Electromagnetic Fields and Waves, Magnetics, Optics, etc.;
  • Signals and Systems: Digital Signal Processing, Communication Systems, Control Systems, etc.;
  • Circuits: Analog and Digital Circuits, Integrated Circuit Design, etc.;
  • Computer Hardware: Logic Design, Computer Architecture, Networks, etc.; and
  • Computer Software: Programming, Data Structures, Compilers, Operating Systems, etc. 

One additional course from a second area must be taken (12 units)

The Coverage requirement is satisfied by taking any additional ECE course(s) or an approved Computer Science course (see the ECE website for the list of approved coverage courses) totaling at least 12 units.

All students are required to take a Capstone Design course.  The Capstone Design course is a senior-level project course (numbered 18-5XX) in which students participate in a semester-long design experience on a team with other students.  Students learn project management skills, create oral presentations, write reports, and discuss the broader social and ethical dimensions of ECE. At the completion of the course students will conclude with a demonstration of their product and will be able to explain the design process. Current Capstone Design courses are listed on the ECE Department website.

B.S. Curriculum

Minimum units required for B.S. in Electrical and Computer Engineering379

For detailed information and regulations of the curriculum along with the degree requirements and the most recent version of the ECE curriculum and course descriptions, please refer to the ECE Academic Guide.

University Requirement

Units
99-101Computing @ Carnegie Mellon3
or 99-102 Computing @ Carnegie Mellon
 3

CIT Requirements (see CIT section of the catalog for specifics):

CIT General Education Units
Two semesters of calculus20
One other introductory engineering course (generally taken during the freshman year)12
33-141Physics I for Engineering Students **12
or 33-131 Matter and Interaction I
 44

** 33-141/33-142 is the recommended course sequence, although 33-131/33-132 will also satisfy this requirement.

Specific ECE requirements:

Units
One Introduction to Electrical and Computer Engineering course (generally taken during the freshman year)
18-100Introduction to Electrical and Computer Engineering12
One ECE Seminar, taken during the fall of the sophomore year
18-200ECE Sophomore Seminar1
Four ECE core courses, three with math co-requisites
18-220Electronic Devices and Analog Circuits12
Physics II for Engineering and Physics Students
(co-requisite for 18-220)
Mathematical Foundations of Electrical Engineering
(co-requisite for 18-220)
18-290Signals and Systems12
Mathematical Foundations of Electrical Engineering
(co-requisite for 18-290)
18-240Structure and Design of Digital Systems12
Concepts of Mathematics
(co-requisite for 18-240)
18-213Introduction to Computer Systems12
Two Area Courses from 1 of the 5 Areas within ECE24
One additional Area Course from a second Area12
One Coverage Course (any additional ECE course or Approved CS course as listed on the ECE web site) 12
One Capstone Design Course (any 18-5xx course)12
 121

Other ECE Requirements:

Units
15-112Fundamentals of Programming and Computer Science12
15-122Principles of Imperative Computation10
Two Math/Science electives18
36-217Probability Theory and Random Processes9
or 36-225 Introduction to Probability Theory
Free Electives56
 105
Math/Science Electives

The math/science electives are satisfied with any course from The Mellon College of Science or The Department of Statistics and Data Science except for: 100-level courses in Mathematics or Statistics, and courses designed for non-science or engineering majors, such as (but not limited to) 03-132, 09-103, 09-108, 21-240, 21-257, 33-115, 33-124, 36-201, 36-202, 36-207 or 36-208.  Although shown in the Junior and year, these courses may be taken at any time.  Mathematics courses of particular interest to students in ECE are:

21-228Discrete Mathematics9
21-241Matrices and Linear Transformations10
21-259Calculus in Three Dimensions9
21-260Differential Equations9
Free Electives56 units

A Free Elective is defined as any graded course offered by any academic unit of the university (including research institutes such as the Robotics Institute and the Software Engineering Institute).  A total of at least 56 units of Free Electives must be taken.

Up to 9 units of Student Taught Courses (StuCO) and Physical Education courses, or other courses taken as Pass/Fail, may also be used toward Free Electives.

Transfer of courses from other high-quality universities may be accepted through submission of the Transfer Credit Request form on the CIT web page. Please see the CIT website for further information regarding the process.

The large number of units without categorical constraints provides the student, in consultation with their Advisor or Mentor, with the flexibility to design a rich educational program.

Sample Curriculum

The following table shows a possible roadmap through our broad and flexible curriculum:

FreshmanSophomore
FallSpringFallSpring
18-100 Introduction to Electrical and Computer EngineeringIntroductory Engineering course18-200 ECE Sophomore Seminar18-2xx ECE Core course
15-112 Fundamentals of Programming and Computer Science33-106 Physics I for Engineering Students18-2xx ECE Core Course21-127 Concepts of Mathematics or 18-202 Mathematicl Foundations of Electrical Engineering
21-120 Differential and Integral Calculus21-122 Integration and Approximation18-202 Mathematical Foundations of Electrical Engineering or 21-127 Concepts of Mathematics15-122 Principles of Imperative Computation
76-101 Interpretation and ArgumentGeneral Education courseGeneral Education course36-217 Probability Theory and Random Processes
99-101 Computing @ Carnegie Mellon33-142 Physics II for Engineering and Physics StudentsGeneral Education course
39-210 Experiential Learning I39-220 Experiential Learning II

JuniorSenior
FallSpringFallSpring
18-2xx ECE Core course18-2xx ECE Core course18-xxx ECE Coverage course18-5xx ECE Capstone Design course
18-3xx/4xx ECE Area 1 course (first course in Area)18-3xx/4xx ECE Area course (either 2nd course from Area 1 or the Area 2 course)18-3xx/4xx ECE Area course (either 2nd course from Area 1 or the Area 2 course)General Education course
General Education courseMath/Science Elective 2General Education courseFree Elective
Math/Science elective 1General Education courseFree ElectiveFree Elective
Free ElectiveFree ElectiveFree ElectiveFree Elective
39-310 Experiential Learning III

Academic Policies

Policy on ECE Coverage Courses with Fewer than 12 Units

The basic curriculum requirements for Area courses, Coverage and Capstone Design are stated in terms of courses rather than units. The nominal total of 60 units for these categories is determined by assuming that each course is 12 units. In the event that courses with fewer than 12 units are used to satisfy some or all of these requirements, additional courses from the ECE coverage lists must be taken until the total units in ECE courses beyond the core meets or exceeds 60 units. Any ECE coverage course is acceptable, and any excess units beyond the required 60 may be counted as free elective credit.

QPA Requirement and Overload Policy

An overload is defined as any schedule with more than 54 units in one semester.  A student will only be permitted to overload by 12 units if she or he achieved an overall QPA of at least 3.5 out of 4.0. If the student's overall QPA is below a 3.5, then the QPA of the previous semester for which he or she is registering will instead be utilized. If that QPA is at least a 3.5 then the student will be permitted to Overload.

Grade Policy for Math Courses

1. CIT states that all mathematics (21-xxx) courses required* for the engineering degree taken at Carnegie Mellon must have a minimum grade of C in order to be counted toward the graduation requirement for the BS engineering degree.

2. A minimum grade of C must be achieved in any required mathematics (21-xxx) course that is a prerequisite for the next higher level required mathematics (21-xxx) course.

3. In addition, ECE requires that 18-202 Mathematical Foundations of Electrical Engineering must be completed with a grade of C or better.

*Elective mathematics courses (like the math/science electives required for ECE) are not included in this policy

Pass/Fail policy

Up to 9 units of StuCo and/or Physical Education courses or other courses taken as Pass/Fail may be used toward Free Electives.  ECE core courses may not be taken as pass/fail.  ECE project-based courses (including capstone design courses) may not be taken pass/fail.  No ECE requirements may be fulfilled using a pass/fail course (except for 99-10x and 18-200)

Other Graduation Requirements

To be eligible to graduate, undergraduate students must complete all course requirements for their program with a cumulative Quality Point Average of at least 2.0. For undergraduate students who enrolled at Carnegie Mellon as freshmen and whose freshman grades cause the cumulative QPA to fall below 2.0, this requirement is modified to be a cumulative QPA of at least 2.0 for all courses taken after the freshman year. Note, however, the cumulative QPA that appears on the student's final transcript will be calculated based on all grades in all courses taken, including freshman year. Students are encouraged to confirm all graduation requirements with their academic advisor.

CIT has the following requirement for graduation. “Students must complete the requirements for their specified degrees with a cumulative quality point average of 2.00 or higher for all courses taken after the freshman year [this is the CIT QPA on the Academic Audit]. In addition, a student is expected to achieve a cumulative quality point average of 2.00 in a series of core departmental courses.” 

In ECE, this means that the student must complete 18-100 Introduction to Electrical and Computer Engineering, ECE Core, Area Courses, Coverage, and Capstone Design courses with a minimum QPA of 2.0 to graduate. When more than one possibility exists for meeting a specific requirement (e.g., Area Course), the courses used for calculating the ECE QPA will be chosen so as to maximize the QPA. Similarly, when an ECE course is retaken, the better grade will be used in the computation of the minimum QPA for the ECE QPA requirement to graduate.

Other Opportunities in ECE

ECE Cooperative Education Program

Our Cooperative Education Program invites students to gain valuable experience in employment that relates directly to their major and career goals. At the same time, it provides employers with opportunities to evaluate students as potential full-time employees, while having them complete meaningful projects. Participation in this program is voluntary, and obtaining a cooperative education assignment is competitive.

Due to federal restrictions on student work experiences, international students are not eligible for co-ops. Please visit the ECE CPT page for information regarding international student internships.

The co-op experience

We require a minimum of eight months of co-op experience to identify the work experience as a co-op. Students must have minimally completed their sophomore year to qualify for application to a co-op and should connect with their Academic Advisor for information on how to apply. While on co-op assignment, students are participating in a recognized CIT educational program, retaining their full-time student status, akin to our students who study abroad in established exchange programs (such as EPFL) for one or two semesters. The Cooperative Education Program agreement may be discontinued if the employers do not provide the students with career-related work experience or if the students do not meet the accepted level of performance as defined by the employers. 

Upon returning to Carnegie Mellon, the students are required to submit for approval the following two documents to the ECE Undergraduate Office: a three to five page technical report of the Co-Op work, and a one page assessment and evaluation of the Co-Op experience.

Students may obtain more detailed information through the ECE department or the Career and Professional Development Center.

Integrated M.S./B.S. Degrees Program

The Integrated Master’s/Bachelor’s program (otherwise known as the IMB program) is an exciting opportunity for students who excel academically to achieve not just a Bachelor’s degree in ECE, but also a Master’s degree- through our Professional MS degree program-without needing to apply separately.  This means no application fee, and no need to take the GRE (Graduate Record Exam). In order to be awarded the MS degree in the IMB program, the student must also earn their BS degree, either simultaneously with the MS degree or at least one semester prior to the awarding of the MS degree. If a course is eligible for the MS degree but must be used to complete the BS degree, the BS degree takes priority over the MS degree.

If a student is at least a 2nd semester junior, has completed at least 270 units and has at least an overall 3.00 QPA, he or she is guaranteed admission into the Professional MS degree in ECE through the IMB program. To be officially admitted, the student must complete the IMB Program form.

If a student does not meet the exact overall 3.00 QPA requirement, he or she is eligible to petition for his or her admission into the IMB program during his or her senior year. Students may obtain the petition forms through a meeting with their assigned academic advisor.
 

Professional MS Degree Requirements:

Please see the ECE web site for the requirements for the Professional MS degree.  For students in the ECE IMB program, all requirements for the Professional MS degree are in addition to the requirements for the BS in ECE.  No requirements for the MS degree may be used in any way toward the BS degree, including minors, additional majors or dual degrees.
 

Residency requirements and financial impacts:

Once a student in the IMB program has completed all of the requirements for the BS degree, he or she may become a graduate (Masters) student.  To do this, the student’s undergraduate degree is certified, and that student officially graduates with the BS degree.  Once a student’s undergraduate degree has been certified, no more courses may then be applied toward the BS degree.  This includes courses toward minors and additional majors, although students pursuing an undergraduate dual degree  with another department may still continue to apply additional coursework toward that second degree.

If a student takes more than 8 semesters to complete both the BS and MS degrees, then he or she must be a graduate student for at least one semester before graduating with the MS degree.

To determine the most appropriate time for an undergraduate student to become a graduate student, he or she should consult with Enrollment Services to understand how becoming a graduate student will affect financial aid, and with his or her academic advisor to determine a course schedule.  When a student is a graduate student through the IMB program, the department is able to provide some financial assistance through Teaching Assistantships.  Please see the ECE web site for further information regarding this financial assistance.

Course Descriptions

Note on Course Numbers

Each Carnegie Mellon course number begins with a two-digit prefix which designates the department offering the course (76-xxx courses are offered by the Department of English, etc.). Although each department maintains its own course numbering practices, typically the first digit after the prefix indicates the class level: xx-1xx courses are freshmen-level, xx-2xx courses are sophomore level, etc. xx-6xx courses may be either undergraduate senior-level or graduate-level, depending on the department. xx-7xx courses and higher are graduate-level. Please consult the Schedule of Classes each semester for course offerings and for any necessary pre-requisites or co-requisites.

18-090 Twisted Signals: Multimedia Processing for the Arts
Fall: 10 units
[IDeATe portal course] - This course presents an overview on manipulating and synthesizing sound, video, and control signals. Signals are the raw materials used in many forms of electronic art and design - electronic music, interactive art, video art, kinetic sculpture, and more. In these fields, signals are used to represent information about sound, images, sensors, and movement. By transforming and manipulating these types of signals, we are able to create powerful new tools for digital art, multimedia applications, music, responsive environments, video and sound installation, smart products, and beyond. In this course we will study Signal Processing from a practical point-of-view, developing tools that can be easily integrated into art-making using the graphical programming environment Max (a.k.a. Max/MSP/Jitter). We will present a survey of Signal Processing techniques used in the sonic and visual arts, and will discuss the mathematical theories underlying these techniques. Students will be encouraged to combine, modify, and extend working examples of software to create original digital artworks. Please note that there will be usage/materials fees associated with this course.
18-099 Special Topics: Mobile App Design & Development
Fall: 12 units
[IDeATe collaborative course] IDeATe is partnering with YinzCam to develop and offer a studio course on mobile app design and development. The course will leverage the extensive expertise of YinzCam on mobile-app development in the sports and entertainment space, both for real-time and asynchronous enrichment of the fan experience and the stadium experience. However, the lessons learned will apply to mobile-app development broadly. Issues covered will include cross-platform development, mobile video, streaming media, real-time content delivery, along with best practices in server-side cloud management for large-scale mobile-app deployment. Please note that this course is for students to take as one of their IDeATe concentration/minor options and will NOT fulfill a CIT/ECE requirement. Open to juniors and seniors. DC and MCS students should take the course after completing another IDeATe collaborative course.
Prerequisites: 62-150 or 15-104 or 18-090
18-100 Introduction to Electrical and Computer Engineering
Fall and Spring: 12 units
The goals of this freshman engineering course are: * To introduce basic concepts in electrical and computer engineering in an integrated manner; * To motivate basic concepts in the context of real applications; * To illustrate a logical way of thinking about problems and their solutions, and; * To convey the excitement of the profession. These goals are attained through analysis, construction and testing of an electromechanical system (e.g., a robot) that incorporates concepts from a broad range of areas within Electrical and Computer Engineering. Some of the specific topics that will be covered include system decomposition, ideal and real sources, Kirchhoff's Current and Voltage Laws, Ohm's Law, piecewise linear modeling of nonlinear circuit elements, Ideal Op-Amp characteristics, combinational logic circuits, Karnaugh Maps, Flip-Flops, sequential logic circuits, and finite state machines. 3 hrs. lec., 1 hr. rec., 3 hr. lab.
18-200 ECE Sophomore Seminar
Fall: 1 unit
"The class comprises of a series of lectures from our own faculty and alumni, Department and University staff, and student groups. Students are required to attend each lecture. The lectures are designed to serve the following purposes: 1. Introduce to students to the faculty member's research field and the most current world advancements in engineering and technology in that area; 2. Provide students a good understanding of our curriculum structure and the courses in various areas; 3. Present correlations between the present technological developments and our courses for each course area; 4. Introduce new undergraduate courses; 5. Advertise on-campus/off-campus research opportunities for undergraduate students and explain the corresponding research projects; 6. Motivate students with positive presentations on the importance of obtaining education and gaining self-learning ability; 7. Provide basic education on learning and working ethics."
Prerequisite: 18-100
18-202 Mathematical Foundations of Electrical Engineering
Fall and Spring: 12 units
This course covers topics from engineering mathematics that serve as foundations for descriptions of electrical engineering devices and systems. It is the corequisite mathematics course for 18-220, Fundamentals of Electrical Engineering. The topics include: 1.MATLAB as a robust computational tool, used to reinforce, enrich and integrate ideas throughout the course, including software exercises and projects in combination with homework assignments; 2.Complex Analysis, including rectangular and polar representations in the complex plane with associated forms of complex arithmetic, powers, roots and complex logarithms, complex differentiation, analytic functions and Cauchy-Riemann equations, complex Taylor series, complex exponential, sinusoidal and hyperbolic functions, and Euler's formula; 3.Fourier Analysis, including orthogonality of sinusoids, trigonometric and exponential forms of Fourier series, Fourier integrals and Fourier transforms; 4.Linear, Constant-Coefficient Differential Equations, including complex exponential solutions to homogeneous equations and particular solutions with polynomial and sinusoidal driving functions described by phasors; 5.Difference Equations, with emphasis upon their relationship to differential equations, and; 6.Linear Algebra and Matrices, including matrix arithmetic, linear systems of equations and Gaussian elimination, vector spaces and rank of matrices, matrix inverses and determinants, eigenvalue problems and their relationship to systems of homogeneous differential equations.
Prerequisite: 21-122 Min. grade C
18-213 Introduction to Computer Systems
Spring and Summer: 12 units
This course provides a programmer's view of how computer systems execute programs, store information, and communicate. It enables students to become more effective programmers, especially in dealing with issues of performance, portability and robustness. It also serves as a foundation for courses on compilers, networks, operating systems, and computer architecture, where a deeper understanding of systems-level issues is required. Topics covered include: machine-level code and its generation by optimizing compilers, performance evaluation and optimization, computer arithmetic, memory organization and management, networking technology and protocols, and supporting concurrent computation. NOTE: students must achieve a C or better in order to use this course to satisfy the pre-requisite for any subsequent Computer Science course. Prerequisites: 15-123 (Grade of C or higher is required in the prerequisite)
Prerequisite: 15-122 Min. grade C

Course Website: http://www.cs.cmu.edu/~213/
18-220 Electronic Devices and Analog Circuits
Fall and Spring: 12 units
This course covers fundamental topics that are common to a wide variety of electrical engineering devices and systems. The topics include an introduction to semiconductor devices and technology, DC circuit analysis techniques, operational amplifiers, energy storage elements, sinusoidal steady-state response, frequency domain analysis, filters, and transient response of first- and second-order systems. The laboratories allow students to use modern electronic instrumentation and to build and operate circuits that address specific concepts covered in the lectures, including semiconductor devices and sensors, layout, operational amplifiers, filters, signal detection and processing, power converters and circuit transients. 3 hrs. lec., 1 hr. rec., 3 hrs. lab.
Prerequisite: 18-100
Course Website: https://www.ece.cmu.edu/courses/items/18220.html
18-231 Sophomore Projects
Fall
The Department of Electrical and Computer Engineering at Carnegie Mellon considers experiential learning opportunities important educational options for its undergraduate students. One such option is conducting undergraduate research with a faculty member. Students do not need to officially register for undergraduate research unless they want it listed on their official transcripts. An ECE student who is involved in a research project and is interested in registering this undergraduate research for course credit on the official transcript may request to be enrolled in this course. To do this, the student should first complete the on-line undergraduate research form available on the ECE undergraduate student page. Once the form has been submitted and approved by the faculty member the student is conducting the research with, the ECE Undergraduate Office will add the course to the student's schedule. Typical credit is granted as one hour of research per week is equal to one unit of credit.
18-232 Sophomore Projects
Spring
The Department of Electrical and Computer Engineering at Carnegie Mellon considers experiential learning opportunities important educational options for its undergraduate students. One such option is conducting undergraduate research with a faculty member. Students do not need to officially register for undergraduate research unless they want it listed on their official transcripts. An ECE student who is involved in a research project and is interested in registering this undergraduate research for course credit on the official transcript may request to be enrolled in this course. To do this, the student should first complete the on-line undergraduate research form available on the ECE undergraduate student page. Once the form has been submitted and approved by the faculty member the student is conducting the research with, the ECE Undergraduate Office will add the course to the student's schedule. Typical credit is granted as one hour of research per week is equal to one unit of credit.
18-240 Structure and Design of Digital Systems
Fall and Spring: 12 units
This course introduces basic issues in design and verification of modern digital systems. Topics include: Boolean algebra, digital number systems and computer arithmetic, combinational logic design and simplification, sequential logic design and optimization, register-transfer design of digital systems, basic processor organization and instruction set issues, assembly language programming and debugging, and a hardware description language. Emphasis is on the fundamentals: the levels of abstraction and hardware description language methods that allow designers to cope with hugely complex systems, and connections to practical hardware implementation problems. Students will use computer-aided digital design software and actual hardware implementation laboratories to learn about real digital systems. 3 hr. lec., 1 hr. rec., 3 hr. lab.
Prerequisite: 18-100
18-290 Signals and Systems
Fall and Spring: 12 units
This course develops the mathematical foundation and computational tools for processing continuous-time and discrete-time signals in both time and frequency domain. Key concepts and tools introduced and discussed in this class include linear time-invariant systems, impulse response, frequency response, convolution, filtering, sampling, and Fourier transform. Efficient algorithms like the fast Fourier transform (FFT) will be covered. The course provides background to a wide range of applications including speech, image, and multimedia processing, bio and medical imaging, sensor networks, communication systems, and control systems. This course serves as entry and prerequisite for any higher level course in the fields of signal processing, communications, and control. Prerequisite(s): 18-100 Corequisite(s): 18-202
Prerequisite: 18-100
Course Website: http://www.ece.cmu.edu/~ece290
18-300 Fundamentals of Electromagnetics
Fall: 12 units
This course introduces electromagnetic principles and describes ways in which those principles are applied in engineering devices and systems. Topics include: vector calculus as a mathematical foundation for field descriptions, Maxwell's equations in integral and differential forms with associated boundary conditions as descriptions of all electromagnetic principles, quasistatic electric fields in free space and in materials, superposition for known charge sources, conduction and polarization, resistance and capacitance, charge relaxation, analytic and numerical methods for electric field boundary value problems, quasistatic magnetic fields in free space and in materials, superposition for known current sources, magnetization, inductance, magnetic diffusion, and analytic and numerical methods for magnetic field boundary value problems. 4 hrs. lec.
Prerequisite: 18-220
18-310 Fundamentals of Semiconductor Devices
Spring: 12 units
This course replaced 18311 in Spring 2005. In this course you will receive an introduction to the operation and fabrication of the most important semiconductor devices used in integrated circuit technology together with device design and layout. At the end of the course you will have a basic understanding of pn diodes, bipolar transistors, and MOSFETs as well as some light emitting and light detecting devices such as photodiodes, LEDs and solar cells. You will also receive an introduction to the fundamental concepts of semiconductor physics such as doping, electron and hole transport, and band diagrams. In the laboratory you will learn how to lay out both bipolar and MOS devices and you will design small (2-3 transistor) circuits. The laboratory portion of the course emphasizes the relation between device design and layout and circuit performance. You will also experimentally evaluate the operation of amplifier and gate circuits fabricated with discrete devices. This course will give you an excellent understanding of the operation and fabrication of the devices which is necessary for high-performance analog and digital circuit design. 3 hrs. lec. (Note: the prerequisite is typically waived for MSE students who intend to pursue the Electronic Materials Minor.)
Prerequisite: 18-220
18-320 Microelectronic Circuits
12 units
18-320 introduces students to the fundamentals of microelectronic circuits. The course will emphasize the analysis and design of basic analog and digital integrated circuits in preparation for further study in analog, digital, mixed-signal, and radio-frequency integrated circuit design. Additionally, students will learn to design and analyze microelectronic circuits using industry standard computer aided design (CAD) software. Topics to be covered include: MOSFET fabrication and layout MOSFET models for analog and digital design Analysis and design of digital CMOS logic gates Analysis and design of clocked storage elements (e.g., flip-flops, latches, memory cells) Delay optimization of digital circuits Circuit topologies for arithmetic and logical functional units Analysis and design of single-stage MOS amplifiers Frequency response characteristics of single-stage amplifiers Differential amplifiers and simple operational amplifiers Analog filters using operational amplifiers The course includes a lab component which will give students hands-on experience in the design and implementation of analog and digital circuits. Labs will employ both design using discrete, SSI, and MSI parts, as well as using CAD design tools.
Prerequisite: 18-220
18-331 Junior Projects
Fall
The Department of Electrical and Computer Engineering at Carnegie Mellon considers experiential learning opportunities important educational options for its undergraduate students. One such option is conducting undergraduate research with a faculty member. Students do not need to officially register for undergraduate research unless they want it listed on their official transcripts. An ECE student who is involved in a research project and is interested in registering this undergraduate research for course credit on the official transcript may request to be enrolled in this course. To do this, the student should first complete the on-line undergraduate research form available on the ECE undergraduate student page. Once the form has been submitted and approved by the faculty member the student is conducting the research with, the ECE Undergraduate Office will add the course to the student's schedule. Typical credit is granted as one hour of research per week is equal to one unit of credit.
18-332 Junior Projects
Spring
The Department of Electrical and Computer Engineering at Carnegie Mellon considers experiential learning opportunities important educational options for its undergraduate students. One such option is conducting undergraduate research with a faculty member. Students do not need to officially register for undergraduate research unless they want it listed on their official transcripts. An ECE student who is involved in a research project and is interested in registering this undergraduate research for course credit on the official transcript may request to be enrolled in this course. To do this, the student should first complete the on-line undergraduate research form available on the ECE undergraduate student page. Once the form has been submitted and approved by the faculty member the student is conducting the research with, the ECE Undergraduate Office will add the course to the student's schedule. Typical credit is granted as one hour of research per week is equal to one unit of credit.
18-340 Digital Computation
Fall: 12 units
In this course we will explore the techniques for designing high-performance digital circuits for computation along with methods for evaluating their properties. We begin by quickly reviewing number systems and digital arithmetic along with basic arithmetic circuits such as ripple-carry adders. We then focus on formal techniques and theory for analyzing the functionality, timing, power consumption, and chip area properties of these basic circuits and ones yet to be presented. From there, we move to more complex adders (carry-lookahead, carry-skip, carry_bypass, Wallace trees, and hybrid techniques) and multipliers (sequential, array, Booth, and others) along with various divider circuits. Floating point units are then built upon the concepts introduced for adder, multipliers, and dividers. Finally, we will investigate the design and implementation of digital filter circuits. For each circuit introduced, we will develop techniques for evaluating their functionality, their speed, power consumption, and silicon area requirements. In addition, we will utilize various CAD tools to design and evaluate most of the computation circuits discussed. After successful completion of the course, students will not only have an understanding of complex computation circuits, but subtle concepts that include hazards, metastability, false paths, inertial delay, sticky bits, clock skew/jitter, dynamic and static sensitization, and many others. 3 hrs. lec., 1 hr. rec.
Prerequisite: 18-240
18-341 Logic Design and Verification
Fall: 12 units
This course is a second level logic design course, studying the techniques of designing at the register-transfer and logic levels of complex digital systems using modern modeling, simulation, synthesis, and verification tools. Topics include register-transfer level systems (i.e., finite state machines and data paths), bus and communication system interfacing (such as a simplified USB interface), discrete-event simulation, testbench organization, assertion-based verification and functional coverage. Design examples will be drawn from bus and communication interfaces, and computation systems, emphasizing how these systems are designed and how their functionality can be verified. A modern hardware description language, such as SystemVerilog, will serve as the basis for uniting these topics. Quizzes, homeworks and design projects will serve to exercise these topics.
Prerequisite: 18-240
18-342 Fundamentals of Embedded Systems
Fall: 12 units
This practical, hands-on course introduces students to the basic building-blocks and the underlying scientific principles of embedded systems. The course covers both the hardware and software aspects of embedded procesor architectures, along with operating system fundamentals, such as virtual memory, concurrency, task scheduling and synchronization. Through a series of laboratory projects involving state-of-the-art processors, students will learn to understand implementation details and to write assembly-language and C programs that implement core embedded OS functionality, and that control/debug features such as timers, interrupts, serial communications, flash memory, device drivers and other components used in typical embedded applications. Relevant topics, such as optimization, profiling, digital signal processing, feedback control, real-time operating systems and embedded middleware, will also be discussed. This course is intended for INI students. Anti-requisites: 18348 or 18349
Prerequisite: 18-240
18-345 Introduction to Telecommunication Networks
Spring: 12 units
This course introduces the fundamental concepts of telecommunication networks. Underlying engineering principles of telephone networks, computer networks and integrated digital networks are discussed. Topics in the course include: telephone and data networks overview; OSI layers; data link protocol; flow control, congestion control, routing; local area networks; transport layer; introduction to high-speed networks; performance evaluation techniques. The course also reviews important aspects of network security and widely used classes of Internet application and services, such as peer-to-peer, content delivery networks, and video streaming.
Prerequisites: (36-212 or 36-226 or 36-217) and 18-213
18-349 Introduction to Embedded Systems
Fall and Spring: 12 units
This practical, hands-on course introduces the various building blocks and underlying scientific and engineering principles behind embedded real-time systems. The course covers the integrated hardware and software aspects of embedded processor architectures, along with advanced topics such as real-time, resource/device and memory management. Students can expect to learn how to program with the embedded architecture that is ubiquitous in cell-phones, portable gaming devices, robots, PDAs, etc. Students will then go on to learn and apply real-time principles that are used to drive critical embedded systems like automobiles, avionics, medical equipment, the Mars rover, etc. Topics covered include embedded architectures (building up to modern 16/32/64-bit embedded processors); interaction with devices (buses, memory architectures, memory management, device drivers); concurrency (software and hardware interrupts, timers); real-time principles (multi-tasking, scheduling, synchronization); implementation trade-offs, profiling and code optimization (for performance and memory); embedded software (exception handling, loading, mode-switching, programming embedded systems). Through a series of laboratory exercises with state-of-the-art embedded processors and industry-strength development tools, students will acquire skills in the design/implementation/debugging of core embedded real-time functionality. Anti-requisites: 18342 or 18348
Prerequisites: 18-213 and 18-240
Course Website: http://www.ece.cmu.edu/~ee349
18-370 Fundamentals of Control
Fall: 12 units
An introduction to the fundamental principles and methodologies of classical feedback control and its applications. Emphasis is on problem formulation and the analysis and synthesis of servomechanisms using frequency and time domain techniques. Topics include analytical, graphical, and computer-aided (MATLAB) techniques for analyzing and designing automatic control systems; analysis of performance, stability criteria, realizability, and speed of response; compensation methods in the frequency domain, root-locus and frequency response design, and pole-zero synthesis techniques; robust controller design; systems with delay and computer control systems; transfer function and state space modeling of linear dynamic physical systems; nonlinearities in control systems; and control engineering software (MATLAB). 4 hrs. lec., 1 hr. rec.
Prerequisites: 18-396 or 18-290
18-372 Fundamental Electrical Power Systems
Fall: 12 units
This course introduces the fundamentals in electric energy systems which will enable you to understand current issues and challenges in electric power systems ("smart grid") and what it takes for you to have a reliable electric power supply at your house. First, the general structure of an electric power system (current and future trends) will be introduced. This includes electric power plants (renewable and non-renewable); transmission and distribution; and consumers. Then, electric power is addressed from a mathematical point of view. The mathematical formulae for AC power and models for the above mentioned elements are derived which will enable you to calculate how much power is flowing over which lines on its way from the power plant to the consumer. Maintaining the balance between generation and consumption is important to avoid catastrophic blackout events. Hence, the notion of stability and available control concepts will be introduced.
Prerequisites: 18-202 and 18-220
18-390 ECE CO-OP
Fall and Spring
The Department of Electrical and Computer Engineering at Carnegie Mellon considers experiential learning opportunities important educational options for its undergraduate students. One such option is cooperative education, which provides a student with an extended period of exposure with a company. To participate, students must complete an ECE Co-op Approval form (located in HH 1115) and submit for approval. Students must possess at least junior status and have an overall grade point average of 3.0 or above. All co-ops must be approximately 8 months in uninterrupted length. If the co-op is approved, the ECE Undergraduate Studies Office will add the course to the student's schedule. Upon completion of the co-op experience, students must submit a 1-2 page report of their work experience, and a 1-2 page evaluation from the company supervisor to the ECE Undergraduate Office. International students should also be authorized by the Office of International Education (OIE). More information regarding CPT is available on OIE's website.
18-401 Electromechanics
12 units
This course provides a broadly based introduction to interactions between mechanical media and electromagnetic fields. Attention is focused on the electromechanical dynamics of lumped-parameter systems, wherein electrical and mechanical subsystems may be modeled in terms of discrete elements. Interactions of quasistatic electric and magnetic fields with moving media are described and exemplified. Unifying examples are drawn from a wide range of technological applications, including energy conversion in synchronous, induction, and commutator rotating machines, electromechanical relays, a capacitor microphone and speaker, and a feedback-controlled magnetic levitation system. 4.5 hrs. rec.
Prerequisite: 18-300
18-402 Applied Electrodynamics
Spring: 12 units
This course builds upon the electric and magnetic field foundations established in 18-300 to describe phenomena and devices where electromagnetic waves are a central issue. Topics include: review of Maxwell's equations, propagation of uniform plane waves in lossless and lossy media, energy conservation as described by the Poynting Theorem, reflection and transmission with normal and oblique incidence upon boundaries, sinusoidal steady state and transients on 2-conductor transmission lines, modal descriptions of waveguides, radiation and antennas. 4 hrs. lec.
Prerequisite: 18-300
18-403 Microfabrication Methods and Technology
Fall: 12 units
This course is a laboratory-based introduction to the theory and practice of microfabrication. Lectures and laboratory sessions cover fundamental processing techniques such as photo-mask creation, lithographic patterning, thin film vacuum deposition processes, wet-chemical and dry-etching processes. This is primarily a hands-on laboratory course which brings students into the microfabrication facility and device testing laboratories. Students will fabricate electronic and opto-electronic devices such the metal-oxide-semiconductor (MOS) capacitor, the Schottky diode, the MOS transistor, the solar cell, and the light-emitting diode. An understanding of the operation of these building block devices will be gained by performing measurements of their electrical and opto-electronic characteristics. Emphasis is placed on understanding the interrelationships between the materials properties, processing, device structure, and the electrical and optical behavior of the devices. The course is intended to provide a background for a deeper appreciation of solid state electronic devices and integrated circuits. 2 lecture periods per week and a minimum of 4 laboratory hours.
Prerequisite: 18-310
18-411 Computational Techniques in Engineering
Spring: 12 units
This course develops the methods to formulate basic engineering problems in a way that makes them amenable to computational/numerical analysis. The course will consist of three main modules: basic programming skills, discretization of ordinary and partial differential equations, and numerical methods. These modules are followed by two modules taken from a larger list: Monte Carlo-based methods, molecular dynamics methods, image analysis methods, and so on. Students will learn how to work with numerical libraries and how to compile and execute scientific code written in Fortran-90 and C++. Students will be required to work on a course project in which aspects from at least two course modules must be integrated.
Prerequisites: 21-260 and 21-259 and 21-122 and 21-120 and 15-100
18-415 From Design to the Market for Deep Submicron IC's
Spring: 12 units
The general objective of the 18-415 class is to introduce and analyze all major design-dependent trade-offs which decide about the IC product commercial success. This objective will be achieved via playing in the class an "imaginary fabless IC design house startup game"- a main class activity. In this game students will be asked to construct "business plans" for a startup fabless IC design house. Each team in the class will have to envision, as an IC design objective, a new product with a functionality, which is already provided by another existing IC product (i.e. by microprocessor). The envisioned product should provide a subset of functionality of the existing product but it should be "better" in some other respect (e.g. it could be less expensive to fabricate, faster etc.). To handle the above assignment, students in the class will be using skills learned in 18-322 as well as all legal sources of "industrial intelligence" typically available for the IC industry. They can also use the class teacher as a source of free consulting, as well as, they can ask for any sequence of lectures or literature sources which they will need to meet the class objectives.
Prerequisite: 18-320
18-418 Electric Energy Processing: Fundamentals and Applications
Spring: 12 units
This course provides an introduction to the fundamentals of electrical energy conversion and its use in several real-life systems. The course starts with a brief review of general mathematical and physical principles necessary for subsequent study of electrical energy conversion applications. This includes modeling, analysis, and control of general physical systems in time and frequency domain. Since the focus of energy conversion methods studied in this course is from electrical to mechanical systems, special attention is paid to electromagnetic theory. Rotating machines theory is developed and intuitively explained starting with Maxwell equations and analyzing general static and dynamic electromagnetic circuits. Power electronics methods are also introduced because most of modern electrical systems employ such methods. At this point, the necessary background is gained to analyze real life electrical energy conversion systems. We will focus on automotive, airplane, space station, and sea power systems. The main focus will be on operational principles and when appropriate stability issues of particular implementations. Time allowing, dynamic problems with interconnecting such systems will be briefly introduced and possibly studied by curious students in their course projects.
Prerequisite: 18-220
18-421 Analog Integrated Circuits
Spring: 12 units
Some form of analog circuit design is a critical step in the creation of every modern IC. First and foremost, analog circuits act as the interface between digital systems and the real world. They act to amplify and filter analog signals, and to convert signals from analog to digital and back again. In addition, high performance digital cell design (either high speed or low power) also invokes significant analog circuit design issues. The goal of this course is to teach students some of the methods used in the design and analysis of analog integrated circuits, to illustrate how one approaches design problems in general, and to expose students to a broad cross-section of important analog circuit topologies. The course will focus on learning design through carrying out design projects. Design and implementation details of wide-band amplifiers, operational amplifiers, filters and basic data converters will be covered. Example topics to be covered include transistor large- and small-signal device models, small-signal characteristics of transistor-based amplifiers, large-signal amplifier characteristics and nonidealities, operational amplifier design, basic feedback amplifier stability analysis and compensation, and comparator design. The course will focus primarily on analog CMOS, but some aspects of BJT design will be discussed. 18-290 and 18-320 or equivalent background material with permission of the instructor. Although students in 18-623 will share Lectures and Recitations with students in 18-421, students in 18-623 will receive distinct homework assignments, distinct design problems, and distinct exams from the ones given to students in 18-421 and will be graded on a separate curve from students taking 18-421.
Prerequisites: 18-290 and 18-320
18-422 Digital Integrated Circuit Design
Fall: 12 units
This course covers the design and implementation of digital circuits in a modern VLSI process technology. Topics will include logic gate design, functional unit design, latch/flip-flop design, system clocking, memory design, clock distribution, power supply distribution, design for test, and design for manufacturing. The lab component of the course will focus on using modern computer aided design (CAD) software to design, simulate, and lay out digital circuits. The final project for the course involves the design and implementation to the layout level of a small microprocessor. 18-240 and 18-320 or equivalent background material with permission of the instructor. Although students in 18-422 and 18-622 will share lectures, labs, and recitations, students in 18-422 and 18-622 will receive different homework assignments, design projects, and exams, and in some cases 18-622 students will also have different or additional lab sessions.
Prerequisites: 18-240 and 18-320
18-431 Undergraduate Projects - Senior
Fall
The Department of Electrical and Computer Engineering at Carnegie Mellon considers experiential learning opportunities important educational options for its undergraduate students. One such option is conducting undergraduate research with a faculty member. Students do not need to officially register for undergraduate research unless they want it listed on their official transcripts. An ECE student who is involved in a research project and is interested in registering this undergraduate research for course credit on the official transcript may request to be enrolled in this course. To do this, the student should first complete the on-line undergraduate research form available on the ECE undergraduate student page. Once the form has been submitted and approved by the faculty member the student is conducting the research with, the ECE Undergraduate Office will add the course to the student's schedule. Typical credit is granted as one hour of research per week is equal to one unit of credit.
18-432 Senior Projects
Spring
The Department of Electrical and Computer Engineering at Carnegie Mellon considers experiential learning opportunities important educational options for its undergraduate students. One such option is conducting undergraduate research with a faculty member. Students do not need to officially register for undergraduate research unless they want it listed on their official transcripts. An ECE student who is involved in a research project and is interested in registering this undergraduate research for course credit on the official transcript may request to be enrolled in this course. To do this, the student should first complete the on-line undergraduate research form available on the ECE undergraduate student page. Once the form has been submitted and approved by the faculty member the student is conducting the research with, the ECE Undergraduate Office will add the course to the student's schedule. Typical credit is granted as one hour of research per week is equal to one unit of credit.
18-447 Introduction to Computer Architecture
Spring: 12 units
Computer architecture is the science and art of selecting and interconnecting hardware components to create a computer that meets functional, performance and cost goals. This course introduces the basic hardware structure of a modern programmable computer, including the basic laws underlying performance evaluation. We will learn, for example, how to design the control and data path hardware for a MIPS-like processor, how to make machine instructions execute simultaneously through pipelining and simple superscalar execution, and how to design fast memory and storage systems. The principles presented in the lecture are reinforced in the laboratory through the design and simulation of a register transfer (RT) implementation of a MIPS-like pipelined superscalar in Verilog. Learning to design programmable systems requires that you already have the knowledge of building RT systems, the knowledge of the behavior storage hierarchies (e.g., cache memories) and virtual memory, and the knowledge of assembly language programming.
Prerequisites: 18-240 and (15-213 or 18-213 or 18-243) and (18-349 or 18-348 or 18-341 or 18-340 or 18-320)
18-451 Networked Cyberphysical Systems
Spring: 12 units
Cyber-physical systems (CPS) represent a new class of systems that bring together sensing, computation, communication, control and actuation to enable continuous interactions with physical processes. This integration of networked devices, people, and physical systems provides huge opportunities and countless applications in biology and healthcare, automotive and transportation, power grids and smart buildings, social and financial markets, etc. Hence, CPS need to provide real-time efficiency, adaptability, optimality, security and robustness to natural disasters or targeted attacks. While the focus on embedded systems relies on building computational models for specific applications, CPS need a multidisciplinary approach and a more general computational paradigm such that more-direct interactions between the system and physical world become possible. This course is primarily an in-depth introduction to networked CPS with an emphasis on methods for modeling, design, and optimization. Focus is on the dominant design paradigms like low-power and communication-centric design. Topics to be covered include: physical processes, models of concurrency, sensing and workload modeling, human behavior modeling, data-driven modeling, networking at micro- and macro-scale, system-wide resources management, programming, validation and integration. From a practical standpoint, students will directly experiment with hardware prototypes and software tools to explore concrete CPS examples. By structure and contents, this class is primarily targeted to ECE students; it can also provide a valuable basis for interdisciplinary research to students in CS and related disciplines.
Prerequisites: 18-349 or (18-240 and 18-213)
18-452 Wireless Networks and Applications
Spring: 12 units
This course introduces fundamental concepts of wireless networks. The design of wireless networks is influenced heavily by how signals travel through space, so the course starts with an introduction to the wireless physical layer, presented in a way that is accessible to a broad range of students. The focus of the course is on wireless MAC concepts including CSMA, TDMA/FDMA, and CDMA. It also covers a broad range of wireless networking standards, and reviews important wireless network application areas (e.g., sensor networks, vehicular) and other applications of wireless technologies (e.g., GPS, RFID, sensing, etc.). Finally, we will touch on public policy issues, e.g., as related to spectrum use. The course will specifically cover: Wireless networking challenges Wireless communication overview Wireless MAC concepts Overview of cellular standards and LTE Overview of wireless MAC protocols WiFi, bluetooth and personal area networks, etc. Wireless in today's Internet: TCP over wireless, mobility, security, etc. Advanced topics, e.g., mesh and vehicular networks, sensor networks, DTNs, localization, sensing, etc. Although students in 18-750 will share Lectures and Recitations with students in 18-452, they will receive distinct homework assignments and exams from students in 18-452. The main project will also be different. The students in the two version of the course will also be graded on a separate curve.
Prerequisites: 15-213 or 18-600 or 18-213
18-474 Embedded Control Systems
Spring: 12 units
This course introduces principles for design of embedded controllers. In applications ranging from airplanes, to automobiles, to manufacturing systems, embedded computers now close feedback loops that were previously closed by mechanical devices or by humans in the loop. This course emphasizes practical insight into the tools for modeling and simulating these dynamic physical systems, and methods for designing the real-time software for embedded computers to control them. Lectures cover relevant theory and background from real-time systems and control engineering, including event-based and clock-based sampling, switching control, PWM (pulse-width modulation), PID (proportional-integral-derivative) design, state-variable feedback, state estimation, and methods for setpoint control and trajectory tracking. Basic embedded computing, sensor, and actuator technologies are reviewed, including microcontrollers, DC motors and optical encoders. In the laboratory, students use commercial tools for simulation and automatic code generation to design and implement embedded control system experiments. 3 hrs. lecture, 3 hrs. lab.
Prerequisites: (15-213 or 18-243) and (18-370 or 18-396)
18-482 Telecommunications, Technology Policy & Management
Spring: 12 units
This course provides a comprehensive introduction to basic principles of telecommunications technology and the telephone network, and the legal, economic, and regulatory environment of the telecommunications industry. Role of new technologies such as fiber, integrated digital networks, computer communications, and information services. Common carrier law and the economics of natural monopoly as the basis for regulation of the telecommunications industry. Issues of competition, monopoly and technical standards. Spectrum allocation and management. International communications and transborder data flow. Special emphasis on how the new technologies have altered and are altered by regulation. Junior or Senior standing required.
Prerequisite: 73-100
18-487 Introduction to Computer Security
Fall: 12 units
Security is becoming one of the core requirements in the design of critical systems. This course will introduce students to the intro-level fundamental knowledge of computer security and applied cryptography. Students will learn the basic concepts in computer security including software vulnerability analysis and defense, networking and wireless security, and applied cryptography. Students will also learn the fundamental methodology for how to design and analyze security critical systems. Anti-requisites: 18-631 and 18-730
Prerequisite: 18-213
18-491 Fundamentals of Signal Processing
Fall: 12 units
This course addresses the mathematics, implementation, design and application of the digital signal processing algorithms widely used in areas such as multimedia telecommunications and speech and image processing. Topics include discrete-time signals and systems, discrete-time Fourier transforms and Z-transforms, discrete Fourier transforms and fast Fourier transforms, digital filter design and implementation, and multi-rate signal processing. The course will include introductory discussions of 2-dimensional signal processing, linear prediction, adaptive filtering, and selected application areas. Classroom lectures are supplemented with implementation exercises using MATLAB.
Prerequisite: 18-290
18-493 Electroacoustics
Fall: 12 units
This course provides an introduction to physical, engineering, and architectural acoustics. The course begins with a review of the wave equation and some of its solutions that are relevant to the propagation of sound from planar and spherical sources, and from arrays of simple sources. Lumped-parameter electrical circuit analogies are developed to describe mechanical and acoustical systems, leading to a discussion of the constraints and tradeoffs involved in the design of loudspeakers, microphones, and other transducers. The characteristics of sound in regular and irregular enclosures will be developed and discussed in the context of the acoustical design for rooms and auditoriums. The interaction of sound and man is also discussed, with introductory lectures on auditory perception and the acoustics of speech production, with applications in the areas of efficient perceptually-based coding of music and speech, and virtual acoustical environments.
Prerequisites: 18-290 and 18-220
18-496 Introduction to Biomedical Imaging and Image Analysis
Fall: 12 units
Bioimage Informatics (formerly Bioimaging) This course gives an overview of tools and tasks in various biological and biomedical imaging modalities, such as fluorescence microscopy, electron microscopy, magnetic resonance imaging, ultrasound and others. The major focus will be on automating and solving the fundamental tasks required for interpreting these images, including (but not restricted to) deconvolution, registration, segmentation, pattern recognition, and modeling, as well as tools needed to solve those tasks (such as Fourier and wavelet methods). The discussion of these topics will draw on approaches from many fields, including statistics, signal processing, and machine learning. As part of the course, students will be expected to complete an independent project.
Prerequisite: 18-290
18-499 Internship
All Semesters
The Department of Electrical and Computer Engineering at Carnegie Mellon considers experiential learning opportunities important educational options for its undergraduate students. One such option is an internship, normally completed during the summer. Students do not need to officially register for an internship unless they want it listed on their official transcripts. ECE students interested in registering their internship for course credit on their transcript may request to be enrolled in this course. The ECE Undergraduate Office will add the course to the student's schedule, and the student will be assessed tuition for 3 units. This process should be used by international students interested in Curricular Practical Training (CPT) or by any other engineering undergraduate wishing to have their internship experience reflected on their official University transcript. International students should also be authorized by the Office of International Education (OIE). More information regarding CPT is available on OIE's website.
18-500 ECE Design Experience
Fall and Spring: 12 units
The ECE Design Experience is a capstone design course that serves to introduce students to broad- based, practical engineering design and applications through an open-ended design problem. Students will work with a team on a project of their choosing (subject to instructor approval) throughout the semester culminating with a final project presentation, report, and public demonstration. The projects will need to encompass a minimum of two ECE areas. Throughout the semester, teams will need to give both written and oral project proposals and periodic performance updates. Team-building experiences designed to educate students on group dynamics, resource management, deadline planning, Big-picture implications of engineering applications: societal, human, ethical, and long-term impact will be explored.
Prerequisites: 18-213 and 18-290 and 18-220 and 18-240
18-540 Rapid Prototyping of Computer Systems
Spring: 12 units
This is a project-oriented course which will deal with all four aspects of project development; the application, the artifact, the computer-aided design environment, and the physical prototyping facilities. The class, in conjunction with the instructors, will develop specifications for a mobile computer to assist in inspection and maintenance. The application will be partitioned between human computer interaction, electronics, industrial design, mechanical, and software components. The class will be divided into groups to specify, design, and implement the various subsystems. The goal is to produce a working hardware/software prototype of the system and to evaluate the user acceptability of the system. We will also monitor our progress in the design process by capturing our design escapes (errors) with the Orthogonal Defect Classification (ODC). Upon completion of this course the student will be able to: generate systems specifications from a perceived need; partition functionality between hardware and software; produce interface specifications for a system composed of numerous subsystems; use computer-aided design tools; fabricate, integrate, and debug a hardware/software system; and evaluate the system in the context of an end user application. Senior standing is required.
Prerequisites: (18-491 or 18-320 or 18-370) and (18-348 or 18-349 or 18-340 or 18-341)
18-578 Mechatronic Design
12 units
Mechatronics is the synergistic integration of mechanism, electronics, and computer control to achieve a functional system. Because of the emphasis upon integration, this course will center around system integration in which small teams of students will configure, design, and implement a succession of mechatronic subsystems, leading to a main project. Lectures will complement the laboratory experience with comparative surveys, operational principles, and integrated design issues associated with the spectrum of mechanism, electronics, and control components. Class lectures will cover topics intended to complement the laboratory work, including mechanisms, actuators, motor drives, sensors and electronic interfaces, microcontroller hardware and programming and basic controls. During the first week of class, each student will be asked to complete a questionnaire about their technical background. The class will then be divided into multi-disciplinary teams of three students. During the first half of the class, lab assignments will be made every 1-2 weeks to construct useful subsystems based on material learned in lecture. The lab assignments are geared to build to the main project. This course is cross-listed as 16-778 and 24-778. Students in other departments may take the course upon availability of slots with permission of instructor. Non ECE students may take the course upon availability of slots with permission of the instructor.
Prerequisites: (18-320 and 18-348) or (15-313 and 18-348) or (18-348 and 18-370) or (18-370 and 18-349) or (15-313 and 18-349) or (18-320 and 18-349) or (18-320 and 18-370)
18-610 Fundamentals of Modern CMOS Devices
Spring: 12 units
This course is intended to provide a foundation in device operation for circuit designers working in today's sub-micron CMOS. This course will also provide advanced understanding of CMOS technology for those interested in integrated circuit process technology and device physics. We review semiconductor device physics, including carrier dynamics and the basic equations of semiconductor device physics. The operation of the p-n junction diode is also reviewed. The course includes a description of integrated circuit fabrication technology and how it is used to fabricate CMOS devices. With this foundation, we then discuss the MOS capacitor (including its application as a varactor). The theory of the MOS transistor will then be developed, followed by a discussion of important phenomena in sub-micron devices such as: velocity saturation; breakdown; drain-induced barrier lowering; random dopant fluctuations, etc. The student will learn the relationship between device geometry, e.g. length, and fabrication, e.g. doping, and the corresponding circuit performance. The course will primarily be lecture-based, with some selected simulation exercises. Students are expected to be acquainted with the basic concepts of electrical circuits; electromagnetic fields at the level of a sophomore level physics course, and to have adequate preparation in mathematics (basic differential equations and MATLAB or similar applications). Prior coursework in device physics is helpful but not required for graduate students. Lecture: 4 hrs Prerequisite(s): 18-310
Prerequisite: 18-310
18-614 Microelectromechanical Systems
Fall: 12 units
This course introduces fabrication and design fundamentals for Microelectromechanical Systems (MEMS): on-chip sensor and actuator systems having micron-scale dimensions. Basic principles covered include microstructure fabrication, mechanics of silicon and thin-film materials, electrostatic force, capacitive motion detection, fluidic damping, piezoelectricity, piezoresistivity, and thermal micromechanics. Applications covered include pressure sensors, micromirror displays, accelerometers, and gas microsensors. Grades are based on exams and homework assignments. 4 hrs. lec.
Prerequisites: 18-300 or 18-310 or 18-320 or 24-351
18-615 Micro and Nano Systems Fabrication
Spring: 12 units
This is a new course intended to introduce students to the process flow and design methodology for integrated systems fabrication. The course will present this material through two paths. Lectures will be presented on the basic unit processes of micro and nanosystems fabrication: deposition, patterning, and etching. Lectures will draw on examples from: Semiconductor device fabrication; Microelectromechanical systems (MEMS) fabrication; Magnetic device fabrication; and Optical device fabrication. Problem sets will be given based on this lecture material to allow students to quantitatively analyze certain process steps in detail. The second path for material presentation will be through a series of labs that allow students to design, fabricate and test an integrated device. These laboratories will be scheduled at regular meeting times, and will use research facilities within the ECE department. This is a PhD level course. MS or senior students must obtain permission from the instructor to be registered.
Prerequisite: 18-310
18-617 Memory Devices and Systems
12 units
Missing Course Description - please contact the teaching department.
Prerequisite: 18-320
18-623 Analog Integrated Circuit Design
Fall: 12 units
Some form of analog circuit design is a critical step in the creation of every modern IC. First and foremost, analog circuits act as the interface between digital systems and the real world. They act to amplify and filter analog signals, and to convert signals from analog to digital and back again. In addition, high performance digital cell design (either high speed or low power) also invokes significant analog circuit design issues. The goal of this course is to teach students some of the methods used in the design and analysis of analog integrated circuits, to illustrate how one approaches design problems in general, and to expose students to a broad cross-section of important analog circuit topologies. The course will focus on learning design through carrying out design projects. Design and implementation details of wide-band amplifiers, operational amplifiers, filters and basic data converters will be covered. Example topics to be covered include transistor large- and small-signal device models, small-signal characteristics of transistor-based amplifiers, large-signal amplifier characteristics and nonidealities, operational amplifier design, basic feedback amplifier stability analysis and compensation, and comparator design. The course will focus primarily on analog CMOS, but some aspects of BJT design will be discussed. 18-290 and 18-320 or equivalent background material with permission of the instructor. Although students in 18-623 will share Lectures and Recitations with students in 18-421, students in 18-623 will receive distinct homework assignments, distinct design problems, and distinct exams from the ones given to students in 18-421 and will be graded on a separate curve from students taking 18-421.
Prerequisites: 18-320 and 18-290
18-632 Introduction to Hardware Security
Fall: 12 units
This course covers basic concepts in the security of hardware systems. Topics covered include active and passive attacks, reverse engineering, counterfeiting, and design of hardware security primitives (e.g., random number generators, physical unclonable functions, crypto-processors). Lab sessions will give students hands on experience with performing attacks, developing countermeasures, and implementing secure hardware building blocks. Students are expected to have basic knowledge of digital logic and Register-Transfer Level (RTL) design, but no specific background in security/cryptography is necessary.
Prerequisites: 18-240 and (18-341 or 18-447)
18-643 Reconfigurable Logic: Technology, Architecture and Applications
Fall: 12 units
Three decades since its original inception as a lower-cost compromise to ASIC, modern Field Programmable Gate Arrays (FPGAs) are versatile and powerful systems-on-a-chip for many applications that need both hardware level efficiency and the flexibility of reprogrammability. More recently, FPGAs have also emerged as a formidable computing substrate with applications ranging from data centers and mobile devices. This course offers a comprehensive coverage of modern FPGAs in terms of technology, architecture and applications. The coverage will also extend into on-going research investigations of future directions. Students will take part in a substantial design projects applying the latest FPGA platforms to compute acceleration. Register-Transfer Level (RTL) hardware design experience is required.
Prerequisites: 18-447 or 18-341
18-649 Distributed Embedded Systems
Spring: 12 units
Embedded computers seem to be everywhere, and are increasingly used in applications as diverse as transportation, medical equipment, industrial controls, and consumer products. This course covers how to design and analyze distributed embedded systems, which typically consist of multiple processors on a local area network performing real time control tasks. The topics covered will include issues such as communication protocols, synchronization, real-time operation, fault tolerance, distributed I/O, design validation, and industrial implementation concerns. The emphasis will be on areas that are specific to embedded distributed systems as opposed to general-purpose networked workstation applications. This course assumes that students already know fundamental topics such as interrupts, basic I/O, and uniprocessor scheduling that are commonly taught in introduction-level embedded system courses such as 18-348 and 18-349. Any graduate student who has not taken one of the pre-requ isites is responsible for understanding relevant material necessary for this course. Additionally, all students are responsible for knowing or learning on their own intermediate-level programming in Java. Prerequisites: 18348 or 18349 and senior or graduate standing.
Prerequisites: 18-349 or 18-348
18-651 Networked Cyber-Physical Systems
Spring: 12 units
Cyber-physical systems (CPS) represent a new class of systems that bring together sensing, computation, communication, control and actuation to enable continuous interactions with physical processes. This integration of networked devices, people, and physical systems provides huge opportunities and countless applications in biology and healthcare, automotive and transportation, power grids and smart buildings, social and financial markets, etc. Hence, CPS need to provide real-time efficiency, adaptability, optimality, security and robustness to natural disasters or targeted attacks. While the focus on embedded systems relies on building computational models for specific applications, CPS need a multidisciplinary approach and a more general computational paradigm such that more-direct interactions between the system and physical world become possible. This course is primarily an in-depth introduction to networked CPS with an emphasis on methods for modeling, design, and optimization. Focus is on the dominant design paradigms like low-power and communication-centric design. Topics to be covered include: physical processes, models of concurrency, sensing and workload modeling, human behavior modeling, data-driven modeling, networking at micro- and macro-scale, system-wide resources management, programming, validation and integration. From a practical standpoint, students will directly experiment with hardware prototypes and software tools to explore concrete CPS examples. By structure and contents, this class is primarily targeted to ECE students; it can also provide a valuable basis for interdisciplinary research to students in CS and related disciplines.
Prerequisites: 18-349 or (18-213 and 18-240)
18-659 Software Engineering Methods
Spring: 12 units
There has been a rapid evolution of software engineering development methods over the past decades. From Waterfall to Iterative and Incremental, to Agile and Lean, we have witnessed waves of new methods, each adding significant value to the field. However, the plethora of available methods poses a challenge for software practitioners: Which method should be adopted on a specific software project? Software Engineering Methods addresses this challenge by introducing students to emerging approaches for developing software-intensive systems. Given the vast spectrum of software development endeavors, these approaches aim at defining custom hybrid methods by focusing on software development principles and practices together with their applicability to specific project contexts. Students learn to analyze the context of a software project and recommend a custom hybrid development method that satisfies the projects specific needs. Students apply this knowledge in the context of a semester-long project. Their goal is to recommend the optimal software development method for a given project aimed at developing a specific software system. They model their recommended method and define a multi-level software engineering plan to enact the method. They build the first system increment by adopting their own method and following their own plan. They monitor their progress and reflect on the effectiveness of their approach and the need for continuous process improvement. Please note that this course is intended for ECE master students with a concentration in Software Engineering and will satisfy the "Systems" course area requirement. Pre-requisites: 18-652
Prerequisite: 18-652
18-664 ULSI Technology Status and Roadmap for System on Chips and System in Package
Fall: 12 units
This course provides the necessary background for the state-of-the art technologies utilized by the leading edge products covering full spectrum of market drivers from mobile platforms, microprocessors, game chips to the highest performance systems for enterprise solutions computing. We will present all key components of such systems, i.e., logic, analog/RF and embedded memories. Then we present the technology roadmap for the upcoming generations in terms of device architecture options for logic devices (FinFET, Nanowire and Tunnel FET) and memories (Phase Change Memory , Resistive RAM and Magnetic RAM/Spin-Transfer Torque RAM) from the device level all the way to the system level specifications. The last part of the class will be devoted to the system integration issues, namely 3-dimensional integration approaches. This course is designed for MS and PhD students from diverse areas: System/Hardware Design, Circuits and Devices/Nanofabrication and is aimed at bridging the gap among these areas. Graduate Standing (or permission from the instructor) is required for this course.
Prerequisite: 18-422
18-712 Elements of Photonics for Communication Systems
Fall: 12 units
Please see the ECE website for a full course description of this course. http://www.ece.cmu.edu/courses/items/18712.html Prerequisites: 18-300 and 18-310 and (18-402 or 33-439) and senior or graduate standing.
Prerequisites: 18-310 and 18-300 and (18-402 or 33-439)
18-715 Physics of Applied Magnetism
Spring: 12 units
In this course we address the physics of magnetism of solids with emphasis on magnetic material properties and phenomena which are useful in various applications. Various applications of magnetism are used to motivate the understanding of the physical properties and phenomena. The content of this course includes the origins of magnetism at the atomic level and the origins of magnetic ordering (ferro-, ferri-, and antiferro-magnetism), magnetic anisotropy, magnetic domains, domain walls, spin dynamics and electronic transport at the crystalline level. The principles of magnetic crystal symmetry, tensors, and energy minimization are utilized to explore magnetic properties such as resonance, domain structures, magnetocrystalline anisotropy, magnetostriction and magnetoelasticity, and susceptibility. Phenomenological properties, such as the technical magnetization process, are used to describe mechanisms of coercivity, eddy current effects and losses, while energy minimization and relaxation are used to explain properties such as single domain particle behavior, memory mechanisms, magnetic aftereffects and thermal stability. Prerequisite: 18-300 or equivalent background in electromagnetic fields; Senior level solid state physics and materials, or the equivalent, and a senior or graduate student standing.
Prerequisite: 18-300
18-716 Advanced Applied Magnetism
Spring: 12 units
Over the past decade, magnetism has once again become one of the dominant themes in material science and solid-state physics. Today, the development of new thin film recording media and the discovery of giant magnetoresistance have resulted in the amount of stored bits in a single disk drive to reach astronomical numbers. Rapid advances in spin-polarized electrical transport have brought to the horizon a new kind of electronics, called spintronics, with a new functionality based upon the spin of the carriers. The newly enriched magnetism brings unbounded technologic opportunities, yet full of challenges. This course will cover many of the important technological applications of advanced magnetism. The emphasis will be placed on how the basic principles and concepts are applied. The topics include: (1) Application and theory of spin dependent transport: CIP and CPP GMR devices, spin injection in semiconductors, spin LED, spin transistors, and spin current induced magnetic switching; (2) Engineering of the magnetic material properties for: thin film recording media, recording heads, magnetoresistive random access memory; (3) Thermally excited ferromagnetic resonance: mag-noise in magnetic devices, and thermally activated magnetization reversal; (4) Continuous and patterned magnetic films: magnetic bubble technology and patterned media; (5) Magnetostriction: magnetostrictive sensors; (6) Magnetic imaging techniques: magnetic resonance imaging (MRI), magnetic force microscopy (MFM), differential-phase-contrast microscopy (DPC), SEMPA, and Kerr microscopy. 4 hrs. lec. Prerequisite: 18-715 or equivalent upon instructor's approval and senior or graduate standing.
Prerequisite: 18-715
18-755 Networks in the Real World
Spring: 12 units
18-755 is a graduate-level course that focuses on networks and their applications to various natural and technological systems. Specifically, this class delves into the new science behind networks and their concrete applications technological, biological, and social systems, as well as various design synergies that exist when looking at these systems from a cyber-physical perspective. By scope and contents, this is not just another class on ?networks?. Want to know how complex networks dominate our world? How communities arise in social networks? How group behavior dominates Twitter? How swarms of bacteria can navigate inside the human body? How patterns of interaction can be identified in hardware and software systems? Want to work on cutting edge projects involving systems and synthetic biology? Or social networks? Or networks-on-chip and internet-of-things? Then this class is for you! Course requirements consist of a few homework assignments, a semester-long project, and in-class presentations of relevant papers. By structure and contents, this class targets primarily the computer engineering and computer science students, but it also provides a valuable foundation for interdisciplinary research to students in related disciplines. Senior or graduate standing standing is required to take this course.
18-765 Digital System Testing and Testable Design
Fall: 12 units
For this course, time- and topic-indexed videos of lecture, homework, projects, etc. will be available from the online learning portal/website. In addition to these resources, two 1-hour live sessions are scheduled per week for recitation. Each student is strongly urged to attend one of these two sessions each week, either remotely or in the classroom on the Carnegie-Mellon Pittsburgh campus. This course examines in depth the theory and practice of fault analysis, test generation, and design for testability for digital ICs and systems. The topics to be covered include circuit and system modeling; fault sources and types; the single stuck-line (SSL), delay, and functional fault models; fault simulation methods; automatic test pattern generation (ATPG) algorithms for combinational and sequential circuits, including the D-algorithm, PODEM, FAN, and the genetic algorithm; testability measures; design-for-testability; scan design; test compression methods; logic-level diagnosis; built-in self-testing (BIST); VLSI testing issues; and processor and memory testing. Advance research issues, including topics on MEMS and mixed-signal testing are also discussed. 4 hours of lecture per week Prerequisites: 18-240 and 15-211 and (18-340 or 18-341) Senior or graduate standing required.
Prerequisites: 15-214 and 18-240 and (18-340 or 18-341)

Course Website: http://www.ece.cmu.edu/~ee765/
18-771 Linear Systems
Spring: 12 units
A modern approach to the analysis and engineering applications of linear systems. Modeling and linearization of multi-input— multi-output dynamic physical systems. State-variable and transfer function matrices. Emphasis on linear and matrix algebra. Numerical matrix algebra and computational issues in solving systems of linear algebraic equations, singular value decomposition, eigenvalue-eigenvector and least-squares problems. Analytical and numerical solutions of systems of differential and difference equations. Structural properties of linear dynamic physical systems, including controllability, observability and stability. Canonical realizations, linear state-variable feedback controller and asymptotic observer design. Design and computer applications to electronic circuits, control engineering, dynamics and signal processing. 4 hrs. lec. Pre-Reqs: 18-470 or 18-474 and Graduate standing in CIT or MCS.
Prerequisites: 18-370 or 18-474
18-785 Data, Inference, and Applied Machine Learning
Fall: 12 units
Please see the ECE website https://www.ece.cmu.edu/ for more information.
18-792 Advanced Digital Signal Processing
Fall: 12 units
This course will examine a number of advanced topics and applications in one-dimensional digital signal processing, with emphasis on optimal signal processing techniques. Topics will include modern spectral estimation, linear prediction, short-time Fourier analysis, adaptive filtering, plus selected topics in array processing and homomorphic signal processing, with applications in speech and music processing. 4 hrs. lec.
Prerequisites: 18-491 and 36-217
18-878 Special Topics in Systems and Controls
Spring: 6 units
Please go to the ECE Website to view "Special Topics in Systems and Controls" course descriptions. http:/www.ece.cmu.edu/courses/index.html
18-883 Special Topics in Energy Systems
Spring: 6 units
Please see the ECE website for a full course description describing the sections of this course.

Faculty

JIM BAIN, Professor of Electrical and Computer Engineering and Materials Science Engineering; Associate Director, Data Storage Systems Center – Ph.D., Stanford University; Carnegie Mellon, 1993–.

LUJO BAUER, Associate Professor of Electrical and Computer Engineering and Computer Science, Institute for Software Research – Ph.D., Princeton University; Carnegie Mellon, 2005–.

VIJAYAKUMAR BHAGAVATULA, Interim Vice Provost for Research - U.A. and Helen Witaker Professor of Electrical and Computer Engineering – Ph.D., Carnegie Mellon University; Carnegie Mellon, 1982–.

CATHY BISHOP, Instructor of Electrical and Computer Engineering - Carnegie Mellon University, Africa Campus – Ph.D.,Carnegie Mellon, 2015–.

SHAWN BLANTON, Professor of Electrical and Computer Engineering – Ph.D., University of Michigan; Carnegie Mellon, 1995–.

TIMOTHY X. BROWN, Distinguished Service Professor, Engineering and Public Policy, Civil and Environmental Engineering; Professor of Electrical and Computer Engineering, Africa Campus – PhD, California Institute of Technology; Carnegie Mellon, 2013–.

DAVID BRUMLEY, Bosch Security and Privacy Professor of Electrical and Computer Engineering; Courtesy Faculty Computer Science; Director of CyLab – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2008–.

L. RICHARD CARLEY, ST Microelectronics Professor of Electrical and Computer Engineering; Affiliated Faculty - Data Storage Systems Center – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 1984–.

MAYSAM CHAMANZAR, Assistant Professor of Electrical and Computer Engineering – Ph.D. , Georgia Institute of Technology; Carnegie Mellon, 2015–.

YUEJIE CHI, Associate Professor of Electrical and Computer Engineering – Ph.D., Princeton University; Carnegie Mellon, 2018–.

ANUPAM DATTA, Associate Professor of Electrical and Computer Engineering; Courtesy Professor Computer Science; Carnegie Mellon University, Silicon Valley Campus – Ph.D., Stanford University; Carnegie Mellon, 2007–.

HAKAN ERDOGMUS, Associate Teaching Professor Carnegie Mellon University, Silicon Valley – Ph.D., Université du Québec; Carnegie Mellon, 2014–.

GIULIA FANTI, Assistant Professor of Electrical and Computer Engineering – Ph.D., University of California at Berkeley; Carnegie Mellon, 2017–.

GARY FEDDER, Howard M. Wilkoff Professor of Electrical and Computer Engineering and Robotics; Interim CEO of the Advanced Robotics for Manufacturing Institute – Ph.D., University of California at Berkeley; Carnegie Mellon, 1994–.

FRANZ FRANCHETTI, Professor of Electrical and Computer Engineering; Faculty Director IT Services – Ph.D., Vienna University of Technology; Carnegie Mellon, 2001–.

GREGORY R. GANGER, Jatras Professor of Electrical and Computer Engineering and Computer Science; Director Parallel Data Lab – Ph.D., University of Michigan; Carnegie Mellon, 1997–.

AMINATA GARBA, Assistant Teaching Professor of Electrical and Computer Engineering; Carnegie Mellon University, Africa Campus – Ph.D., McGill University; Carnegie Mellon, 2013–.

SAUGATA GHOSE, Systems Scientist, Electrical and Computer Engineering – Ph.D., Cornell University; Carnegie Mellon, 2017–.

PHILLIP GIBBONS, Professor of Electrical and Computer Engineering and Computer Science – Ph.D., University of California at Berkeley; Carnegie Mellon, 2015–.

VIRGIL GLIGOR, Professor of Electrical and Computer Engineering; Co-Director CyLab – Ph.D., University of California, Berkeley; Carnegie Mellon, 2008–.

PULKIT GROVER, Assistant Professor of Electrical and Computer Engineering; Center for Neural Basis of Cognition – Ph.D., University of California at Berkeley; Carnegie Mellon, 2013–.

JAMES HOE, Professor of Electrical and Computer Engineering and Computer Science; Director, CALCM – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 2000–.

BOB IANNUCCI, Director, CyLab Mobility Research Center; Distinguished Service Professor of Electrical and Computer Engineering; Silicon Valley Campus – Ph.D., Massachusetts Institute of Technology; .

MARIJA ILIC, Professor of Electrical and Computer Engineering and Engineering and Public Policy – Ph.D., Washington University; Carnegie Mellon, 2002–.

JOVAN ILIC, Associate Teaching Professor of Electrical and Computer Engineering – Ph.D., The University of Tennessee; Carnegie Mellon, 2014–.

LIMIN JIA, Assistant Research Professor of Electrical and Computer Engineering; CyLab; Information Networking Institute – Ph.D., Princeton University; Carnegie Mellon, 2013–.

CARLEE JOE-WONG, Assistant Professor of Electrical and Computer Engineering; Carnegie Mellon University - Silicon Valley Campus – Ph.D., Princeton University ; Carnegie Mellon, 2016–.

GAURI JOSHI, Assistant Professor of Electrical and Computer Engineering – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 2017–.

SOUMMYA KAR, Associate Professor of Electrical and Computer Engineering – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2011–.

HYONG S. KIM, Drew D. Perkins (E’86) Professor of Electrical and Computer Engineering; – Ph.D., University of Toronto; Carnegie Mellon, 1990–.

PHILIP J. KOOPMAN, Associate Professor of Electrical and Computer Engineering and Computer Science – Ph.D., Carnegie Mellon University; Carnegie Mellon, 1989–.

JELENA KOVAČEVIĆ, Hamerschlag University Professor of Electrical and Computer Engineering; University Professor of BioMedical Engineering – Ph.D., Columbia University; Carnegie Mellon, 2003–.

BRUCE H. KROGH, Professor of Electrical and Computer Engineering – Ph.D., University of Illinois at Urbana-Champaign; Carnegie Mellon, 1983–.

SWARUN S. KUMAR, Assistant Professor of Electrical and Computer Engineering – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 2015–.

IAN LANE, Associate Research Professor of Electrical and Computer Engineering; Carnegie Mellon University, Silicon Valley Campus – Ph.D., Kyoto University, Japan; Carnegie Mellon, 2011–.

TZE MENG LOW, Systems Scientist, Electrical and Computer Engineering – Ph.D., University of Texas at Austin; Carnegie Mellon, 2013–.

BRANDON LUCIA, Assistant Professor of Electrical and Computer Engineering – Ph.D., University of Washington; Carnegie Mellon, 2014–.

KEN MAI, Senior Systems Scientist, Electrical and Computer Engineering – Ph.D., Stanford University; Carnegie Mellon, 2005–.

DIANA MARCULESCU, David Edward Schramm Professor, Electrical and Computer Engineering; ; Associate Department Head for Academic Affairs, Electrical and Computer Engineering – Ph.D., University of Southern California; Carnegie Mellon, 2000–.

RADU MARCULESCU, Professor of Electrical and Computer Engineering – Ph.D., University of Southern California; Carnegie Mellon, 2000–.

OLE MENGSHOEL, Principal Systems Scientist, Electrical and Computer Engineering; Carnegie Mellon University, Silicon Valley Campus – Ph.D., University of Illinois, Urbana-Champaign; Carnegie Mellon, 2009–.

M. GRANGER MORGAN, Professor of Electrical and Computer Engineering; Lord University Professor Engineering and Public Policy; Professor Heinz School of Public Policy and Management – Ph.D., University of California, San Diego; Carnegie Mellon, 1974–.

JOSÉ M. F. MOURA, University Professor of Electrical and Computer Engineering; Associate Department Head for Research & Strategic Initiatives; Professor of BioMedical Engineering - Affiliated Faculty - Data Storage Systems Center – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 1986–.

LINDA MOYA, Assistant Teaching Professor of Electrical and Computer Engineering; Social and Decision Sciences – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2014–.

TAMAL MUKHERJEE, Professor of Electrical and Computer Engineering – Ph.D., Carnegie Mellon University; Carnegie Mellon, 1996–.

WILLIAM NACE, Associate Teaching Professor of Electrical and Computer Engineering – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2008–.

PRIYA NARASIMHAN, Professor of Electrical and Computer Engineering – Ph.D., University of California at Santa Barbara; Carnegie Mellon, 2001–.

ROHIT NEGI, Professor of Electrical and Computer Engineering - Affiliated Faculty - Data Storage Systems Center – Ph.D., Stanford University; Carnegie Mellon, 2000–.

DAVID O'HALLARON, Professor of Electrical and Computer Engineering and Computer Science – Ph.D., University of Virginia; Carnegie Mellon, 1989–.

JEYANANDH PARAMESH, Associate Professor of Electrical and Computer Engineering – Ph.D., University of Washington; Carnegie Mellon, 2007–.

BRYAN PARNO, Associate Professor of Electrical and Computer Engineering; Computer Science – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2017–.

CÉCILE PÉRAIRE, Assistant Teaching Professor of Electrical and Computer Engineering, Carnegie Mellon University, Silicon Valley Campus – Ph.D., École polytechnique fédérale de Lausanne; Carnegie Mellon, 2014–.

GIANLUCA PIAZZA, Professor of Electrical and Computer Engineering; Courtesy Faculty Mechanical Engineering; Director Nano Systems Lab – Ph.D., University of California at Berkeley; Carnegie Mellon, 2012–.

LAWRENCE T. PILEGGI, Tanoto Professor of Electrical and Computer Engineering; – Ph.D., Carnegie Mellon University; Carnegie Mellon, 1996–.

RAGUNATHAN RAJKUMAR, George Westinghouse Professor of Electrical and Computer Engineering; Courtesy Faculty Robotic Institute; Director, GM-CM CRL; Director, Real-Time and Multimedia Systems Laboratory – Ph.D., Carnegie Mellon University; Carnegie Mellon, 1992–.

ANTHONY ROWE, Associate Professor of Electrical and Computer Engineering – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2009–.

ASWIN SANKARANARAYANAN, Assistant Professor of Electrical and Computer Engineering – Ph.D., University of Maryland; Carnegie Mellon, 2013–.

MARIOS SAVVIDES, Research Professor of Electrical and Computer Engineering; Director Cylab Biometrics Center – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2005–.

VYAS SEKAR, Assistant Professor of Electrical and Computer Engineering – Ph.D. , Carnegie Mellon University ; Carnegie Mellon, 2013–.

JOHN SHEN , Professor of Electrical and Computer Engineering; Carnegie Mellon University, Silicon Valley Campus – Ph.D., University of Southern California; Carnegie Mellon, 2015–.

DANIEL P. SIEWIOREK, Buhl University Professor of Electrical and Computer Engineering and Computer Science; Human Computer Interaction Institute – Ph.D., Stanford University; Carnegie Mellon, 1972–.

BRUNO SINOPOLI, Professor of Electrical and Computer Engineering; Courtesy Faculty Robotics Institute and Mechanical Engineering – Ph.D., University of California, Berkeley; Carnegie Mellon, 2007–.

VIRGINIA SMITH, Assistant Professor of Electrical and Computer Engineering – Ph.D., University of California, Berkeley; Carnegie Mellon, 2018–.

PETER STEENKISTE, Professor of Electrical and Computer Engineering and Computer Science – Ph.D., Stanford University; Carnegie Mellon, 1987–.

RICHARD M. STERN, JR., Professor of Electrical and Computer Engineering, Language Technologies Institute, Computer Science, and BioMedical Engineering; Lecturer, Music – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 1977–.

ANDRZEJ J. STROJWAS, Keithley Professor of Electrical and Computer Engineering – Ph.D., Carnegie Mellon University; Carnegie Mellon, 1983–.

THOMAS SULLIVAN, Associate Teaching Professor of Electrical and Computer Engineering; Lecturer, Music – Ph.D., Carnegie Mellon University; Carnegie Mellon, 1996–.

PATRICK TAGUE, Associate Research Professor of Electrical and Computer Engineering, Cylab and Information Networking Institute, Carnegie Mellon University, Silicon Valley – Ph.D., University of Washington; Carnegie Mellon, 2009–.

OZAN TONGUZ, Professor of Electrical and Computer Engineering – Ph.D., Rutgers University; Carnegie Mellon, 2000–.

ELIAS TOWE, Professor of Electrical and Computer Engineering; Albert and Ethel Grobstein Memorial Professor of Materials Science and Engineering; Director Center for Nano-enabled Device and Energy Technologies – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 2001–.

TAHA SELIM USTUN , Assistant Teaching Professor of Electrical and Computer Engineering; Carnegie Mellon University, Africa Campus – Ph.D., Victoria University; Carnegie Mellon, 2013–.

DAVID VERNON, Professor of Electrical and Computer Engineering; Carnegie Mellon University, Africa Campus – Ph.D., Trinity College Dublin; Carnegie Mellon, 2017–.

OSMAN YAĞAN, Assistant Research Professor of Electrical and Computer Engineering – Ph.D., University of Maryland, College Park; Carnegie Mellon, 2013–.

BYRON YU, Associate Professor of Electrical and Computer Engineering; Assistant Professor BioMedical Engineering – Ph.D., Stanford University; Carnegie Mellon, 2009–.

PEI ZHANG, Associate Research Professor of Electrical and Computer Engineering; Carnegie Mellon University, Silicon Valley Campus – Ph.D., Princeton University; Carnegie Mellon, 2008–.

JIA ZHANG, Associate Teaching Professor of Electrical and Computer Engineering; Carnegie Mellon University, Silicon Valley Campus – Ph.D., University of Illinois, Chicago; Carnegie Mellon, 2014–.

JIAN-GANG ZHU, ABB Professor of Electrical and Computer Engineering; Materials Science and Engineering; Physics; Director Data Storage Systems Center – Ph.D., University of California, San Diego; Carnegie Mellon, 1997–.

Courtesy

YURVRAJ AGARWAL, Assistant Professor of Computer Science; Courtesy Faculty of Electrical and Computer Engineering – Ph.D., University of California, San Diego; Carnegie Mellon, 2013–.

DAVID ANDERSEN, Associate Professor of Computer Science; Courtesy Faculty of Electrical and Computer Engineering – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 2014–.

JAMES ANTAKI, Professor of BioMedical Engineering; Courtesy Faculty of Electrical and Computer Engineering – Ph.D., University of Pittsburgh; Carnegie Mellon, 2014–.

NATHAN BECKMANN, Assistant Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 2017–.

MARIO BERGES, Assistant Professor of Civil and Environmental Engineering; Courtesy Faculty of Electrical and Computer Engineering – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2017–.

RANDAL E. BRYANT, University Professor of the School of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 1984–.

KATHLEEN CARLEY, Professor of Computer Science, Institute for Software Research; Courtesy Faculty of Electrical and Computer Engineering – Ph.D., Harvard University; Carnegie Mellon, 2011–.

STEVE CHASE, Assistant Professor Center for the Neural Basis of Cognition and BioMedical Engineering: Courtesy Faculty of Electrical and Computer Engineering – Ph.D., John Hopkins University ; Carnegie Mellon, 2012–.

HOWIE CHOSET, Professor of Robotics Institute; Courtesy Professor of Electrical and Computer Engineering – Ph.D., California Institute of Technology; Carnegie Mellon, 1996–.

NICOLAS CHRISTIN, Associate Research Professor of Engineering and Public Policy; Assistant Research Professor, CyLab; Courtesy Professor of Electrical and Computer Engineering – Ph.D., University of Virginia; Carnegie Mellon, 2005–.

EDMUND M. CLARKE, Fore Systems University Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Cornell University; Carnegie Mellon, 1982–.

LORRIE CRANOR, Professor of Computer Science and Engineering and Public Policy; Courtesy Faculty of Electrical and Computer Engineering – Ph.D., Washington University; Carnegie Mellon, 2008–.

ROBERT DAVIS, John and Claire Bertucci Distinguished Professor of Materials Science and Engineering; Courtesy Professor of Electrical and Computer Engineering – Ph.D., University of California, Berkeley; Carnegie Mellon, 2010–.

ANIND DEY, Professor of Human-Computer Interaction Institute; Courtesy Faculty of Electrical and Computer Eningeering – Ph.D., Georgia Techology; Carnegie Mellon, 2009–.

JOHN DOLAN, Principal Systems Scientist of The Robotics Institute; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2006–.

DAVE ECKHARDT, Assistant Teaching Professor of Computer Science; Courtesy Faculty of Electrical and Computer Engineering – Ph.D.,Carnegie Mellon, 2011–.

CHRISTOS FALOUTSOS, Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D., University of Toronto; Carnegie Mellon, 1998–.

KAYVON FATAHALIAN, Assistant Professor of Computer Science; Courtesy Faculty of Electrical and Computer Engineering – Ph.D., Stanford University ; Carnegie Mellon, 2011–.

RANDY FEENSTRA, Professor of Physics; Courtesy Professor of Electrical and Computer Engineering – Ph.D., California Institute of Technology; Carnegie Mellon, 1995–.

FERNANDO DE LA TORRE FRADE, Associate Research Professor of Robotics Institute; Courtesy Faculty of Electrical and Computer Engineering – Ph.D., La Salle School of Engineering, Barcelona, Spain; Carnegie Mellon, 2009–.

MATT FREDRICKSON, Assistant Professor of Computer Science and Institute of Software Research; Courtesy Faculty of Electrical and Computer Engineering – Ph.D., University of Wisconsin-Madison; Carnegie Mellon, 2016–.

GARTH GIBSON, Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D., University of California at Berkeley; Carnegie Mellon, 1991–.

SETH C. GOLDSTEIN, Associate Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D., University of California at Berkeley; Carnegie Mellon, 1997–.

MOR HARCHOL-BALTER, Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D., University of California at Berkeley; Carnegie Mellon, 1999–.

ALEX HILLS, Distinguished Service Professor of Engineering and Public Policy; Courtesy Professor of Electrical and Computer Engineering; – Ph.D., Carnegie Mellon University; Carnegie Mellon, 1992–.

RALPH HOLLIS, Research Professor of Robotics Institute; Courtesy Professor of Electrical and Computer Engineering; Carnegie – Ph.D., University of Colorado, Boulder; Carnegie Mellon, 1993–.

JASON HONG, Associate Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D., University of California at Berkeley; Carnegie Mellon, 2010–.

MOHAMAD ISLAM, Associate Research Professor of Materials Science and Engineering; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Lehigh University; Carnegie Mellon, 2008–.

FARNAM JAHANIAN, Vice President of Research; Courtesy Professor of Electrical and Computer Engineering – Ph.D., University of Texas at Austin; Carnegie Mellon, 2014–.

TAKEO KANADE, U.A. and Helen Whitaker Professor of Computer Science; Robotics Institute; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Kyoto University; Carnegie Mellon, 1980–.

SHAWN KELLY, Senior Systems Scientist, The Institute for Complex Engineered Systems; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 2012–.

ZICO KOLTER, Assistant Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D.,Carnegie Mellon, 2015–.

DAVE LAUGHLIN, ALCOA Professor of Materials Science Engineering; Courtesy Professor of Electrical and Computer Engineering - Affiliated Faculty - Data Storage Systems Center – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 1974–.

SARA MAJETICH, Professor of Physics; Courtesy Professor of Electrical and Computer Engineering; Affiliated Faculty - Data Storage Systems Center – Ph.D., University of Georgia; Carnegie Mellon, 2010–.

JENNIFER MANKOFF, Associate Professor of Human Computer Interaction Institute; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Georgia Institute of Technology; Carnegie Mellon, 2015–.

ROY MAXION, Research Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D., University of Colorado; Carnegie Mellon, 1984–.

JAMES MORRIS, Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering; – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 1982–.

TODD MOWRY, Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering; Co-Director CALCM – Ph.D., Stanford University; Carnegie Mellon, 1997–.

SRINIVASA NARASIMHAN, Professor of Robotics Institute; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Columbia University ; Carnegie Mellon, 2016–.

HAE YOUNG NOH, Assistant Professor of Civil and Environmental Engineering; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Stanford University ; Carnegie Mellon, 2014–.

CORINA PASAREANU, Senior Research Scientist, Carnegie Mellon University, Silicon Valley Campus; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Kansas State University; Carnegie Mellon, 2015–.

ANDY PAVLO, Assistant Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D.,Carnegie Mellon, 2014–.

JON M. PEHA, Professor of Engineering and Public Policy; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Stanford University; Carnegie Mellon, 1991–.

ANDRE PLATZER, Associate Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D., University of Oldenburg, Germany; Carnegie Mellon, 2010–.

BHIKSHA RAJ RAMAKRISHNAN, Associate Professor of Language Technologies Institute; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2009–.

RAJ REDDY, Mozah Bint Nasser University Professor of Computer Science and Robotics; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Stanford University ; Carnegie Mellon, 2000–.

MAJD SAKR, Teaching Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D., University of Pittsburgh; Carnegie Mellon, 20015–.

MAHADEV SATYANARAYANAN, Carnegie Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Carnegie Mellon University; Carnegie Mellon, 1983–.

JEFF SCHNEIDER, Research Professor of Robotics Institute; Courtesy Professor of Electrical and Computer Engineering – Ph.D. , University of Rochester; Carnegie Mellon, 2013–.

SRINIVASAN SESHAN, Associate Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D., University of California at Berkeley; Carnegie Mellon, 2000–.

RITA SINGH, Research Faculty Language Technologies Institute; Courtesy Pfrofessor of Electrical and Computer Engineering – Ph.D.,Carnegie Mellon, 2017–.

MARVIN A. SIRBU, Professor of Engineering and Public Policy; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 1985–.

MAREK SKOWRONSKI, Professor Materials Science Engineering; Courtesy Professor of Electrical and Computer Engineering; Affiliated Faculty - Data Storage Systems Center – Ph.D., Warsaw University; Carnegie Mellon, 2014–.

ASIM SMAILAGIC, Research Professor of ICES; Director of LINCS; Courtesy Professor of Electrical and Computer Engineering – Ph.D., University of Sarajevo and University of Edinburgh; Carnegie Mellon, 1992–.

ALEX SMOLA, Professor of Machine Learning; Courtesy Professor of Electrical and Computer Engineering – Ph.D., University of Technology Berlin; Carnegie Mellon, 2014–.

KOUSHIL SREENATH, Assistant Professor of Robotics Institute; Courtesy Professor of Electrical and Computer Science – Ph.D., University of Michigan; Carnegie Mellon, 2014–.

SRIDHAR TAYUR, CoFord Distinguished Research Chair, Professor of Operations Management Tepper School of Business; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Cornell University; Carnegie Mellon, 2017–.

ADRIEN TREUILLE, Assistant Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D., University of Washington; Carnegie Mellon, 2010–.

MANUELA VELOSO, Herbert A. Simon University Professor; Head Machine Learning; Courtesy Professor of Electrical and Computer Engineering – Ph.D.,Carnegie Mellon, 2011–.

LEE WEISS, Research Professor of Robotics Institute; Courtesy Professor of Electrical and Computer Engineering – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2009–.

ERIK YDSTIE, Professor of Chemical Engineering; Courtsey Professor of Electrical and Computer Engineering – Ph.D., Imperial College, London; Carnegie Mellon, 1992–.

HUI ZHANG, Professor of Computer Science; Courtesy Professor of Electrical and Computer Engineering – Ph.D., University of California, Berkeley; Carnegie Mellon, 1995–.

Adjunct Faculty

NIKHIL BALRAM, Adjunct Faculty - Electrical and Computer Engineering – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2014–.

RONALD P. BIANCHINI, Adjunct Faculty - Electrical and Computer Engineering – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2005–.

PEDRO CARVALHO, Adjunct Faculty - Electrical and Computer Engineering; Professor Instituto Superior Técnico of the Universidade Técnica de Lisboa - Portugal – Ph.D.,Carnegie Mellon, 2014–.

TSUHAN CHEN, Adjunct Faculty - Electrical and Computer Engineering; Professor of Electrical and Computer Engineering at Cornell University – Ph.D., California Institute of Technology; Carnegie Mellon, 1997–.

BABAK FALSAFI, Adjunct Faculty - Electrical and Computer Engineering and Computer Science; – Ph.D., University of Wisconsin, Madison; Carnegie Mellon, 2001–.

PETER GILGUNN, Adjunct Faculty – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2012–.

GABRIELA HUG, Adjunct Faculty - Electrical and Computer Engineering – Ph.D., ETH Zurich, Switzerland; Carnegie Mellon, 2009–.

ABE ISHIHARA, Adjunct Faculty - Carnegie Mellon University, Silicon Valley Campus – Ph.D., Stanford University; Carnegie Mellon, 2012–.

COLLIN JACKSON, Adjunct Faculty - Electrical and Computer Engineering, Cylab and Information Networking Institute, Carnegie Mellon Silicon Valley – Ph.D., Stanford University; Carnegie Mellon, 2009–.

ALEK KAVCIC, Adjuncted Faculty - Electrical and Computer Engineering – Ph.D.,Carnegie Mellon, 2016–.

PRADEEP KHOSLA, Adjunct Faculty - Electrical and Computer Engineering; Chancellor, University of California, San Diego – Ph.D., Carnegie Mellon University; Carnegie Mellon, 1986–.

XIN LI, Adjunct Faculty - Electrical and Computer Engineering – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2001–.

MING LI, Adjunct Faculty - Electrical and Computer Engineering; Assistant Professor Sun Yat-sen University, China – Ph.D., University of Southern California; Carnegie Mellon, 2014–.

JASON LOHN, Adjunct Associate Research Faculty - Electrical and Computer Engineering; Senior Research Scientist, Carnegie Mellon Silicon Valley – Ph.D., University of Maryland; Carnegie Mellon, 2009–.

TIMOTHY MCCOY, Adjunct Faculty - Electrical and Computer Engineering; Director, Research and Development Converteam North America – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 2008–.

NATASA MISKOV-ZIVANOV, Adjunct Faculty - Electrical and Computer Engineering; Assistant Professor University of Pittsburgh – Ph.D., Carnegie Mellon University ; Carnegie Mellon, 2016–.

ONUR MUTLU, Adjunct Faculty - Electrical and Computer Engineering and Computer Science – Ph.D., University of Texas at Austin; Carnegie Mellon, 2009–.

ADRIAN PERRIG, Adjunct Faculty - Electrical and Computer Engineering, Engineering and Public Policy and Computer Science; Technical Director, Cylab – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2002–.

MARKUS PÜSCHEL, Adjunct Faculty – Ph.D., University of Karlsruhe; Carnegie Mellon, 1999–.

DAVID RICKETTS, Adunct Faculty - Electrical and Computer Engineering – Ph.D., Harvard University; Carnegie Mellon, 2006–.

GUSTAVO ROHDE, Adjunct Faculty - BioMedical Engineering and Electrical and Computer Engineering – Ph.D., University of Maryland; Carnegie Mellon, 2006–.

ROB A. RUTENBAR, Adjunct Faculty - Electrical and Computer Engineering and Computer Science; – Ph.D., University of Michigan; Carnegie Mellon, 1984–.

TUVIAH E. SCHLESINGER, Adjunct Faculty – Ph.D., California Institute of Technology; Carnegie Mellon, 1985–.

DAWN SONG, Adjunct Faculty - Electrical and Computer Engineering and Computer Science – Ph.D., University of California at Berkeley; Carnegie Mellon, 2002–.

DANIEL D. STANCIL, Adjunct Faculty - Electrical and Computer Engineering – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 1986–.

XIAOYING TANG, Adjunct Faculty - Electrical and Computer Engineering – Ph.D., Johns Hopkins University; Carnegie Mellon, 2016–.

LEENDERT VAN DOORN, Adjunct Faculty - Electrical and Computer Engineering; Senior Fellow AMD – Ph.D.,Carnegie Mellon, 2009–.

PAULO VERISSIMO, Adjunct Faculty - Electrical and Computer Engineering and Professor of University of Lisboa, Portugal – Ph.D., IST of the Technical University of Lisboa; Carnegie Mellon, 2008–.

KAI WANG, Adjunct Faculty - Electrical and Computer Engineering; Sun Yat-Sen University, China – Ph.D., University of Waterloo; Carnegie Mellon, 2014–.

ANTHONY WASSERMAN, Adjunct Faculty - Electrical and Computer Engineering; Silicon Valley Campus – Ph.D., University of Wisconsin; Carnegie Mellon, 2008–.

JEFF WELDON, Adjunct Professor of Electrical and Computer Engineering – Ph.D., University of California, Berkeley; Carnegie Mellon, 2011–.

JEANNETTE WING, President’s Professor of Computer Science; Adjunct Faculty - Electrical and Computer Engineering – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 1985–.

YINLIANG XU, Adjunct Faculty - Electrical and Computer Engineering; Sun Yat-Sen University, China – Ph.D.,Carnegie Mellon, 2015–.

TERRY YE, Adjunct Faculty - Electrical and Computer Engineering; Sun Yat-Sen University, China – Ph.D.,Carnegie Mellon, 2016–.

ZHIYI YU, Adjunct Faculty - Electrical and Computer Engineering; Associate Professor of Microelectronics, Fudan University – Ph.D.,Carnegie Mellon, 2016–.

JOY ZHANG, Adjunct Faculty - Electrical and Computer Engineering; Carnegie Mellon, Silicon Valley Campus – Ph.D., Carnegie Mellon University ; Carnegie Mellon, 2009–.

JINGXI ZHU, Adjunct Faculty - Electrical and Computer Engineering; Sun Yat-Sen University, China – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2014–.

Emeriti

DAVID W. GREVE, Emeritus Professor of Electrical and Computer Engineering – Ph.D., Lehigh University; Carnegie Mellon, 1982–.

MARTIN GRISS, Emeritus Professor of Carnegie Mellon University, Silicon Valley and CyLab – Ph.D., University of Illinois; Carnegie Mellon, 2008–.

JAMES F. HOBURG, Emeritus Professor of Electrical and Computer Engineering – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 1975–.

ANGEL JORDON, Emeritus University Professor of Electrical and Computer Engineering, – Ph.D., Carnegie Mellon University; Carnegie Mellon, 1959–.

MARK H. KRYDER, Emeritus University Professor of Electrical and Computer Engineering; Chief Technical Officer and Vice President of Research, Seagate (Retired) – Ph.D., California Institute of Technology; Carnegie Mellon, 1978–.

DAVID N. LAMBETH, Emeritus Professor of Electrical and Computer Engineering and Materials Science and Engineering – Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 1989–.

WOJCIECH MALY, Emeritus Professor of Electrical and Computer Engineering – Ph.D., Polish Academy of Sciences, Warsaw; Carnegie Mellon, 1986–.

CHARLES P. NEUMAN, Emeritus Professor of Electrical and Computer Engineering – Ph.D., Harvard University; Carnegie Mellon, 1969–.

RONALD ROHRER, Emeritus Professor of Electrical and Computer Engineering – Ph.D., University of California, Berkeley; Carnegie Mellon, 2008–.

SAROSH N. TALUKDAR, Emeritus Professor of Electrical and Computer Engineering – Ph.D,, Purdue University; Carnegie Mellon, 1974–.

DONALD E. THOMAS, JR., Emeritus Professor of Electrical and Computer Engineering – Ph.D., Carnegie Mellon University; Carnegie Mellon, 1977–.

ROBERT WHITE, Emeritus University Professor Emeritus of Electrical and Computer Engineering and Engineering and Public Policy – Ph.D., Stanford University; Carnegie Mellon, 1993–.

Affiliated Faculty

SHELLEY ANNA, Associate Professor of Mechanical Engineering; Affiliated Faculty - Data Storage Systems Center – Ph.D. ,Carnegie Mellon, 2007–.

LUC BERGER, Emeritus Professor of Physics; Affiliated Faculty - Data Storage Systems Center – Ph.D., University of Lausanne (Switzerland); .

MYUNG S. JHON, Professor of Chemical Engineering; Affiliated Faculty - Data Storage Systems Center – Ph.D., University of Chicago; .

MICHAEL E. MCHENRY, Professor of Materials Science and Engineering; Affiliated Faculty - Data Storage Systems Center – Ph.D., Massachusetts Institute of Technology; .

O. BURAK OZDOGANLAR, Ver Planck Professor of Mechanical Engineering; Affiliated Faculty - Data Storage Systems Center – Ph.D., University of Michigan; Carnegie Mellon, 2007–.

PAUL A. SALVADOR, Professor of Materials Science and Engineering; Affiliated Faculty - Data Storage Systems Center – Ph.D., Northwestern University; .

VINCENT SOKALSKI, Assistant Research Professor of Materials Science and Engineering; Affiliated Faculty - Data Storage Systems Center – Ph.D., Carnegie Mellon University ; Carnegie Mellon, 2013–.