Electrical and Computer Engineering

Academic Year 2023 – 2024

General Information

Address
Engineering Quadrangle
Phone

Program Offerings:

  • Ph.D.
  • M.Eng.

Director of Graduate Studies:

Graduate Program Administrator:

Overview

The Department of Electrical and Computer Engineering doctoral program draws students from all over the world. Most candidates enter the program directly after completing an undergraduate degree in disciplines such as electrical engineering, computer science, or physics. Although our doctoral program is one of the largest at Princeton, its scale still allows students to receive personal attention and extensive faculty interaction.

Research in the Department is collaborative and interdisciplinary. The current main themes of research span areas from applied physics, devices, advanced circuits, and high-performance computing to security, data and information science, and artificial intelligence. Details on those research themes and related application domains can be found on the Department’s website under the Research link and faculty and research group websites. There are also a variety of interdisciplinary research centers at Princeton that enhance and broaden educational and research opportunities. 

New graduate students spend the first semester on coursework and typically select a thesis research advisor at the start of the spring semester, based on a match of research interests. The program combines a balance of preliminary and advanced coursework (400/500 numbered courses) and innovative research leading to a doctoral dissertation and award of the Ph.D. degree. Candidates earn a Masters of Arts degree en route to the Ph.D. degree. The nominal length of the program is five years. Students maintaining good progress will be provided with full financial support during the duration of the program. This support covers university tuition and fees and provides a stipend for living expenses. It is awarded through a combination of university fellowships and research/teaching assistantship positions. Housing is available for all first-year graduate students, and most students are accommodated in university housing for the duration of their regular enrollment. Many additional details about the program can be found in the Graduate Student Handbook.

Apply

Application deadline
Ph.D. - December 15, 11:59 p.m. Eastern Standard Time; MEng - January 3, 11:59 p.m. (This deadline is for applications for enrollment beginning in fall 2024)
Program length
Ph.D. 5 years, M.Eng 1 year
Fee
$75
GRE
General Test - optional/not required

Additional departmental requirements

Ph.D. applicants are required to select an area of research interest when applying.

The M.Eng. program in Electrical and Computer Engineering for academic year 2024-25 is intended for all Princeton seniors in the Class of 2024. If you submit an application to the M.Eng. program, please list 8 courses that you are interested in taking in your statement of purpose.

 

Program Offerings

Program Offering: Ph.D.

Program description

The doctoral program combines coursework and participation in original research. Most students enter the program with an undergraduate degree in electrical engineering, computer science, physics, or a related discipline. Some have a master’s degree, but that is not necessary for success in the program. Every admitted Ph.D. student is given financial support in the form of a first-year fellowship. Students in academic good standing are supported by a teaching assistant or research assistant after the first year. Students who remain on campus working with their adviser during the summer will receive summer salary. In addition, all admitted Ph.D. students are automatically considered for the prestigious Wu and Upton Fellowships.

Courses

In the first year of the program, the main emphasis is on coursework. Students take courses both for proficiency within their specialty as well as for breadth. The program has no specific required courses, but there is a required minimum course count and a required minimum GPA. Students must complete a minimum of six courses in their area(s) of interest in preparation for research and the general examination during the first year. Each student is assigned a first-year academic adviser who assists the student in determining the appropriate courses. Students must complete a minor area of study. This can be completed by achieving a GPA of at least 3.3 in two or more coherent courses approved by the adviser. The courses must be in an area distinct from the student's research.

Additional pre-generals requirements

Research Adviser
Each incoming student to the Department of Electrical and Computer Engineering is assigned an academic adviser to help with course selection and other student concerns. Midway through the fall semester, each student gives a rank-ordered list of preferred research advisers. This information is combined with the faculty’s preference ranking of students and available funding to arrive at the faculty-student pairing. This is usually done by the end of the fall semester in the student’s first year. Students should consult with their academic adviser and the faculty coordinator for guidance during the adviser selection process. Once the adviser and the student have agreed upon the advisee-adviser pairing, the chosen adviser takes over academic and research advising.

Choosing a research adviser is one of the most important steps in the Ph.D. program; it should be done with care. Students should prepare for adviser selection by reviewing research materials for all faculty members in their area of interest and speaking with potential advisers to determine intellectual fit and capability.

General exam

Students are expected to successfully complete the general during the fourth term of their Ph.D. studies. Students are not normally readmitted to a third-year (fifth term) of graduate study unless they have successfully completed the general examination. The general examination consists of a research seminar and an oral exam. The seminar is a 45-minute presentation of research accomplished at Princeton. It is intended to indicate that a student is capable of independent research and has started a research topic that has the potential to lead to a doctoral dissertation. The examination committee administers the oral exam. It is held not more than one month after the research seminar and within the periods set by the Graduate School for the general exam. The research adviser selects the examination committee in consultation with the student.

Qualifying for the M.A.

The Master of Arts can be earned by Ph.D. students en route to their Ph.D., after the student has: (a) presented a research seminar approved by the student’s general examination committee and (b) passed the oral general examination. It may also be awarded to students who, for various reasons, leave the Ph.D. program, provided that these requirements have been met.

Please note, students admitted to the Ph.D. program who do not wish to complete the program may be considered for an M.S.E. degree with approval from the department and the Graduate School. Ph.D. students who have already been awarded the incidental M.A. are not eligible to earn an M.S.E.

Teaching

Teaching experience is considered to be a significant part of graduate education. Prior to completion of the program, doctoral students must complete at least one assignment as a teaching assistant (TA). To be a teaching assistant, a student must first demonstrate proficiency in English by passing or being exempted from, the Princeton Oral Proficiency Test (POPT). Students are encouraged to satisfy the POPT requirement as early as possible.

Post-Generals requirements

At least six months before the FPO (or with the permission of the Director of Graduate Studies for another time), students must schedule and hold a preliminary FPO with the FPO committee present. At the pre-FPO presentation, students are required to present research progress, results to date and present plans, and a timeline to complete their dissertation work. The pre-PFO is meant to give the student valuable feedback from the committee and is not graded.

Dissertation and FPO

The final public oral examination is taken after the candidate’s dissertation has been examined for technical mastery by a committee and approved by the Graduate School; it is primarily a defense of the dissertation.

The Ph.D. is awarded after the candidate’s doctoral dissertation has been accepted and the final public oral examination sustained.

Program Offering: M.Eng.

Program description

The Master of Engineering (M.Eng.) program is designed to enable students to develop a stronger foundation in a technical area for professional practice or preparation for a higher degree.  The degree requires the successful completion of eight courses. A thesis is not required for the M.Eng. degree. However, research project courses are available.

The M.Eng. program in Electrical and Computer Engineering for academic year 2024-25 is intended for all Princeton seniors in the Class of 2024. If applying to the M.Eng. program, please list 8 courses that you are interested in taking in your statement of purpose.

We discourage applications from others, who will not be admitted.

Courses

Master’s students must successfully complete eight graded technical courses at the 400 and/or 500 levels, including at least four courses at the 500-level.  These courses must be approved by the student’s academic advisor. All eight courses must be taken for a grade, and students must have an overall G.P.A. of  "B" (3.0)  or better at the time they complete the program requirements in order to receive the degree. 

Faculty

  • Chair

    • James C. Sturm
  • Associate Chair

    • Claire F. Gmachl
  • Director of Graduate Studies

    • Hakan E. Türeci
    • Mengdi Wang (associate)
  • Director of Undergraduate Studies

    • David Wentzlaff
    • Gerard Wysocki (associate)
  • Professor

    • Ravindra N. Bhatt
    • Stephen Y. Chou
    • Jason W. Fleischer
    • Claire F. Gmachl
    • Andrea J. Goldsmith
    • Andrew A. Houck
    • Niraj K. Jha
    • Antoine Kahn
    • Sanjeev R. Kulkarni
    • Sun-Yuan Kung
    • Stephen A. Lyon
    • Sharad Malik
    • Prateek Mittal
    • H. Vincent Poor
    • Paul R. Prucnal
    • Peter J. Ramadge
    • Barry P. Rand
    • Alejandro W. Rodriguez
    • Kaushik Sengupta
    • Mansour Shayegan
    • James C. Sturm
    • Naveen Verma
    • Pramod Viswanath
  • Associate Professor

    • Jason D. Lee
    • Jeffrey D. Thompson
    • Hakan E. Türeci
    • Mengdi Wang
    • David Wentzlaff
    • Gerard Wysocki
    • Nathalie P. de Leon
  • Assistant Professor

    • Maria Apostolaki
    • Minjie Chen
    • Jaime Fernandez Fisac
    • Tian-Ming Fu
    • Yasaman Ghasempour
    • Sarang Gopalakrishnan
    • Chi Jin
    • Saien Xie
  • Associated Faculty

    • Amir Ali Ahmadi, Oper Res and Financial Eng
    • Craig B. Arnold, Mechanical & Aerospace Eng
    • David I. August, Computer Science
    • Jianqing Fan, Oper Res and Financial Eng
    • Kyle A. Jamieson, Computer Science
    • Gillat Kol, Computer Science
    • Kai Li, Computer Science
    • Lynn Loo, Chemical and Biological Eng
    • Margaret R. Martonosi, Computer Science
    • Jason Petta, Physics
    • Jennifer Rexford, Computer Science
    • Bartolomeo Stellato, Oper Res and Financial Eng
  • Lecturer

    • Hossein Valavi

For a full list of faculty members and fellows please visit the department or program website.

Permanent Courses

Courses listed below are graduate-level courses that have been approved by the program’s faculty as well as the Curriculum Subcommittee of the Faculty Committee on the Graduate School as permanent course offerings. Permanent courses may be offered by the department or program on an ongoing basis, depending on curricular needs, scheduling requirements, and student interest. Not listed below are undergraduate courses and one-time-only graduate courses, which may be found for a specific term through the Registrar’s website. Also not listed are graduate-level independent reading and research courses, which may be approved by the Graduate School for individual students.

COS 516 - Automated Reasoning about Software (also ECE 516)

An introduction to algorithmic techniques for reasoning about software. Basic concepts in logic-based techniques including model checking, invariant generation, symbolic execution, and syntax-guided synthesis; automatic decision procedures in modern solvers for Boolean Satisfiability (SAT) and Satisfiability Modulo Theory (SMT); and their applications in automated verification, analysis, and synthesis of software. Emphasis on algorithms and automatic tools.

COS 526 - Neural Rendering (also ECE 576)

Advanced topics in computer graphics, with focus on learning recent methods in rendering, modeling, and animation. Appropriate for students who have taken COS426 (or equivalent) and who would like further exposure to computer graphics.

COS 583 - Great Moments in Computing (also ECE 583)

Course covers pivotal developments in computing, including hardware, software, and theory. Material will be covered by reading seminal papers, patents, and descriptions of highly-influential architectures. Course emphasizes a deep understanding of the discoveries and inventions that brought computer systems to where they are today, and class is discussion-oriented. Final project or paper required. Graduate students and advanced undergraduates from ELE, COS, and related fields welcome.

ECE 504 - Mixed-signal Circuits and Systems

Discuss design and simulation methodologies for realizing robust analog CMOS circuits implementing major building blocks in AID converters. With attention to design specifications, a comprehensive study of single-ended and differential op-amp topologies are covered with an emphasis on: feedback and stability; linear and non-linear settling; distortion; noise; and voltage swing. Conclude with switched-capacitor circuits exploring impact of non-linearity and noise in sampled systems. Design projects using circuit simulators reinforce theoretical concepts.

ECE 511 - Quantum Mechanics with Applications

This course covers the principles of quantum mechanics, including applications of relevance to students in applied physics, materials science and engineering. Topics include the concept of Hilbert Spaces, Schrodinger and Heisenberg Representations, Bound State problems in one, two and three dimensions, consequences of symmetry, Angular momentum algebra, Approximation methods for stationary states, Many-body systems, Quantum statistics, Time dependent Perturbation Theory, Second Quantization and Electromagnetic Fields.

ECE 514 - Extramural Research Internship

Full-time research internship at a host institution, to perform scholarly research relevant to student's dissertation work. Research objectives will be determined by advisor in conjunction with outside host. A mid-semester progress review and a final paper are required. Enrollment limited to post-generals students for up to two semesters. Special rules apply to international students regarding CPT/OPT use. Students may register by application only.

ECE 515 - Extramural Summer Project

Summer research project designed in conjuction with the student's advisor and an industrial, NGO, or government sponsor, that will provide practical experience relevant to the student's research area. Start date no earlier than June 1. A research project and sponsor's evaluation are required.

ECE 518 - Selected Topics in Computer Engineering and Information Sciences and Systems

Introduction to topics and methods of research in computer engineering and information sciences and systems, providing an overview of current research of the faculty in computer engineering and information sciences and systems. It is meant to help first year graduate students find a research adviser.

ECE 519 - Selected Topics in Solid-State Electronics

One or more advanced topics in solid-state electronics. Contents vary from year to year. Recent topics have included: electronic properties of doped semiconductors, physics and technology of nanostructures, and organic materials for optical and electronic device application.

ECE 520 - Mathematics of Data Science

This is a graduate-level course covering various aspects of mathematical data science, particularly for large-scale problems. It covers the mathematical foundations of several fundamental learning and inference problems, including clustering, spectral methods, tensor decomposition, graphical models, large-scale numerical linear algebra, matrix concentration inequalities, sparse recovery and compressed sensing, low-rank matrix factorization, shallow neural nets, etc. Both convex and nonconvex approaches are discussed. The course focuses on designing algorithms that are effective in both theory and practice.

ECE 521 - Linear System Theory (also MAE 547)

This course covers the fundamentals of linear system theory. Various topics important for further study in dynamic systems, control and communication and signal processing are presented.

ECE 522 - Large-Scale Optimization for Data Science

This course introduces optimization methods suitable for large-scale problems in data science and machine learning applications; algorithms efficient for both smooth and nonsmooth problems, including gradient methods, proximal methods, ADMM, quasi-Newton methods and large-scale numerical linear algebra. We discuss the efficiency of these methods in concrete data science problems (e.g. low-rank matrix recovery, dictionary learning, graph matching), under appropriate statistical models. We introduce a global geometric analysis to characterize the nonconvex landscape of the empirical risks in several estimation and learning problems.

ECE 523 - Nonlinear System Theory (also MAE 548)

A study of the mathematical techniques found useful in the analysis and design of nonlinear systems. Topics include stability and qualitative behavior of differential equations, functional analysis and input/output behavior of systems, and "modern'' nonlinear system theory, which uses both geometric and algebraic techniques. Prerequisite: 521.

ECE 524 - Foundations of Reinforcement Learning

The course is a graduate level course, focusing on theoretical foundations of reinforcement learning. It covers basics of Markov Decision Process (MDP), dynamic programming based algorithms, policy optimization, planning, exploration, as well as information theoretical lower bounds. Various advanced topics are also discussed, including off-policy evaluation, function approximation, partial observable MDP and deep reinforcement learning. This course puts special emphases on the algorithms and their theoretical analyses. Prior knowledge on linear algebra, probability theory, and stochastic process is required.

ECE 525 - Random Processes in Information Systems

Fundamentals of probability and random processes and their applications to information sciences and systems. The course examines sequences of random variables and convergence; stationarity and ergodicity; second-order properties and estimation; Poisson and renewal processes; and Markov processes.

ECE 526 - Digital Communications and Systems

Digital communications and data transmission. Topics include source coding, signal encoding, representation, and quantization; methods of modulation, synchronization, and transmission; optimum demodulation techniques; and communication through band-limited and random channels.

ECE 528 - Information Theory

An exploration of the Shannon theory of information, covering noiseless source coding theory of ergodic sources and channel coding theorems, including channels with memory, multiple-access, and Gaussian channels.

ECE 530 - Theory of Detection Estimation and Learning

Hypothesis testing; detection and estimation of signals in noise; detection of signals with unknown parameters; prediction and filtering of stationary time series; detection of stochastic signals; and nonparametric and robust techniques. Prerequisite: 525 or the equivalent.

ECE 532 - Safety-Critical Robotic Systems (also COS 572/MAE 572)

The course covers the mathematical foundations of dynamical system safety analysis and modern algorithmic approaches for robotic decision making in safety-critical contexts. The focus is on safe robot learning, multiagent systems, and interaction with humans, paying special attention to uncertainty and the reality gap between mathematical models and the physical world.

ECE 535 - Machine Learning and Pattern Recognition

An introduction to the theoretical foundations of machine learning and pattern recognition. Topics include Bayesian pattern classification; parametric methods; nearest neighbor classification; Kernel methods; density estimation; VC theory; neural networks; stochastic approximation. Prerequisites: ELE525 or the permission of the instructor.

ECE 538 - Special Topics in Information Sciences and Systems

Advanced studies in selected areas in signal processing, communication and information theory, decision and control, and system theory. Emphasis on recent developments and current literature. Content varies from year to year according to the instructor's and students' interests.

ECE 538A - Special Topics in Information Sciences and Systems:

Advanced studies in selected areas in signal processing, communication and information theory, decision and control, and system theory. Emphasis on recent developments and current literature. Content varies from year to year according to the instructor's and students' interests.

ECE 538B - Special Topics in Information Sciences and Systems

Advanced studies in selected areas in signal processing, communication and information theory, decision and control, and system theory. Emphasis on recent developments and current literature. Content varies from year to year according to the instructor's and students' interests.

ECE 539 - Special Topics in Data and Information Science (also COS 512)

Advanced studies in selected areas in signal processing, communication and information theory, decision and control, and system theory. Emphasis on recent developments and current literature. Content varies from year to year according to the instructor's and students' interests.

ECE 539B - Special Topics in Information Sciences and Systems (also COS 597P)

Advanced studies in selected areas in signal processing, communication and information theory, decision and control, and system theory. Emphasis on recent developments and current literature. Content varies from year to year according to the instructor's and students' interests.

ECE 540 - Organic Materials for Photonics & Electronics

An introduction to organic materials with application to active electronic and photonic devices. Basic concepts and terminology in organic materials, and electronic and optical structure-property relationships are discussed. Charge transport, light emission and photoinduced charge transfer are examined. Finally, archetype organic devices as light emitting diodes, photodetectors and transistors are described.

ECE 541 - Quantum Material Spectroscopy (also MSE 554)

This course introduces students to state-of-the-art techniques in spectroscopy and imaging of solid-state quantum materials, including material systems for quantum information processing, topological and 2D materials, and strongly correlated systems. Lectures focus on both theoretical and practical understanding of the primary materials spectroscopy tools, complemented by a literature survey of current topics. Particular emphasis is placed on novel techniques such as nanoscale quantum sensing, low dimensional systems, spectroscopy of nanostructures, and understanding sources of decoherence in quantum information processing platforms.

ECE 542 - Solid State Physics II

This is a second-semester course with an emphasis on topics of interest to applied physicists and material scientists (e.g., semiconductors, optical properties and dielectrics.) It builds upon the material covered in ELE 441 and extends it to multiple areas. These include electronic structure of solids, electron dynamics and transport, semiconductors and impurity states, electron-electron, electron-phonon, and phonon-phonon interactions, anharmonic effects in crystals, dielectric properties of insulators, magnetism, superconductivity. Prerequisites of ELE 441 or PHY 405 or permission of instructor.

ECE 544 - Physics & Technology of Low-Dimensional Electronic Structures

A broad overview of materials science and physics of low-dimensional electronic structures will be presented. Emphasis is on the fabrication and physics of high-mobility carrier systems in modulation-doped structures. Examples include two-dimensional, one-dimensional (quantum wire), and zero-dimensional (quantum dot) systems.

ECE 545 - Electronic Devices

The physics and technology of electronic devices; junctions, junction transistors, and field-effect transistors; and MOS; and integrated circuits, and special microwave devices.

ECE 546 - Subwavelength Nanophotonics and Plasmonics

An introductory course for the first and second year graduate students to understand the theory and application of a new class of the photonic materials and devices, termed "subwavelength optical elements" (SOE) or "subwavelength photonics" (SWP), that are fundamentally different from bulk materials and devices. A striking property of the SOEs or SWPs, which have feature size smaller than the wavelength of light, is that it makes an optical system thinner than a paper.

ECE 547 - Selected Topics in Solid-State Electronics

One or more advanced topics in solid-state electronics. Contents vary from year to year. Recent topics have included: electronic properties of doped semiconductors, physics and technology of nanostructures, and organic materials for optical and electronic device application.

ECE 547B - Selected Topics in Solid-State Electronics (also MSE 557)

One or more advanced topics in solid-state electronics. Content may vary from year to year. Recent topics have included electronic properties of doped semiconductors, physics and technology of nanastructures, and organic materials for optical and electronic device application.

ECE 549 - Micro-Nanofabrication and Thin-Film Processing (also MSE 549)

This course investigates the technology and underlying science of micro-and nano-fabrication, which are the methods used to build billions of electronic and optoelectronic devices on a chip, as well as general small sensors and actuators generally referred to as micro-electromechanical systems (MEMS). The general approach involves deposition, modification, and patterning of layers less than one-micrometer thick, hence the generic term "thin-film" processing. Topics covered: film deposition and growth via physical and chemical vapor deposition, photolithography, pattern transfer, plasma-processing, ion-implantation, and vacuum science.

ECE 550 - Laser Spectroscopy: New Technologies and Applications

The course focuses on various aspects of laser spectroscopic sensing. Topics include physical principles of atomic and molecular spectroscopy, fundamentals of high resolution lasers spectroscopy, spectroscopic measurement techniques and instrumentation, laser sources and practical applications of spectroscopic sensing. Example applications of laser spectroscopy to chemical analysis and trace gas detection in fundamental science, industrial and environmental monitoring and medical diagnostics are discussed.

ECE 552 - Advanced Microscopy and Image Processing for Living Systems (also BNG 552)

For the past three decades have witnessed an explosion of new forms of optical microscopy that allows us to study living systems with unprecedented details. This course aims to cut through the confusion of the wide array of new imaging methods by offering both a unified theoretical framework and practical descriptions of the pros and cons of each. In addition, this course also explores advances in computational tools, especially recent advances in AI, for image visualization and quantification.

ECE 554 - Nonlinear Optics (also MSE 553)

An introduction to nonlinear optics, second-harmonic generation, parametric amplification and oscillation, electrooptic effects, third-order nonlinearities, phase-conjugate optics, photorefractive materials, and solitons.

ECE 557 - Solar Cells: Physics, Materials, and Technology (also ENE 557/MSE 558)

Photovoltaic materials and devices are discussed. Topics covered: solar flux distribution & spectra, photovoltaic parameters, loss mechanisms, Shockley-Queisser detailed balance approach, stability, light management, module design & various solar cell technologies, drawing distinctions between heterojunction & homojunction devices including crystalline Si and III-V, & thin film cells such as CIGS, CdTe, dye sensitized, & organic. In-depth treatment of organic solar cells including lab to fabricate & analyze an organic solar cell. We present methods to go beyond classical limits, such as intermediate band solar cells & multijunction devices.

ECE 558 - Photonics and Lightwave Communications

Introduction to fiber-optic communication systems. The basic principles of optical fibers, lasers, and detectors. In-depth analysis of noise and signal degradation in optical communication systems. The design and performance of fiber optic receivers and communication links. Introduction to optical amplifiers, dispersion management and soliton transmission. Technologies and architectures for wavelength division multiplexed long haul transmission systems and metropolitan area networks. In-depth lab project or paper on a topic of the student's choice with a class presentation.

ECE 559 - Photonic Systems

Rapid advances in photonic chip integration has enabled the development of increasingly sophisticated photonic systems for communications and computing. This course covers: Photonic system fundamentals, including device limitations, noise characteristics & performance requirements; Photonic system design & technology, based on off-the-shelf components & integrated silicon photonic platforms; Photonic systems applications, including communication networks & intra-chip interconnects, analog signal processors for cyber-physical systems & cryptography, and neuromorphic computing for nonlinear optimization & real-time signal analysis.

ECE 560 - Fundamentals of Nanophotonics (also MSE 556/PHY 565)

Introduction to theoretical techniques for understanding and modeling nanophotonic systems, emphasizing important algebraic properties of Maxwell's equations. Topics covered include Hermitian eigensystems, photonic crystals, Bloch's theorem, symmetry, band gaps, omnidirectional reflection, localization and mode confinement of guided and leaky modes. Techniques covered include Green's functions, density of states, numerical eigensolvers, finite-difference and boundary-element methods, coupled-mode theory, scattering formalism, and perturbation theory. The course explores application of these techniques to current research problems.

ECE 562 - Design of Very Large-Scale Integrated (VLSI) Systems

Analysis and design of digital integrated circuits using deep sub-micron CMOS technologies as well as emerging and post-CMOS technologies (Si finFETs, III-V, carbon). Emphasis on design, including synthesis, simulation, layout and post-layout verification. Analysis of energy, power, performance, area of logic-gates, interconnect and signaling structures.

ECE 567 - Advanced Solid-State Electron Physics (also PHY 567)

Electron localization in disordered structuresAnderson model and scaling theory of localization; correlated electron systemsHubbard model, Mott transition; metal-insulator transitions in correlated and disordered materials; quantum hall effectinteger and fractional; and quantum phase transitions.

ECE 568 - Implementations of Quantum Information (also QSE 568)

Course begins with an overview of DiVincenzo criteria for physical implementation of algorithms, then moves to consideration of leading contenders for a physical system, including superconducting qubits, electron spins in semiconductors and on liquid helium, and ion-trap-based quantum computers. A variety of possible quantum architectures will be considered. Weekly problem sets. Knowledge of quantum mechanics at the undergraduate level will be assumed.

ECE 569 - Quantum Information and Entanglement (also PHY 568)

Quantum information theory is a set of ideas and techniques that were developed in the context of quantum computation but now guide our thinking about a range of topics from black holes to semiconductors. This course introduces the central ideas of quantum information theory and surveys their applications. Topics include: quantum channels and open quantum systems; quantum circuits and tensor networks; a brief introduction to quantum algorithms; quantum error correction; and applications to sensing, many-body physics, black holes, etc.

ECE 571 - Deep Learning Networks

Various fundamental aspects of neurocomputing, including theory, modeling, algorithms, architectures, and applications. The course introduces various working network models and the corresponding learning algorithms. It then derives a unification of existing neural nets and basic building blocks of neural computers. The course explores the important future prospects on neural modeling and the potential impacts on conventional algorithm/architecture design as well as promising applications to various image/vision processing and pattern recognition problems.

ECE 572 - Architectures for Secure Computers and Smartphones

This course focuses on how to design secure processors, caches and systems for secure computers and smartphones. Topics include hardware-enhanced secure execution environments, secure cache architectures resilient to side and covert channel attacks, new processor designs for defeating speculative and timing attacks, solving security problems using machine/deep learning, smartphone security architecture, designing a deep learning engine for smartphone security and attacks on deep learning systems.

ECE 575 - Computer Architecture

Modern computer processor architecture. I/O Architecture. Instruction-set architecture and high-performance processor organization including pipelining and data and instruction parallelism. Cache, memory, and storage architectures. Multiprocessors and multicore processors. Coherent caches. Interconnection and network infrastructures.

ECE 580 - Advanced Topics in Computer Engineering

Selected research topics in computer engineering. Emphasis is on new results and emerging areas. (More detailed outlines are contained in the booklet <I>Course Outlines</I>, issued by the department each year.)

ECE 581 - Advanced Power Electronics (also ENE 581)

This course presents fundamental principles and design techniques of power electronics. Topics include 1) circuit elements: semiconductor devices, magnetic components, and filters; 2) circuit topology: canonical switching cells of power converters, inverters, rectifiers, dc-dc converters and ac-dc converters; 3) system modeling and control: small signal modeling, feedback control and system stability analysis; 4) design methods: gate drive, magnetic optimization, electromagnetic interference and thermal management. Numerous practical design examples are presented in class.

ECE 582 - Wireless and High Speed Integrated Circuits and Systems

This course aims to cover the fundamentals of the wireless and high-speed integrated circuits for future wireless technology. We cover analysis and design of high-speed and wireless ICs that enables modern wireless communication across device-circuits-system level abstractions. The understanding of these fundamental concepts prepares students for a wide range of advanced topics from circuits and systems for communication to emerging areas of sensing and biomedical electronics.

ECE 584 - Advanced Wireless Systems

This course focuses on advanced and merging topics in wireless systems. It covers millimeter-wave and terahertz communications, reconfigurable radio environments, wireless sensing, communications for robotics, multiple-input multiple output systems, visible light communication, 5G/6G, wireless security. The students develop skills to understand and critically evaluate research advances related to wireless systems. This course is half-lecture half-debate. We first cover the principles of wireless systems and then students read and debate over recent papers published at flagship conferences. The students learn to critically analyze research.

ECE 585 - Parallel Computation

The class reads seminal papers on different parallel programming models and computer architectures. In addition, we explore different parallel programming models via programming assignments. Finally the course culminates in a project where students create a research-grade experiment and write a full length conference-style paper. One of the goals of this class is to get students introduced to writing a complete conference style computer architecture/CS paper.

ECE 597 - Electrical Engineering Graduate Project Course

Under the direction of a faculty member, each student carries out a master's-level project and presents their results. For M. Eng. student, 597, fall term; 598 spring term.

ECE 598 - Electrical Engineering Master's Project

Under the direction of a faculty member, each student carries out a master's-level project and presents their results. For M. Eng. student, 597, fall term; 598 spring term.

EGR 501 - Responsible Conduct in Research: A Course on Ethics in Engineering (Half-term) (also ECE 501)

This course educates the graduate student of engineering in the responsible conduct of research. The lectures provide theoretical background information as well as case studies about ethics in day-to-day research situations, in publishing and peer-review, in student-advisor relationships, in collaborative research, as well as in the big picture and considerations of long-term impact. The students are provided with resources to consult in ethical questions. In small-group discussions in departmental and research field-specific precepts, the theoretical concepts are made relevant to the individual students situations.

ORF 570 - Special Topics in Statistics and Operations Research (also ECE 578)

Advanced topics in statistics and operations research or the investigation of problems of current interest.