EE 11SC:
Dream It, Build It!
The world is filled with electronic devices! There seem to be more and more all the time. Wouldn't it be cool to hack and build stuff? Bend electronics to your will? Cloud connect your own stuff? Dream It, Build It is a great place to start. Designed for folks with no experience, it will take you from zero to capable in short order. We will show you some of the worst kept secrets of how things are built and help you build stuff of your own. We'll start out with some basics about how to build things, how to measure things, how to hook stuff together and end up being able to make cloud-connected gizmos. [This is a SOPHOMORE COLLEGE course. Visit soco.stanford.edu for full details.]
Terms: Aut, Sum
| Units: 2
EE 14N:
Things about Stuff
Preference to freshmen. The stories behind disruptive inventions such as the telegraph, telephone, wireless, television, transistor, and chip are as important as the inventions themselves, for they elucidate broadly applicable scientific principles. Focus is on studying consumer devices; projects include building batteries, energy conversion devices and semiconductors from pocket change. Students may propose topics and projects of interest to them. The trajectory of the course is determined in large part by the students themselves.
Terms: Aut
| Units: 3
| UG Reqs: GER:DB-EngrAppSci, WAY-SMA
EE 21N:
Making at the nanometer scale: A journey into microchips
Have you ever wondered what is inside your phone and your computer? What physical events happen in between the time you press the 'search' button and the information shows up on the screen? In this course, we start with the classic paper by Richard Feynman, "There's Plenty of Room at the Bottom," which laid down a challenge to the nanotechnologists. Today's microchips are nanotechnology in action. Transistors are nanometer scale. We will introduce students to the tools of nanotechnologists and the basic elements of nanoscale science and engineering such as nanotubes, nanowires, nanoparticles, and self-assembly. We will visit nanotechnology laboratories to consolidate our learning, go into the Stanford Nanofabrication Facility (SNF), and do a four-week project on nanofabrication. Hands-on laboratory work will be introduced (e.g., lithography, seeing things at the nanoscale using electron microscopes). We will learn how to build transistors from scratch and test them.
Terms: Win
| Units: 3
| UG Reqs: GER:DB-EngrAppSci, WAY-SMA
EE 60N:
Man versus Nature: Coping with Disasters Using Space Technology (GEOPHYS 60N)
Preference to freshman. Natural hazards, earthquakes, volcanoes, floods, hurricanes, and fires, and how they affect people and society; great disasters such as asteroid impacts that periodically obliterate many species of life. Scientific issues, political and social consequences, costs of disaster mitigation, and how scientific knowledge affects policy. How spaceborne imaging technology makes it possible to respond quickly and mitigate consequences; how it is applied to natural disasters; and remote sensing data manipulation and analysis. GER:DB-EngrAppSci
Terms: Win
| Units: 4
| UG Reqs: GER:DB-EngrAppSci, WAY-SMA
EE 64:
Mechanical Prototyping for Electrical Engineers
This course will give non-mechanical engineers experience designing mechanical assemblies specifically for manufacture with readily accessible tools such as 3D printers and laser cutters. It will also teach students to debug their own mechanical designs, and interface them with other components (such as store bought parts). By the end of the quarter students will feel comfortable independently designing and manufacturing simple assemblies to solve issues in their projects, careers and daily lives. The course will meet in Lab64 (Room 134) on the first floor of Packard. Class website: ee64.stanford.edu
Terms: Win
| Units: 3
EE 84N:
From the Internet for People to the Internet of Things
Driven by the ubiquity of the Internet and advances in various technological fields, all aspects of the physical world in which we live are undergoing a major transformation. Underlying this transformation is a concept known as the Internet of Things (IoT) which envisions that every physical object in the world could be connected to the Internet. This concept is at the root of such developments as the fourth industrial revolution, precision agriculture, smart cities, intelligent transportation, home and building automation, precision medicine, etc. In this seminar, we trace back the origins of the IoT concept in terms of both the vision and pioneering work, identify the building blocks of an IoT system, and explore enabling technologies pertaining to the devices that get attached to things (possibly comprising sensors, actuators, and embedded systems) and the communications capabilities (RFID, Bluetooth, wireless sensor networks, Wi-Fi, Low Power WANs, cellular networks, vehicular communications). Students will apply the acquired knowledge to the design of IoT systems meeting specific objectives in various application domains.
Terms: Win
| Units: 3
EE 100:
The Electrical Engineering Profession
Lectures/discussions on topics of importance to the electrical engineering professional. Continuing education, professional societies, intellectual property and patents, ethics, entrepreneurial engineering, and engineering management.
Terms: Aut
| Units: 1
EE 101A:
Circuits I
Introduction to circuit modeling and analysis. Topics include creating the models of typical components in electronic circuits and simplifying non-linear models for restricted ranges of operation (small signal model); and using network theory to solve linear and non-linear circuits under static and dynamic operations. Prerequisite: MATH 20 (or equivalent) is required, and ENGR 40M is strongly recommended.
Terms: Win, Sum
| Units: 4
| UG Reqs: GER:DB-EngrAppSci, WAY-SMA
EE 102A:
Signals and Systems I
Concepts and tools for continuous- and discrete-time signal and system analysis with applications in signal processing, communications, and control. Mathematical representation of signals and systems. Linearity and time invariance. System impulse and step responses. System frequency response. Frequency-domain representations: Fourier series and Fourier transforms. Filtering and signal distortion. Time/frequency sampling and interpolation. Continuous-discrete-time signal conversion and quantization. Discrete-time signal processing. Prerequisites: MATH 53 or CME 102. EE 102A may be taken concurrently with either course, provided students have proficiency in complex numbers.
Terms: Win
| Units: 4
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR
EE 108:
Digital System Design
Digital circuit, logic, and system design. Digital representation of information. CMOS logic circuits. Combinational logic design. Logic building blocks, idioms, and structured design. Sequential logic design and timing analysis. Clocks and synchronization. Finite state machines. Microcode control. Digital system design. Control and datapath partitioning. Lab. *In Autumn, enrollment preference is given to EE majors. Any EE majors who must enroll in Autumn are invited to contact the instructor. Formerly EE 108A.
Terms: Aut, Win
| Units: 5
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA
Instructors: ;
Kapetanovic, Z. (PI);
Mitra, S. (PI);
Goel, A. (GP);
Iyer, N. (TA);
Manuelito, T. (TA);
Ragins, M. (TA);
Ravipati, S. (TA);
Rosenberg, R. (TA);
Salcedo, W. (TA);
Saldana, A. (TA);
Sokk, C. (TA)
EE 114:
Fundamentals of Analog Integrated Circuit Design (EE 214A)
Analysis and simulation of elementary transistor stages, current mirrors, supply- and temperature-independent bias, and reference circuits. Overview of integrated circuit technologies, circuit components, component variations and practical design paradigms. Differential circuits, frequency response, and feedback will also be covered. Performance evaluation using computer-aided design tools. Undergraduates must take EE 114 for 4 units. Prerequisite: 101B. GER:DB-EngrAppSci
Terms: Aut
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA
EE 118:
Introduction to Mechatronics (ME 210)
Technologies involved in mechatronics (intelligent electro-mechanical systems), and techniques to apply this technology to mecatronic system design. Topics include: electronics (A/D, D/A converters, op-amps, filters, power devices); software program design, event-driven programming; hardware and DC stepper motors, solenoids, and robust sensing. Large, open-ended team project. Prerequisites: ENGR 40, CS 106, or equivalents.
Terms: Win
| Units: 4
EE 133:
Analog Communications Design Laboratory (EE 233)
Design, testing, and applications of Radio Frequency (RF) electronics: Amplitude Modulation (AM), Frequency Modulation (FM) and concepts of Software Define Radio (SDR) systems. Practical aspects of circuit implementations are developed; labs involve building and characterization of subsystems as well as integration of a complete radio system and a final project. Total enrollment limited to 25 students, undergraduate and graduate levels. Prerequisite: EE101B. Undergraduate students enroll in EE133 for 4 units and Graduate students enroll in EE233 for 3 units. Recommended: EE114/214A.
Terms: Win
| Units: 3-4
EE 134:
Introduction to Photonics
Optics and photonics underpin the technologies that define our daily life, from communications and sensing to displays and imaging. This course provides an introduction to the principles that govern the generation, manipulation, and detection of light and will give students hands-on lab experience applying these principles to analyze and design working optical systems. The concepts we will cover form the basis for many systems in biology, optoelectronics, and telecommunications and build a foundation for further learning in photonics and optoelectronics. Connecting theory to observation and application is a major theme for the course. Prerequisite: EE 102A and one of the following: EE 42, Physics 43, or Physics 63.
Terms: Win
| Units: 4
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA
EE 156:
Board Level Design (EE 256)
The ability to rapidly create board level electronics at prototype and short run volumes is enabling; Board Level Design teaches how to do this. This course focuses on applying circuit design concepts to rapidly create electronics to augment existing research instruments, explore and reduce technical risk, and provide engineering samples for evaluation. Students will send several PCBs for fabrication during the Quarter. The PCBs will be "brought-up" and tested to confirm functionality and performance to specification. Undergraduate EE majors will gain deeper exposure to circuits and planar electromagnetics. Students must enroll in EE 156 for 4 units and EE 256 for 3 units. Prerequisites: EE 42, EE 101A, and EE 108 or consent of instructor.
Terms: Aut
| Units: 3-4
EE 160A:
Principles of Robot Autonomy I (AA 174A, CS 137A)
Basic principles for endowing mobile autonomous robots with perception, planning, and decision-making capabilities. Algorithmic approaches for robot perception, localization, and simultaneous localization and mapping; control of non-linear systems, learning-based control, and robot motion planning; introduction to methodologies for reasoning under uncertainty, e.g., (partially observable) Markov decision processes. Extensive use of the Robot Operating System (ROS) for demonstrations and hands-on activities. Prerequisites: CS 106A or equivalent, CME 100 or equivalent (for linear algebra), and CME 106 or equivalent (for probability theory).
Terms: Aut
| Units: 3-4
EE 179:
Analog and Digital Communication Systems
This course covers the fundamental principles underlying the analysis, design and optimization of analog and digital communication systems. Design examples will be taken from the most prevalent communication systems today: cell phones, Wifi, radio and TV broadcasting, satellites, and computer networks. Analysis techniques based on Fourier transforms and energy/power spectral density will be developed. Mathematical models for random variables and random (noise) signals will be presented, which are used to characterize filtering and modulation of random noise. These techniques will then be used to design analog (AM and FM) and digital (PSK and FSK) communication systems and determine their performance over channels with noise and interference. Prerequisite: 102A.
Terms: Win
| Units: 3
EE 180:
Digital Systems Architecture
The design of processor-based digital systems. Instruction sets, addressing modes, data types. Assembly language programming, low-level data structures, introduction to operating systems and compilers. Processor microarchitecture, microprogramming, pipelining. Memory systems and caches. Input/output, interrupts, buses and DMA. System design implementation alternatives, software/hardware tradeoffs. Labs involve the design of processor subsystems and processor-based embedded systems. Formerly EE 108B. Prerequisite: one of CS107 or CS 107E (required) and EE108 (recommended but not required).
Terms: Win
| Units: 4
| UG Reqs: GER:DB-EngrAppSci, WAY-SMA
EE 190:
Special Studies or Projects in Electrical Engineering
Independent work under the direction of a faculty member. Individual or team activities involve lab experimentation, design of devices or systems, or directed reading. Course may be repeated for credit.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
Instructors: ;
Arbabian, A. (PI);
Bambos, N. (PI);
Boahen, K. (PI);
Boneh, D. (PI);
Bowden, A. (PI);
Boyd, S. (PI);
Cioffi, J. (PI);
Clark, S. (PI);
Dally, B. (PI);
Duchi, J. (PI);
El Gamal, A. (PI);
Emami-Naeini, A. (PI);
Engler, D. (PI);
Fan, J. (PI);
Fan, S. (PI);
Fraser-Smith, A. (PI);
Gibbons, J. (PI);
Giovangrandi, L. (PI);
Girod, B. (PI);
Hanrahan, P. (PI);
Hennessy, J. (PI);
Hesselink, L. (PI);
Horowitz, M. (PI);
Howe, R. (PI);
Inan, U. (PI);
Kahn, J. (PI);
Kapetanovic, Z. (PI);
Katti, S. (PI);
Kazovsky, L. (PI);
Khuri-Yakub, B. (PI);
Kovacs, G. (PI);
Kozyrakis, C. (PI);
Lall, S. (PI);
Lee, T. (PI);
Levis, P. (PI);
Levoy, M. (PI);
McKeown, N. (PI);
Miller, D. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Murmann, B. (PI);
Nishi, Y. (PI);
Nishimura, D. (PI);
Olukotun, O. (PI);
Osgood, B. (PI);
Paulraj, A. (PI);
Pauly, J. (PI);
Pease, R. (PI);
Pianetta, P. (PI);
Plummer, J. (PI);
Poon, A. (PI);
Pop, E. (PI);
Prabhakar, B. (PI);
Rivas-Davila, J. (PI);
Rosenblum, M. (PI);
Saraswat, K. (PI);
Schroeder, D. (PI);
Senesky, D. (PI);
Soh, H. (PI);
Solgaard, O. (PI);
Song, S. (PI);
Thompson, N. (PI);
Thrun, S. (PI);
Tobagi, F. (PI);
Van Roy, B. (PI);
Vuckovic, J. (PI);
Wang, S. (PI);
Weissman, T. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Widrow, B. (PI);
Wong, H. (PI);
Wong, S. (PI);
Wooley, B. (PI);
Wootters, M. (PI);
Yamamoto, Y. (PI);
Zebker, H. (PI);
Rutherford, E. (GP)
EE 191:
Special Studies and Reports in Electrical Engineering
Independent work under the direction of a faculty member given for a letter grade only. If a letter grade given on the basis of required written report or examination is not appropriate, enroll in 190. Course may be repeated for credit.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
Instructors: ;
Arbabian, A. (PI);
Bambos, N. (PI);
Boahen, K. (PI);
Boneh, D. (PI);
Bowden, A. (PI);
Boyd, S. (PI);
Cioffi, J. (PI);
Clark, S. (PI);
Congreve, D. (PI);
Dally, B. (PI);
Duchi, J. (PI);
El Gamal, A. (PI);
Emami-Naeini, A. (PI);
Engler, D. (PI);
Fan, J. (PI);
Fan, S. (PI);
Fraser-Smith, A. (PI);
Gibbons, J. (PI);
Girod, B. (PI);
Hanrahan, P. (PI);
Hennessy, J. (PI);
Hesselink, L. (PI);
Horowitz, M. (PI);
Howe, R. (PI);
Inan, U. (PI);
Kahn, J. (PI);
Kapetanovic, Z. (PI);
Katti, S. (PI);
Kazovsky, L. (PI);
Khuri-Yakub, B. (PI);
Kozyrakis, C. (PI);
Lall, S. (PI);
Lee, T. (PI);
Levin, C. (PI);
Levis, P. (PI);
McKeown, N. (PI);
Miller, D. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Moslehi, M. (PI);
Murmann, B. (PI);
Nishi, Y. (PI);
Nishimura, D. (PI);
Olukotun, O. (PI);
Osgood, B. (PI);
Pauly, J. (PI);
Pease, R. (PI);
Pianetta, P. (PI);
Plummer, J. (PI);
Poon, A. (PI);
Pop, E. (PI);
Prabhakar, B. (PI);
Raina, P. (PI);
Rivas-Davila, J. (PI);
Rosenblum, M. (PI);
Saraswat, K. (PI);
Schroeder, D. (PI);
Senesky, D. (PI);
Soh, H. (PI);
Solgaard, O. (PI);
Song, S. (PI);
Tobagi, F. (PI);
Van Roy, B. (PI);
Vuckovic, J. (PI);
Wang, S. (PI);
Weissman, T. (PI);
Widom, J. (PI);
Widrow, B. (PI);
Wong, H. (PI);
Wong, S. (PI);
Wootters, M. (PI);
Zebker, H. (PI);
Rutherford, E. (GP)
EE 191A:
Special Studies and Reports in Electrical Engineering
EE191A is part of the Accelerated Calculus for Engineers program. Independent work under the direction of a faculty member given for a letter grade only. EE 191A counts as a Math one unit seminar course: it is this unit that constitutes the ACE program.
Terms: Aut, Win, Spr
| Units: 1
EE 191W:
Special Studies and Reports in Electrical Engineering (WIM)
WIM-version of EE 191. For EE students using special studies (e.g., honors project, independent research project) to satisfy the writing-in-major requirement. A written report that has gone through revision with an adviser is required. An adviser from the Technical Communication Program is recommended.
Terms: Aut, Win, Spr, Sum
| Units: 3-10
Instructors: ;
Arbabian, A. (PI);
Bambos, N. (PI);
Boahen, K. (PI);
Bowden, A. (PI);
Boyd, S. (PI);
Clark, S. (PI);
Congreve, D. (PI);
Duchi, J. (PI);
El Gamal, A. (PI);
Fan, J. (PI);
Fan, S. (PI);
Fraser-Smith, A. (PI);
Gibbons, J. (PI);
Girod, B. (PI);
Hanrahan, P. (PI);
Hennessy, J. (PI);
Hesselink, L. (PI);
Horowitz, M. (PI);
Howe, R. (PI);
Kahn, J. (PI);
Kapetanovic, Z. (PI);
Katti, S. (PI);
Kazovsky, L. (PI);
Khuri-Yakub, B. (PI);
Kovacs, G. (PI);
Kozyrakis, C. (PI);
Lee, T. (PI);
Levin, C. (PI);
Levis, P. (PI);
Levoy, M. (PI);
McKeown, N. (PI);
Miller, D. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Murmann, B. (PI);
Nishimura, D. (PI);
Olukotun, O. (PI);
Osgood, B. (PI);
Ozgur, A. (PI);
Pauly, J. (PI);
Pianetta, P. (PI);
Plummer, J. (PI);
Poon, A. (PI);
Pop, E. (PI);
Prabhakar, B. (PI);
Rivas-Davila, J. (PI);
Saraswat, K. (PI);
Schroeder, D. (PI);
Senesky, D. (PI);
Soh, H. (PI);
Solgaard, O. (PI);
Song, S. (PI);
Tobagi, F. (PI);
Van Roy, B. (PI);
Vuckovic, J. (PI);
Wang, S. (PI);
Weissman, T. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Widrow, B. (PI);
Wong, H. (PI);
Wong, S. (PI);
Wootters, M. (PI);
Zebker, H. (PI);
Rutherford, E. (GP)
EE 195:
Electrical Engineering Instruction
Students receive training from faculty or graduate student mentors to prepare them to assist in instruction of Electrical Engineering courses. The specific training and units of credit received are to be defined in consultation with one of the official instructors of EE 195. Note that University regulations prohibit students from being paid for the training while receiving academic credit for it. Enrollment limited.
Terms: Aut, Win, Spr
| Units: 1-3
EE 205:
Product Management for Electrical Engineers and Computer Scientists
Successful products are the highest impact contribution anyone can make in product development. Students will learn to build successful products using fundamental concepts in Product Management. These include understanding customers, their job to be done, Identifying new product opportunities, and defining what to build that is technically feasible, valuable to the customer, and easy to use The course has two components, Product Management Project with corporate partners, and case-based classroom discussion of PM concepts and application. Prerequisite: Students must be currently enrolled in a MS or PhD engineering degree program.
Terms: Aut
| Units: 3
EE 212:
Integrated Circuit Fabrication Processes
For students interested in the physical bases and practical methods of silicon VLSI chip fabrication, or the impact of technology on device and circuit design, or intending to pursue doctoral research involving the use of Stanford's Nanofabrication laboratory. Process simulators illustrate concepts. Topics: principles of integrated circuit fabrication processes, physical and chemical models for crystal growth, oxidation, ion implantation, etching, deposition, lithography, and back-end processing. Required for 410.
Terms: Aut
| Units: 3
EE 214A:
Fundamentals of Analog Integrated Circuit Design (EE 114)
Analysis and simulation of elementary transistor stages, current mirrors, supply- and temperature-independent bias, and reference circuits. Overview of integrated circuit technologies, circuit components, component variations and practical design paradigms. Differential circuits, frequency response, and feedback will also be covered. Performance evaluation using computer-aided design tools. Undergraduates must take EE 114 for 4 units. Prerequisite: 101B. GER:DB-EngrAppSci
Terms: Aut
| Units: 3-4
EE 214B:
Advanced Integrated Circuit Design
Analysis and design of analog and digital integrated circuits in advanced CMOS technology. Emphasis on compact modeling of performance limiting aspects and intuitive approaches to design. Analytical treatment of noise; analog circuit sizing using the transconductance to current ratio; analysis and design of feedback circuits. Delay analysis of digital logic gates; decoder design using logical effort. CMOS image sensors are used as a motivating application example. Prerequisites: EE114/214A.
Terms: Win
| Units: 3
EE 216:
Principles and Models of Semiconductor Devices
Carrier generation, transport, recombination, and storage in semiconductors. Physical principles of operation of the p-n junction, heterojunction, metal semiconductor contact, bipolar junction transistor, MOS capacitor, MOS and junction field-effect transistors, and related optoelectronic devices such as CCDs, solar cells, LEDs, and detectors. First-order device models that reflect physical principles and are useful for integrated-circuit analysis and design. Prerequisite: 116 or equivalent.
Terms: Aut
| Units: 3
EE 218:
Power Semiconductor Devices and Technology
This course starts by covering the device physics and technology of current silicon power semiconductor devices including power MOSFETs, IGBTs, and Thyristors. Wide bandgap materials, especially GaN and SiC are potential replacements for Si power devices because of their fundamentally better properties. This course explores what is possible in these new materials, and what the remaining challenges are for wide bandgap materials to find widespread market acceptance in power applications. Future clean, renewable energy systems and high efficiency power control systems will critically depend on the higher performance devices possible in these new materials. Prerequisites: EE 116 or equivalent.
Terms: Win
| Units: 3
EE 222:
Applied Quantum Mechanics I (MATSCI 201)
Emphasis is on applications in modern devices and systems. Topics include: Schr¿dinger's equation, eigenfunctions and eigenvalues, solutions of simple problems including quantum wells and tunneling, quantum harmonic oscillator, coherent states, operator approach to quantum mechanics, Dirac notation, angular momentum, hydrogen atom, calculation techniques including matrix diagonalization, perturbation theory, variational method, and time-dependent perturbation theory with applications to optical absorption, nonlinear optical coefficients, and Fermi's golden rule. Prerequisites: MATH 52 and 53, one of EE 65, ENGR 65, PHYSICS 71 (formerly PHYSICS 65), PHYSICS 70.
Terms: Aut
| Units: 3
EE 223:
Applied Quantum Mechanics II
Continuation of 222, including more advanced topics: quantum mechanics of crystalline materials, methods for one-dimensional problems, spin, systems of identical particles (bosons and fermions), introductory quantum optics (electromagnetic field quantization, coherent states), fermion annihilation and creation operators, interaction of different kinds of particles (spontaneous emission, optical absorption, and stimulated emission). Quantum information and interpretation of quantum mechanics. Other topics in electronics, optoelectronics, optics, and quantum information science. Prerequisite: 222.
Terms: Win
| Units: 3
EE 225:
Biochips and Medical Imaging (MATSCI 225, SBIO 225)
The course covers state-of-the-art and emerging bio-sensors, bio-chips, imaging modalities, and nano-therapies which will be studied in the context of human physiology including the nervous system, circulatory system and immune system. Medical diagnostics will be divided into bio-chips (in-vitro diagnostics) and medical and molecular imaging (in-vivo imaging). In-depth discussion on cancer and cardiovascular diseases and the role of diagnostics and nano-therapies.
Terms: Win
| Units: 3
EE 227:
Robot Perception: Hardware, Algorithm, and Application (CS 227A)
Robot Perception is the cornerstone of modern robotics, enabling machines to interpret, understand, and respond to an array of sensory information they encounter. In the course, students will study the basic principles of typical sensor hardware on a robotics system (e.g., vision, tactile, and acoustic sensors), the algorithms that process the raw sensory data, and make actionable decisions from that information. Over the course of the semester, students will incrementally build their own vision-based robotics system in simulation via a series of homework coding assignments. Students enrolling 4 units will be required to submit an additional final written report. Prerequisites: This course requires programming experience in python as well as basic knowledge of linear algebra. Most of the required mathematical concepts will be reviewed, but it will be assumed that students have strong programming skills. All the homework requires extensive programming. Previous knowledge of robotics, machine learning or computer vision would be helpful but is not absolutely required.
Terms: Win
| Units: 3-4
EE 233:
Analog Communications Design Laboratory (EE 133)
Design, testing, and applications of Radio Frequency (RF) electronics: Amplitude Modulation (AM), Frequency Modulation (FM) and concepts of Software Define Radio (SDR) systems. Practical aspects of circuit implementations are developed; labs involve building and characterization of subsystems as well as integration of a complete radio system and a final project. Total enrollment limited to 25 students, undergraduate and graduate levels. Prerequisite: EE101B. Undergraduate students enroll in EE133 for 4 units and Graduate students enroll in EE233 for 3 units. Recommended: EE114/214A.
Terms: Win
| Units: 3-4
EE 235A:
Analytical Methods in Biotechnology I
This course provides fundamental principles underlying important analytical techniques used in modern biotechnology. The course comprises of lectures and hands-on laboratory experiments. Students will learn the core principles for designing, implementing and analyzing central experimental methods including polymerase chain reaction (PCR), electrophoresis, immunoassays, and high-throughput sequencing. The overall goal of the course is to enable engineering students with little or no background in molecular biology to transition into research in the field of biomedicine.
Terms: Win
| Units: 3
EE 236A:
Modern Optics
Geometrical optics; lens analysis and design, aberrations, optical instruments, radiometry. ray matrices. Wave nature of light; polarization, plane waves at interfaces and in media with varying refractive index, diffraction, Fourier Optics, Gaussian beams. Interference; single-beam interferometers (Fabry-Perot), multiple-beam interferometers (Michelson, Mach-Zehnder). Prerequisites: EE 142 or familiarity with electromagnetism and plane waves.
Terms: Aut
| Units: 3
EE 236B:
Guided Waves
Maxwell's equations, constitutive relations. Kramers-Kronig relations. Modes in waveguides: slab, rectangular, circular. Photonic crystals, surface plasmon modes. General properties of waveguide modes: orthogonality, phase and group indices, group velocity dispersion. Chirped pulse propagation in dispersive media and its connection to Gaussian beam propagation. Time lens. Waveguide technologies: glass, silicon, III-V semiconductor, metallic. Waveguide devices: fibers, lasers, modulators, arrayed waveguide gratings. Scattering matrix description of passive optical devices, and constraints from energy conservation, time-reversal symmetry and reciprocity. Mode coupling, directional couplers, distributed-feedback structures. Resonators from scattering matrix and input-output perspective. Micro-ring resonators. Prerequisites: EE 236A and EE 242 or familiarity with differential form of Maxwell's equations.
Terms: Win
| Units: 3
EE 242:
Electromagnetic Waves
This course will provide an advanced treatment of electromagnetic waves in free space and media. The first part of the course will cover reflection, refraction, resonators, photonic crystals, and waveguides. The second part will cover finite-difference time-domain (FDTD) computation and introduce students to commercial FDTD software. The third part will focus on an analysis of EM waves in matter. The fourth part will cover potentials, Green's functions, far-field radiation, near-field radiation, antennas, and phased arrays. In lieu of a final exam, students will perform a group project demonstrating theoretical and application proficiency in a topic of their choosing. Homeworks and the final project will tie into real world applications of electromagnetics and utilize scientific computing (Matlab, Mathematica, or Python). Prerequisites: EE 142 or PHYSICS 120, and prior programming experience (Matlab or other language at level of CS 106A or higher).
Terms: Aut
| Units: 3
EE 251:
High-Frequency Circuit Design Laboratory
Students will study the theory of operation of instruments such as the time-domain reflectometer, sampling oscilloscope and vector network analyzer. They will build on that theoretical foundation by designing, constructing and characterizing numerous wireless building blocks in the upper-UHF range (e.g., up to about 500MHz), in a running series of laboratory exercises that conclude in a final project. Examples include impedance-matching and coupling structures, filters, narrowband and broadband amplifiers, mixers/modulators, and voltage-controlled oscillators. Prerequisite: EE 114 or EE 214A.
Terms: Win
| Units: 3
EE 256:
Board Level Design (EE 156)
The ability to rapidly create board level electronics at prototype and short run volumes is enabling; Board Level Design teaches how to do this. This course focuses on applying circuit design concepts to rapidly create electronics to augment existing research instruments, explore and reduce technical risk, and provide engineering samples for evaluation. Students will send several PCBs for fabrication during the Quarter. The PCBs will be "brought-up" and tested to confirm functionality and performance to specification. Undergraduate EE majors will gain deeper exposure to circuits and planar electromagnetics. Students must enroll in EE 156 for 4 units and EE 256 for 3 units. Prerequisites: EE 42, EE 101A, and EE 108 or consent of instructor.
Terms: Aut
| Units: 3-4
EE 260A:
Principles of Robot Autonomy I (AA 274A, CS 237A)
Basic principles for endowing mobile autonomous robots with perception, planning, and decision-making capabilities. Algorithmic approaches for robot perception, localization, and simultaneous localization and mapping; control of non-linear systems, learning-based control, and robot motion planning; introduction to methodologies for reasoning under uncertainty, e.g., (partially observable) Markov decision processes. Extensive use of the Robot Operating System (ROS) for demonstrations and hands-on activities. Prerequisites: CS 106A or equivalent, CME 100 or equivalent (for linear algebra), and CME 106 or equivalent (for probability theory).
Terms: Aut
| Units: 3
EE 261:
The Fourier Transform and Its Applications
The Fourier transform as a tool for solving physical problems. Fourier series, the Fourier transform of continuous and discrete signals and its properties. The Dirac delta, distributions, and generalized transforms. Convolutions and correlations and applications; probability distributions, sampling theory, filters, and analysis of linear systems. The discrete Fourier transform and the FFT algorithm. Multidimensional Fourier transform and use in imaging. Further applications to optics, crystallography. Emphasis is on relating the theoretical principles to solving practical engineering and science problems. Prerequisites: Math through ODEs, basic linear algebra, Comfort with sums and discrete signals, Fourier series at the level of 102A
Terms: Win, Sum
| Units: 3
EE 263:
Introduction to Linear Dynamical Systems (CME 263)
Applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. Topics: least-squares approximations of over-determined equations, and least-norm solutions of underdetermined equations. Symmetric matrices, matrix norm, and singular-value decomposition. Eigenvalues, left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multi-input/multi-output systems, impulse and step matrices; convolution and transfer-matrix descriptions. Control, reachability, and state transfer; observability and least-squares state estimation. Prerequisites: Linear algebra and matrices as in ENGR 108 or MATH 104; ordinary differential equations and Laplace transforms as in EE 102B or CME 102.
Terms: Aut, Sum
| Units: 3
EE 264:
Digital Signal Processing
Digital signal processing (DSP) techniques and design of DSP applications. Topics include: discrete-time random signals; sampling and multi-rate systems; oversampling and quantization in A-to-D conversion; properties of LTI systems; quantization in fixed-point implementations of filters; digital filter design; discrete Fourier Transform and FFT; spectrum analysis using the DFT; parametric signal modeling and adaptive filtering. The course also covers applications of DSP in areas such as speech, audio and communication systems. The optional lab section (Section 02) provides a hands-on opportunity to explore the application of DSP theory to practical real-time applications in an embedded processing platform. See ee264.stanford.edu for more information. Register in Section 02 to take the lab. Undergraduate students taking the lab should register for 4 units to meet the EE design requirement. The optional lab section is not available to remote SCPD students. Prerequisites: EE 102A and EE 102B or equivalent, basic programming skills (Matlab and C++)
Terms: Win
| Units: 3-4
EE 264W:
Digital Signal Processing (WIM)
Writing in the Major (WIM) version of the 4-unit EE 264 theory + lab course. Digital signal processing (DSP) techniques and design of DSP applications. Topics include: discrete-time random signals; sampling and multi-rate systems; oversampling and quantization in A-to-D conversion; properties of LTI systems; quantization in fixed-point implementations of filters; digital filter design; discrete Fourier Transform and FFT; spectrum analysis using the DFT; parametric signal modeling and adaptive filtering. The course also covers applications of DSP in areas such as speech, audio and communication systems. The lab component provides a hands-on opportunity to explore the application of DSP theory to practical real-time applications in an embedded processing platform. See ee264.stanford.edu for more information. Prerequisites: EE 102A and EE 102B or equivalent, basic programming skills (Matlab and C++)
Terms: Win
| Units: 5
EE 268:
The Engineering Economics of Electricity Markets (ECON 261)
This course presents the power system engineering and economic concepts necessary to understand the costs and benefits of transitioning to a low carbon electricity supply industry. The technical characteristics of generation units and transmission and distribution networks as well as the mechanisms used to operate the electricity supply industries will be studied. The fundamental economics of wholesale markets and how intermittent renewables impact the price and quantity of physical and financial products traded in these markets (e.g., energy, capacity, ancillary services, and financial contracts) will be analyzed. Long-term resource adequacy mechanisms will be introduced and their properties analyzed. The role of both short-duration and seasonal energy storage will be analyzed. Mechanisms for determining the engineering and economic need for transmission network expansions in a wholesale market will be discussed. The impact of distributed versus grid-scale generation on the performance of electricity supply industries will be studied. A detailed treatment of electricity retailing will focus on the importance of active demand-side participation in a low carbon energy sector. This course uses knowledge of probability at the level of Stats 116, optimization at the level of MS&E 111, statistical analysis at the level of Economics 102B, microeconomics at the level of Economics 51 and computer programming in R.
Terms: Aut
| Units: 3
EE 269:
Signal Processing for Machine Learning
This course will introduce you to fundamental signal processing concepts and tools needed to apply machine learning to discrete signals. You will learn about commonly used techniques for capturing, processing, manipulating, learning and classifying signals. The topics include: mathematical models for discrete-time signals, vector spaces, Fourier analysis, time-frequency analysis, Z-transforms and filters, signal classification and prediction, basic image processing, compressed sensing and deep learning. This class will culminate in a final project. Prerequisites: EE 102A and EE 102B or equivalent, basic programming skills (Matlab). ENGR 108 and EE 178 are recommended.
Terms: Aut
| Units: 3
EE 271:
Introduction to VLSI Systems
Provides a quick introduction to MOS transistors and IC fabrication and then creates abstractions to allow you to create and reason about complex digital systems. It uses a switch resistor model of a transistor, uses it to model gates, and then shows how gates and physical layout can be synthesized from Verilog or SystemVerilog descriptions. Most of the class will be spent on providing techniques to create designs that can be validated, are low power, provide good performance, and can be completed in finite time. Prerequisites: 101A and 108; familiarity with transistors, logic design, Verilog and digital system organization
Terms: Aut
| Units: 3
EE 272:
Design Projects in VLSI Systems I
This course will introduce you to mixed signal design and the electronic design automation (EDA) tools used for it. Working in teams, you will create a chip with a digital deep neural network (DNN) accelerator and a small analog block using a modern design flow and EDA tools. The project involves writing a synthesizable C++ and a Verilog model of your chip, creating a testing/debug strategy for your chip, wrapping custom layout to fit into a standard cell system, using synthesis and place and route tools to create the layout of your chip, and understanding all the weird stuff you need to do to tape-out a chip. Useful for anyone who will build a chip in their Ph.D. Pre-requisites: EE271 and experience in digital/analog circuit design.
Terms: Win
| Units: 3-4
EE 274:
Data Compression: Theory and Applications
The course focuses on the theory and algorithms underlying modern data compression. The first part of the course introduces techniques for entropy coding and for lossless compression. The second part covers lossy compression including techniques for multimedia compression. The last part of the course will cover advanced theoretical topics and applications, such as neural network based compression, distributed compression, and computation over compressed data. Prerequisites: basic probability and programming background (EE178, CS106B or equivalent), a course in signals and systems (EE102A), or instructor's permission.
Terms: Aut
| Units: 3
EE 276:
Information Theory
(Formerly EE 376A.) Information theory was invented as a mathematical theory for communication but has subsequently found a broad range of applications. We study how to measure, represent, and communicate information effectively: from the foundational concepts of entropy and mutual information to the fundamental role they play in data representation, communication, inference, practical compression and error correction. As time allows, we cover relations and applications to other areas such as probability, statistics, learning and genomics. Prerequisite: a first undergraduate course in probability.
Terms: Win
| Units: 3
EE 277:
Bandit Learning: Behaviors and Applications (MS&E 237A)
The subject of reinforcement learning addresses the design of agents that improve decisions over time while operating within complex and uncertain environments. This first course of the sequence restricts attention to the special case of bandit learning, which focuses on environments in which all consequences of an action are realized immediately. This course covers desired agent behaviors and principled scalable approaches to realizing such behavior. Topics include learning from trial and error, exploration, contextualization, generalization, and representation learning. Motivating examples will be drawn from recommendation systems, crowdsourcing, education, and generative artificial intelligence. Homework assignments primarily involve programming exercises carried out in Colab, using the python programming language and standard libraries for numerical computation and machine learning. Prerequisites: programming (e.g., CS106B), probability (e.g., MS&E 121, EE 178 or CS 109), machine learning (e.g., EE 104/ CME 107, MS&E 226 or CS 229).
Terms: Aut
| Units: 3
EE 278:
Probability and Statistical Inference
Many engineering applications require efficient methods to process, analyze, and infer signals, data and models of interest that are best described probabilistically. Building on a first course in probability (such as EE178 or equivalent), this course introduces more advanced topics in probability such as concentration inequalities, random vectors and random processes, and explores their applications in statistics, machine learning and signal processing. Specific applications include hypothesis testing and classification; dimensionality reduction and generalization in machine learning, minimum mean square error estimation and Kalman filtering. Prerequisites: EE178 or equivalent
Terms: Aut
| Units: 3
EE 284:
Introduction to Computer Networks
Structure and components of computer networks; functions and services; packet switching; layered architectures; OSI reference model; physical layer; data link layer; error control; window flow control; media access control protocols used in local area networks (Ethernet, Token Ring, FDDI) and satellite networks; network layer (datagram service, virtual circuit service, routing, congestion control, Internet Protocol); transport layer (UDP, TCP); application layer.
Terms: Aut
| Units: 3
EE 285:
Embedded Systems Workshop (CS 241)
Project-centric building hardware and software for embedded computing systems. This year the course projects are on a large interactive light sculpture to be installed in Packard. Syllabus topics will be determined by the needs of the enrolled students and projects. Examples of topics include: interrupts and concurrent programming, mechanical control, state-based programming models, signaling and frequency response, mechanical design, power budgets, software, firmware, and PCB design. Interested students can help lead community workshops to begin building the installation. Prerequisites: one of CS107, EE101A, EE108, ME80.
Terms: Win
| Units: 3
| Repeatable
3 times
(up to 9 units total)
EE 290A:
Curricular Practical Training for Electrical Engineers
For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, EE MS and PhD students who have received a Satisfactory ("S") grade in EE290A; for 290C, EE PhD degree candidacy and an "S" grade in EE 290B; for 290D, EE PhD degree candidacy, an "S" grade in EE 290C and instructor consent.
Terms: Aut, Win, Spr, Sum
| Units: 1
EE 290B:
Curricular Practical Training for Electrical Engineers
For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, EE MS and PhD students who have received a Satisfactory ("S") grade in EE290A; for 290C, EE PhD degree candidacy and an "S" grade in EE 290B; for 290D, EE PhD degree candidacy, an "S" grade in EE 290C and instructor consent.
Terms: Aut, Win, Spr, Sum
| Units: 1
EE 290C:
Curricular Practical Training for Electrical Engineers
For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, EE MS and PhD students who have received a Satisfactory ("S") grade in EE290A; for 290C, EE PhD degree candidacy and an "S" grade in EE 290B; for 290D, EE PhD degree candidacy, an "S" grade in EE 290C and instructor consent.
Terms: Aut, Win, Spr, Sum
| Units: 1
EE 290D:
Curricular Practical Training for Electrical Engineers
For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, EE MS and PhD students who have received a Satisfactory ("S") grade in EE290A; for 290C, EE PhD degree candidacy and an "S" grade in EE 290B; for 290D, EE PhD degree candidacy, an "S" grade in EE 290C and instructor consent.
Terms: Aut, Win, Spr, Sum
| Units: 1
EE 290E:
Curricular Practical Training for Electrical Engineers
For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, EE MS and PhD students who have received a Satisfactory ("S") grade in EE290A; for 290C, EE PhD degree candidacy and an "S" grade in EE 290B; for 290D, EE PhD degree candidacy, an "S" grade in EE 290C and instructor consent; for 290E, EE PhD degree candidacy, an "S" grade in EE 290D and instructor consent.
Terms: Aut, Win, Spr, Sum
| Units: 1
EE 290F:
Curricular Practical Training for Electrical Engineers
For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: EE PhD degree candidacy, an "S" grade in EE 290E and instructor consent.
Terms: Aut, Win, Spr, Sum
| Units: 1
EE 290G:
Curricular Practical Training for Electrical Engineers
For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: EE PhD degree candidacy, an "S" grade in EE 290F and instructor consent.
Terms: Aut, Win, Spr, Sum
| Units: 1
EE 290H:
Curricular Practical Training for Electrical Engineers
For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: EE PhD degree candidacy, an "S" grade in EE 290G and instructor consent.
Terms: Win
| Units: 1
EE 292E:
Seminar Series for Image Systems Engineering
Seminar. For engineering students interested in camera and display engineering, computer vision, and computational imaging. Speakers include Stanford faculty and research scientists as well as industry professionals, mostly from consumer electronics companies. This course is open to graduate and professional students only, or with instructor consent.
Terms: Aut, Win, Spr
| Units: 1
| Repeatable
for credit
(up to 99 units total)
EE 292H:
Engineering, Entrepreneurship & Climate Change
The purpose of this seminar series course is to help students and professionals develop the tools to apply the engineering and entrepreneurial mindset to problems that stem from climate change, in order to consider and evaluate possible stabilizing, remedial and adaptive approaches. This course is not a crash course on climate change or policy. Instead we will focus on learning about and discussing the climate problems that seem most tractable to these approaches. Each week Dr. Field and/or a guest speaker will lead a short warm-up discussion/activity and then deliver a talk in his/her area of expertise. We will wrap up with small-group and full-class discussions of related challenges/opportunities and possible engineering-oriented solutions. Class members are asked to do background reading before each class, to submit a question before each lecture, and to do in-class brainstorming. May be repeated for credit.
Terms: Win
| Units: 1
| Repeatable
for credit
EE 292J:
Designing for Authenticity
The Internet is at an inflection point. As mis/disinformation abounds and AI and synthetic media explode, the world's digital knowledge faces unprecedented threats. At the same time, a new generation of web technologies known as "Web3" offer new opportunities to protect the security and integrity of data. Our class jumps into this high-stakes moment and equips students with a new framework to understand and deploy methods to restore trust in digital content whether it's news and information, legally admissible evidence, or tamper-proof archives. Open to students of all experience levels, this class will provide an introduction to how advances in cryptography and the decentralized web can allow users to establish the provenance and veracity of data as it moves online. Students will create end-to-end technical prototypes and emerge with a new understanding that authenticity isn't a guaranteed part of information systems. You have to design for authenticity.
Terms: Win
| Units: 1
EE 292S:
Understanding the Sensors in your Smartphone
This course provides an introduction to the sensor systems found in modern-day smartphones, wearables, and hearable devices. As much as we take their functionality for granted, there is a tremendous amount of engineering needed to sense "real world" signals such as acceleration, touch, or altitude. There will be an overview on the actual circuitry and hardware used in sensor implementations, with a focus on MEMS devices (eg, accelerometer/gyro), going up through the algorithms commonly seen in sensors processing, and finally fusion of data from multiple sensors to yield final data presented to a user. The four broad areas that will be covered are: Inertial sensing/movement; Touch sensing/authentication; Health sensing (PPG, ECG, SpO2); Next-generation (force, radar/ranging, ultrasonics, and more). There is a lab/project associated with each of these areas, each project spanning roughly two weeks. The projects are designed to be more at a system level; the student will be required to explore the performance and limitations of sensing hardware, and then take that understanding to solve real-world sensor problems. All projects will be built on a Raspberry Pi with various sensor boards; students should be comfortable with wiring up a small breadboard, and coding on an RPi a high-level language such as Python or Java. Prerequisites: EE101A, EE102A, and CS106A or equivalents.
Terms: Aut
| Units: 3
EE 292T:
SmartGrids and Advanced Power Systems Seminar (CEE 272T)
A series of seminar and lectures focused on power engineering. Renowned researchers from universities and national labs will deliver bi-weekly seminars on the state of the art of power system engineering. Seminar topics may include: power system analysis and simulation, control and stability, new market mechanisms, computation challenges and solutions, detection and estimation, and the role of communications in the grid. The instructors will cover relevant background materials in the in-between weeks. The seminars are planned to continue throughout the next academic year, so the course may be repeated for credit.
Terms: Aut, Win, Spr
| Units: 1-2
| Repeatable
2 times
(up to 4 units total)
EE 292Y:
Software Techniques for Emerging Hardware Platforms (CS 349H)
Research seminar on software techniques for emerging computational substrates with guest lectures from hardware designers from research and industry. This seminar explores the benefits of novel hardware technologies, the challenges gating broad adoption of these technologies, and how software techniques can help mitigate these challenges and improve the usability of these hardware platforms. Note that the computational substrates discussed vary depending on the semester. Topics covered include: In-memory computing platforms, dynamical system-solving mixed-signal devices, exible and bendable electronics, neuromorphic computers, intermittent computing platforms, ReRAMs, DNA-based storage, and optical computing platforms. Prerequisites: CS107 or CS107E (required) and EE180 (recommended).
Terms: Aut
| Units: 3
EE 293B:
Fundamentals of Energy Processes (ENERGY 201B)
For seniors and graduate students. Covers scientific and engineering fundamentals of renewable energy processes involving heat. Thermodynamics, heat engines, solar thermal, geothermal, biomass. Recommended: MATH 19-21; PHYSICS 41, 43, 45
Terms: Win
| Units: 3
EE 300:
Master's Thesis and Thesis Research
Independent work under the direction of a department faculty. Written thesis required for final letter grade. The continuing grade 'N' is given in quarters prior to thesis submission. See 390 if a letter grade is not appropriate. Course may be repeated for credit.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
Instructors: ;
Achour, S. (PI);
Bambos, N. (PI);
Boahen, K. (PI);
Boneh, D. (PI);
Boyd, S. (PI);
Cioffi, J. (PI);
Dally, B. (PI);
Duchi, J. (PI);
El Gamal, A. (PI);
Emami-Naeini, A. (PI);
Engler, D. (PI);
Fan, J. (PI);
Fan, S. (PI);
Fraser-Smith, A. (PI);
Gibbons, J. (PI);
Girod, B. (PI);
Hanrahan, P. (PI);
Hennessy, J. (PI);
Hesselink, L. (PI);
Horowitz, M. (PI);
Howe, R. (PI);
Kahn, J. (PI);
Kapetanovic, Z. (PI);
Kazovsky, L. (PI);
Khuri-Yakub, B. (PI);
Kovacs, G. (PI);
Kozyrakis, C. (PI);
Lall, S. (PI);
Lee, T. (PI);
Levis, P. (PI);
McKeown, N. (PI);
Miller, D. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Murmann, B. (PI);
Nishimura, D. (PI);
Olukotun, O. (PI);
Osgood, B. (PI);
Pauly, J. (PI);
Pianetta, P. (PI);
Pilanci, M. (PI);
Plummer, J. (PI);
Prabhakar, B. (PI);
Raina, P. (PI);
Rivas-Davila, J. (PI);
Rosenblum, M. (PI);
Saraswat, K. (PI);
Schroeder, D. (PI);
Senesky, D. (PI);
Soh, H. (PI);
Solgaard, O. (PI);
Song, S. (PI);
Tobagi, F. (PI);
Van Roy, B. (PI);
Vuckovic, J. (PI);
Wang, S. (PI);
Weissman, T. (PI);
Widom, J. (PI);
Widrow, B. (PI);
Wong, H. (PI);
Wong, S. (PI);
Wootters, M. (PI);
Zebker, H. (PI);
Rutherford, E. (GP)
EE 301:
Introductory Research Seminar in Electrical Engineering
The EE 301 seminar course is offered primarily for incoming EE PhD students; however, all graduate or undergraduate students may enroll, and all students and faculty in the Department are welcome to attend. EE faculty members present seminars on their research, giving new PhD students an overview of research opportunities across the Department.
Terms: Aut
| Units: 1
| Repeatable
4 times
(up to 4 units total)
EE 309A:
Semiconductor Memory Devices and Circuit Design
The functionality and performance of ULSI systems are increasingly dependent upon the characteristics of the memory subsystem. This course introduces students to various semiconductor memory devices: SRAM, DRAM and FLASH, that are used in today's memory subsystems. The course will cover various aspects of semiconductor memories, including basic operation principles, device design considerations, device scaling, device fabrication, memory array architecture, and addressing and readout circuits. The course will also introduce students to recent research in near- and in-memory computing using these memory technologies. The next course is this series is EE 309B, which talks about emerging non-volatile memory devices and circuit design. Pre-requisite: EE 216. Preferred: EE 316.
Terms: Aut
| Units: 3
EE 309B:
Emerging Non-Volatile Memory Devices and Circuit Design
The functionality and performance of ULSI systems are increasingly dependent upon the characteristics of the memory subsystem. This course starts off where EE 309A leaves, and introduces students to various emerging non-volatile memory devices: metal oxide resistive switching memory (RRAM), nanoconductive bridge memory (CBRAM), phase change memory (PCM), magnetic tunnel junction memory, spin-transfer-torque random access memory (MRAM, STT-RAM), ferroelectric memory (FRAM) and ferroelectric transistor (FeFET). For each of these memories, the course will cover basic operation principles, device design considerations, device scaling, device fabrication, memory array architecture, and addressing and readout circuits. The course will also introduce students to recent in-memory computing research using these memory technologies. Pre-requisite: EE 216. Preferred: EE 316, EE 309A.
Terms: Win
| Units: 3
EE 310:
SystemX: Ubiquitous Sensing, Computing and Communication Seminar
This is a seminar course with invited speakers. Sponsored by Stanford's SystemX Alliance, the talks will cover emerging topics in contemporary hardware/software systems design. Special focus will be given to the key building blocks of sensors, processing elements and wired/wireless communications, as well as their foundations in semiconductor technology, SoC construction, and physical assembly as informed by the SystemX Focus Areas. The seminar will draw upon distinguished engineering speakers from both industry and academia who are involved at all levels of the technology stack and the applications that are now becoming possible. May be repeat for credit
Terms: Aut, Win, Spr
| Units: 1
| Repeatable
for credit
EE 312:
Integrated Circuit Fabrication Laboratory
Formerly EE 410. Fabrication, simulation, and testing of a submicron CMOS process. Practical aspects of IC fabrication including silicon wafer cleaning, photolithography, etching, oxidation, diffusion, ion implantation, chemical vapor deposition, physical sputtering, and electrical testing. Students also simulate the CMOS process using process simulator TSUPREM4 of the structures and electrical parameters that should result from the process flow. Taught in the Stanford Nanofabrication Facility (SNF). Preference to students pursuing doctoral research program requiring SNF facilities. Enrollment limited to 20. Prerequisites: EE 212, EE 216, or consent of instructor.
Terms: Win
| Units: 3-4
EE 317:
Special Topics on Wide Bandgap Materials and Devices
Wide-bandgap (WBG) semiconductors present a pathway to push the limits of efficiency in optoelectronics and electronics enabling significant energy savings, offering new and compact architecture, and more functionality. We will first study the examples set by GaN and SiC in lighting, radiofrequency and power applications, then use it to explore new materials like Ga2O3, AlN and diamond to understand their potential to drive the future semiconductor industry. The term papers will include a short project that may require simulation to conduct device design and analysis. Prerequisites: EE 216 or EE 218
Terms: Aut
| Units: 3
EE 329:
The Electronic Structure of Surfaces and Interfaces (PHOTON 329)
Physical concepts and phenomena for surface science techniques probing the electronic and chemical structure of surfaces, interfaces and nanomaterials. Microscopic and atomic models of microstructures; applications including semiconductor device technology, catalysis and energy. Physical processes of UV and X-ray photoemission spectroscopy, Auger electron spectroscopy, surface EXAFS, low energy electron diffraction, electron/photon stimulated ion desorption, scanning tunneling spectroscopy, ion scattering, energy loss spectroscopy and related imaging methods; and experimental aspects of these surface science techniques. Prerequisites: PHYSICS 70 and MATSCI 199/209, or consent of instructor.
Terms: Aut
| Units: 3
EE 334:
Micro and Nano Optical Device Design
Lecture and project course on design and analysis of optical devices with emphasis on opportunities and challenges created by scaling to the micrometer and nanometer ranges. The emphasis is on fundamentals, combined with some coverage of practical implementations. Prerequisite: EE 242 or equivalent
Terms: Aut
| Units: 3
EE 336:
Nanophotonics (MATSCI 346)
Recent developments in micro- and nanophotonic materials and devices. Basic concepts of photonic crystals. Integrated photonic circuits. Photonic crystal fibers. Superprism effects. Optical properties of metallic nanostructures. Sub-wavelength phenomena and plasmonic excitations. Meta-materials. Prerequisite: Electromagnetic theory at the level of 242.
Terms: Aut
| Units: 3
EE 347:
Optical Methods in Engineering Science
Design and understanding of modern optical systems. Topics: geometrical optics; aberration theory; systems layout; applications such as microscopes, telescopes, optical processors. Computer ray tracing program as a design tool. Prerequisite: 236A or equivalent.
Terms: Win
| Units: 3
EE 348:
Advanced Optical Fiber Communications
Optical amplifiers: gain, saturation, noise. Semiconductor amplifiers. Erbium-doped fiber amplifiers. System applications: preamplified receiver performance, amplifier chains. Raman amplifiers, lumped vs. distributed amplification. Group-velocity dispersion management: dispersion-compensating fibers, filters, gratings. Interaction of dispersion and nonlinearity, dispersion maps. Multichannel systems. Wavelength-division multiplexing components: filters, multiplexers. WDM systems, crosstalk. Time, subcarrier, code and polarization-division multiplexing. Comparison of modulation techniques: differential phase-shift keying, phase-shift keying, quadrature-amplitude modulation. Comparison of detection techniques: noncoherent, differentially coherent, coherent. Prerequisite: 247.
Terms: Aut
| Units: 3
EE 355:
Imaging Radar and Applications (GEOPHYS 265)
Radar remote sensing, radar image characteristics, viewing geometry, range coding, synthetic aperture processing, correlation, range migration, range/Doppler algorithms, wave domain algorithms, polar algorithm, polarimetric processing, interferometric measurements. Applications: surfafe deformation, polarimetry and target discrimination, topographic mapping surface displacements, velocities of ice fields. Prerequisites: EE261. Recommended: EE254, EE278, EE279.
Terms: Win
| Units: 3
EE 356A:
Resonant Converters
Miniaturization of efficient power converters remain a challenge in power electronics whose goal is improving energy use and reducing waste. In this course, we will study the design of Resonant converters which are capable of operating at higher frequencies than their 'hard-switch' counterparts. Resonant converter are found in high performance applications where high control bandwidth and high power density are required. We will also explore practical design issues and trade off in selecting converter topologies in high performance applications. Prerequisites: EE153/EE253.
Terms: Aut
| Units: 3
EE 359:
Wireless Communications
This course will cover advanced topics in wireless communications as well as current wireless system design. Topics include: an overview of current and future wireless systems; wireless channel models including path loss, shadowing, and statistical multipath channel models; fundamental capacity limits of wireless channels; digital modulation and its performance in fading and under intersymbol interference; techniques to combat fading including adaptive modulation and diversity; multiple antenna (MIMO) techniques to increase capacity and diversity, intersymbol interference including equalization, multicarrier modulation (OFDM), and spread spectrum; and multiuser system design, including multiple access techniques. Course is 3 units but can be taken for 4 units with an optional term project. Prerequisite: 279 or instructor consent.
Terms: Win
| Units: 3-4
EE 364A:
Convex Optimization I (CME 364A)
Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as EE263, basic probability.
Terms: Win, Sum
| Units: 3
Instructors: ;
Ayazifar, B. (PI);
Boyd, S. (PI);
Ogut, G. (PI);
Bell, L. (TA);
Hofgard, J. (TA);
Holt, G. (TA);
Johansson, K. (TA);
Parshakova, T. (TA);
Tse, D. (TA);
Yang, A. (TA)
EE 364M:
Mathematics of Convexity
This course covers the elegant mathematical underpinnings of convex optimization, with a focus on those analytic techniques central to the successes of the field. Topics include, but are not limited to, convex sets and functions, separation theorems, duality, set-valued analysis, and the mathematical insights central to the development of modern optimization methods. Pre- or co-requisite: EE364A, and mathematical analysis at the level of MATH171.
Terms: Win
| Units: 1
EE 367:
Computational Imaging (CS 448I)
Digital photography and basic image processing, convolutional neural networks for image processing, denoising, deconvolution, single pixel imaging, inverse problems in imaging, proximal gradient methods, introduction to wave optics, time-of-flight imaging, end-to-end optimization of optics and imaging processing. Emphasis is on applied image processing and solving inverse problems using classic algorithms, formal optimization, and modern artificial intelligence techniques. Students learn to apply material by implementing and investigating image processing algorithms in Python. Term project. Recommended: EE261, EE263, EE278.
Terms: Win
| Units: 3
EE 369A:
Medical Imaging Systems I (BMP 269A)
Imaging internal structures within the body using high-energy radiation and ultrasound, studied from a systems viewpoint. Modalities covered: x-ray, computed tomography, nuclear medicine, and ultrasound. Review of linear signals and systems, Fourier transforms, random variables, and noise. Analysis of existing and proposed systems in terms of resolution, frequency response, detection sensitivity, noise, and potential for improved diagnosis. This course covers Fourier transform basics and serves as an alternative prerequisite to EE 261 for EE 369B. Prerequisite: EE 102A (undergraduate-level signals and systems) or similar.
Terms: Win
| Units: 3
EE 370:
Reinforcement Learning: Behaviors and Applications (MS&E 237B)
This course treats reinforcement learning, which addresses the design of agents to operate in environments where actions induce delayed consequences. Concepts generalize those arising in bandit learning, which is covered in EE277/MS&E 237A. The course covers principled and scalable approaches to realizing a range of intelligent learning behaviors. Topics include planning, credit assignment, and learning of models, value functions, and policies. Motivating examples will be drawn from generative artificial intelligence, web services, control, and finance. Prerequisites: EE277.
Terms: Win
| Units: 3
EE 377:
Information Theory and Statistics (STATS 311)
Information theoretic techniques in probability and statistics. Fano, Assouad,nand Le Cam methods for optimality guarantees in estimation. Large deviationsnand concentration inequalities (Sanov's theorem, hypothesis testing, thenentropy method, concentration of measure). Approximation of (Bayes) optimalnprocedures, surrogate risks, f-divergences. Penalized estimators and minimumndescription length. Online game playing, gambling, no-regret learning. Prerequisites: EE 276 (or equivalent) or STATS 300A.
Terms: Aut
| Units: 3
EE 379A:
Data Transmission Design
Data Transmission Design is the first of a two-quarter sequence (leading to EE379B) in MSEE communications depth sequence. Intended students are those interested in research or design of data transmission systems' lower layers. The course includes methods for transmission designs with and without coding and includes basic examples as well as their relationship to modern current/next-generation wireless and wireline transmission systems. The course also develops and uses information measures as generalizations of signal processing and minimum-mean-square-error estimation, developing design intution. Basic phase-locking and synchronization methods also appear. EE379B progresses to multidimensional modulation methods and their use in modern and next-generation multiuser MIMO networks, along with network-design strategies. Prerequisites: EE102B and EE278 (or equivalents). EE279 is helpful but not required.
Terms: Win
| Units: 3
EE 380:
Colloquium on Computer Systems
Live presentations of current research in the design, implementation, analysis, and applications of computer systems. Topics range over a wide range and are different every quarter. Topics may include fundamental science, mathematics, cryptography, device physics, integrated circuits, computer architecture, programming, programming languages, optimization, applications, simulation, graphics, social implications, venture capital, patent and copyright law, networks, computer security, and other topics of related to computer systems. May be repeated for credit.
Terms: Win, Sum
| Units: 1
| Repeatable
for credit
EE 381:
Sensorimotor Learning for Embodied Agents (CS 381)
This is an advanced course that will focus on modern machine learning algorithms for autonomous robots as an embodied intelligent agent. It covers advanced topics that center around 1. what is embodied AI and how it differs from internet AI, 2. how embodied agents perceive their environment from raw sensory data and make decisions, and 3. continually adapt to the physical world through both hardware and software improvements. By the end of the course, we hope to prepare you for conducting research in this area, knowing how to formulate the problem, design the algorithm, critically validate the idea through experimental designs and finally clearly present and communicate the findings. Students are expected to read, present, and debate the latest research papers on embodied AI, as well as obtain hands-on experience through the course projects. Prerequisites: Recommended EE 160A/EE 260A /CS 237A or equivalent.
Terms: Aut
| Units: 3
EE 382C:
Interconnection Networks
The architecture and design of interconnection networks used to communicate from processor to memory, from processor to processor, and in switches and routers. Topics: network topology, routing methods, flow control, router microarchitecture, and performance analysis. Enrollment limited to 30. Prerequisite: 282.
Terms: Win
| Units: 3
EE 390:
Special Studies or Projects in Electrical Engineering
Independent work under the direction of a faculty member. Individual or team activities may involve lab experimentation, design of devices or systems, or directed reading. May be repeated for credit.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
Instructors: ;
Arbabian, A. (PI);
Bambos, N. (PI);
Boahen, K. (PI);
Boneh, D. (PI);
Bowden, A. (PI);
Boyd, S. (PI);
Choi, J. (PI);
Cioffi, J. (PI);
Congreve, D. (PI);
Dally, B. (PI);
Duchi, J. (PI);
El Gamal, A. (PI);
Emami-Naeini, A. (PI);
Engler, D. (PI);
Fan, J. (PI);
Fan, S. (PI);
Fraser-Smith, A. (PI);
Gibbons, J. (PI);
Girod, B. (PI);
Hanrahan, P. (PI);
Hennessy, J. (PI);
Hesselink, L. (PI);
Horowitz, M. (PI);
Howe, R. (PI);
Kahn, J. (PI);
Kapetanovic, Z. (PI);
Katti, S. (PI);
Kazovsky, L. (PI);
Khuri-Yakub, B. (PI);
Kovacs, G. (PI);
Kozyrakis, C. (PI);
Lall, S. (PI);
Lee, T. (PI);
Levin, C. (PI);
Levis, P. (PI);
McKeown, N. (PI);
Miller, D. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Murmann, B. (PI);
Nishimura, D. (PI);
Olukotun, O. (PI);
Osgood, B. (PI);
Ozgur, A. (PI);
Pauly, J. (PI);
Pianetta, P. (PI);
Pilanci, M. (PI);
Plummer, J. (PI);
Poon, A. (PI);
Pop, E. (PI);
Prabhakar, B. (PI);
Raina, P. (PI);
Rajagopal, R. (PI);
Rivas-Davila, J. (PI);
Rosenblum, M. (PI);
Saraswat, K. (PI);
Schroeder, D. (PI);
Senesky, D. (PI);
Shenoy, K. (PI);
Smith, J. (PI);
Soh, H. (PI);
Solgaard, O. (PI);
Song, S. (PI);
Tobagi, F. (PI);
Trippel, C. (PI);
Tse, D. (PI);
Van Roy, B. (PI);
Vuckovic, J. (PI);
Wang, S. (PI);
Weissman, T. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Widrow, B. (PI);
Wong, H. (PI);
Wong, S. (PI);
Wootters, M. (PI);
Zebker, H. (PI);
Rutherford, E. (GP)
EE 391:
Special Studies and Reports in Electrical Engineering
Independent work under the direction of a faculty member; written report or written examination required. Letter grade given on the basis of the report; if not appropriate, student should enroll in 390. May be repeated for credit.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
Instructors: ;
Achour, S. (PI);
Arbabian, A. (PI);
Bambos, N. (PI);
Boahen, K. (PI);
Boneh, D. (PI);
Bowden, A. (PI);
Boyd, S. (PI);
Choi, J. (PI);
Chowdhury, S. (PI);
Cioffi, J. (PI);
Congreve, D. (PI);
Dally, B. (PI);
Duchi, J. (PI);
El Gamal, A. (PI);
Emami-Naeini, A. (PI);
Engler, D. (PI);
Fan, J. (PI);
Fan, S. (PI);
Fejer, M. (PI);
Finn, C. (PI);
Flynn, M. (PI);
Fraser-Smith, A. (PI);
Gibbons, J. (PI);
Girod, B. (PI);
Hanrahan, P. (PI);
Hennessy, J. (PI);
Hesselink, L. (PI);
Horowitz, M. (PI);
Howe, R. (PI);
Kahn, J. (PI);
Kapetanovic, Z. (PI);
Katti, S. (PI);
Kazovsky, L. (PI);
Khuri-Yakub, B. (PI);
Kovacs, G. (PI);
Kozyrakis, C. (PI);
Lall, S. (PI);
Lee, T. (PI);
Levin, C. (PI);
Levis, P. (PI);
McKeown, N. (PI);
Miller, D. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Murmann, B. (PI);
Nishimura, D. (PI);
Olukotun, O. (PI);
Osgood, B. (PI);
Ozgur, A. (PI);
Pauly, J. (PI);
Pianetta, P. (PI);
Pilanci, M. (PI);
Plummer, J. (PI);
Poon, A. (PI);
Pop, E. (PI);
Prabhakar, B. (PI);
Raina, P. (PI);
Rajagopal, R. (PI);
Rivas-Davila, J. (PI);
Rosenblum, M. (PI);
Sadigh, D. (PI);
Saraswat, K. (PI);
Schroeder, D. (PI);
Senesky, D. (PI);
Soh, H. (PI);
Solgaard, O. (PI);
Song, S. (PI);
Tobagi, F. (PI);
Trippel, C. (PI);
Tse, D. (PI);
Van Roy, B. (PI);
Vuckovic, J. (PI);
Wang, S. (PI);
Weissman, T. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Widrow, B. (PI);
Wong, H. (PI);
Wong, S. (PI);
Wootters, M. (PI);
Zebker, H. (PI);
Rutherford, E. (GP)
EE 392D:
Wireless Sensing Systems
This research course will cover current topics related to wireless communication, sensing, and the Internet of Things (IoT). Students will read published research papers, participate in group discussions, and complete a final research project in small groups. The course is open to all Ph.D., masters, and advanced undergraduate students. Prerequisites: This course does not have any official prerequisites. However, students should have a mature understanding of wireless sensor networks and embedded systems.
Terms: Aut
| Units: 3
EE 400:
Thesis and Thesis Research
Limited to candidates for the degree of Engineer or Ph.D.May be repeated for credit.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
Instructors: ;
Arbabian, A. (PI);
Bambos, N. (PI);
Boahen, K. (PI);
Boneh, D. (PI);
Boyd, S. (PI);
Chowdhury, S. (PI);
Cioffi, J. (PI);
Congreve, D. (PI);
Dally, B. (PI);
Duchi, J. (PI);
El Gamal, A. (PI);
Emami-Naeini, A. (PI);
Engler, D. (PI);
Fan, J. (PI);
Fan, S. (PI);
Fejer, M. (PI);
Fraser-Smith, A. (PI);
Gibbons, J. (PI);
Girod, B. (PI);
Hanrahan, P. (PI);
Hennessy, J. (PI);
Hesselink, L. (PI);
Horowitz, M. (PI);
Howe, R. (PI);
Kahn, J. (PI);
Kapetanovic, Z. (PI);
Katti, S. (PI);
Kazovsky, L. (PI);
Khuri-Yakub, B. (PI);
Kovacs, G. (PI);
Kozyrakis, C. (PI);
Lall, S. (PI);
Lee, T. (PI);
Levis, P. (PI);
McKeown, N. (PI);
Miller, D. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Murmann, B. (PI);
Nishi, Y. (PI);
Nishimura, D. (PI);
Olukotun, O. (PI);
Osgood, B. (PI);
Ozgur, A. (PI);
Pauly, J. (PI);
Pauly, K. (PI);
Pianetta, P. (PI);
Pilanci, M. (PI);
Plummer, J. (PI);
Poon, A. (PI);
Pop, E. (PI);
Prabhakar, B. (PI);
Raina, P. (PI);
Rajagopal, R. (PI);
Rivas-Davila, J. (PI);
Rosenblum, M. (PI);
Sadigh, D. (PI);
Saraswat, K. (PI);
Schroeder, D. (PI);
Senesky, D. (PI);
Soh, H. (PI);
Solgaard, O. (PI);
Song, S. (PI);
Tobagi, F. (PI);
Trippel, C. (PI);
Tse, D. (PI);
Van Roy, B. (PI);
Vuckovic, J. (PI);
Wang, S. (PI);
Weissman, T. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Widrow, B. (PI);
Wong, H. (PI);
Wong, S. (PI);
Wootters, M. (PI);
Zebker, H. (PI);
Rutherford, E. (GP)
EE 402A:
Topics in International Technology Management (EALC 402A, EASTASN 402A)
Autumn 2023 Theme: "The Emerging Digital Economy in Context: US-Asia Cooperation and Competition." This course will examine ways in which new digital technologies, business models, and data governance frameworks are addressing problems and opportunities at the interface between the digital economy and the external world, with special attention to new patterns of competition and cooperation between Asia and the U.S. Individual sessions will focus on topics such as live commerce, new models of AI governance, the role of digital transformation in addressing climate change, cross-border data sharing in an era of heightened concern for privacy and security, digital platforms for supply chain integration, and AI competition. Distinguished speakers and panels from industry and government.
Terms: Aut
| Units: 1
| Repeatable
for credit
EE 469B:
RF Pulse Design for Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) and spectroscopy (MRS) based on the use of radio frequency pulses to manipulate magnetization. Analysis and design of major types of RF pulses in one and multiple dimensions, analysis and design of sequences of RF pulses for fast imaging, and use of RF pulses for the creation of image contrast in MRI. Prerequisite: 369B.
Terms: Aut
| Units: 3
EE 801:
TGR Project
May be repeated for credit.
Terms: Aut, Win, Spr, Sum
| Units: 0
| Repeatable
for credit
Instructors: ;
Arbabian, A. (PI);
Bambos, N. (PI);
Boahen, K. (PI);
Boneh, D. (PI);
Boyd, S. (PI);
Cioffi, J. (PI);
Dally, B. (PI);
Duchi, J. (PI);
El Gamal, A. (PI);
Fan, J. (PI);
Fan, S. (PI);
Fraser-Smith, A. (PI);
Gibbons, J. (PI);
Girod, B. (PI);
Hanrahan, P. (PI);
Hennessy, J. (PI);
Hesselink, L. (PI);
Horowitz, M. (PI);
Howe, R. (PI);
Kahn, J. (PI);
Kapetanovic, Z. (PI);
Katti, S. (PI);
Kazovsky, L. (PI);
Khuri-Yakub, B. (PI);
Kozyrakis, C. (PI);
Lall, S. (PI);
Lee, T. (PI);
Levis, P. (PI);
McKeown, N. (PI);
Miller, D. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Murmann, B. (PI);
Nishimura, D. (PI);
Olukotun, O. (PI);
Osgood, B. (PI);
Pauly, J. (PI);
Pianetta, P. (PI);
Pilanci, M. (PI);
Plummer, J. (PI);
Poon, A. (PI);
Pop, E. (PI);
Prabhakar, B. (PI);
Raina, P. (PI);
Rajagopal, R. (PI);
Rivas-Davila, J. (PI);
Rosenblum, M. (PI);
Sadigh, D. (PI);
Saraswat, K. (PI);
Schroeder, D. (PI);
Senesky, D. (PI);
Soh, H. (PI);
Solgaard, O. (PI);
Song, S. (PI);
Tobagi, F. (PI);
Van Roy, B. (PI);
Vuckovic, J. (PI);
Wang, S. (PI);
Weissman, T. (PI);
Widom, J. (PI);
Widrow, B. (PI);
Wong, H. (PI);
Wong, S. (PI);
Wootters, M. (PI);
Zebker, H. (PI)
EE 802:
TGR Dissertation
May be repeated for credit.
Terms: Aut, Win, Spr, Sum
| Units: 0
| Repeatable
for credit
Instructors: ;
Arbabian, A. (PI);
Bambos, N. (PI);
Boahen, K. (PI);
Boneh, D. (PI);
Bowden, A. (PI);
Boyd, S. (PI);
Chowdhury, S. (PI);
Cioffi, J. (PI);
Congreve, D. (PI);
Dally, B. (PI);
Duchi, J. (PI);
El Gamal, A. (PI);
Engler, D. (PI);
Fan, J. (PI);
Fan, S. (PI);
Fraser-Smith, A. (PI);
Gibbons, J. (PI);
Girod, B. (PI);
Hanrahan, P. (PI);
Hennessy, J. (PI);
Hesselink, L. (PI);
Horowitz, M. (PI);
Howe, R. (PI);
Inan, U. (PI);
Kahn, J. (PI);
Kapetanovic, Z. (PI);
Katti, S. (PI);
Kazovsky, L. (PI);
Khuri-Yakub, B. (PI);
Kovacs, G. (PI);
Kozyrakis, C. (PI);
Lall, S. (PI);
Lee, T. (PI);
Levin, C. (PI);
Levis, P. (PI);
Levoy, M. (PI);
McKeown, N. (PI);
Miller, D. (PI);
Mitchell, J. (PI);
Mitra, S. (PI);
Montanari, A. (PI);
Murmann, B. (PI);
Nishimura, D. (PI);
Olukotun, O. (PI);
Osgood, B. (PI);
Ozgur, A. (PI);
Pauly, J. (PI);
Pauly, K. (PI);
Pianetta, P. (PI);
Pilanci, M. (PI);
Plummer, J. (PI);
Poon, A. (PI);
Pop, E. (PI);
Prabhakar, B. (PI);
Raina, P. (PI);
Rajagopal, R. (PI);
Rivas-Davila, J. (PI);
Rosenblum, M. (PI);
Sadigh, D. (PI);
Saraswat, K. (PI);
Schroeder, D. (PI);
Senesky, D. (PI);
Soh, H. (PI);
Solgaard, O. (PI);
Song, S. (PI);
Tobagi, F. (PI);
Tse, D. (PI);
Van Roy, B. (PI);
Vuckovic, J. (PI);
Wang, S. (PI);
Weissman, T. (PI);
Wetzstein, G. (PI);
Widom, J. (PI);
Widrow, B. (PI);
Wong, H. (PI);
Wong, S. (PI);
Wootters, M. (PI);
Xing, L. (PI);
Zebker, H. (PI);
Rutherford, E. (GP)
