Course goals: learn how to design a secure system, probe systems for weaknesses, write code with fewer security bugs, use crypto libraries correctly, protect (or breach!) privacy, and use your powers ethically. Main topics: basic cryptography, system security, network security, firewalls, malware, web security, privacy technologies, cryptocurrencies, human factors, physical security, economics, and ethics of security.

Signals that carry information, e.g. sound, images, sensors, radar, communication, robotic control, play a central role in technology and engineering. This course teaches mathematical tools to analyze, manipulate, and preserve information signals. We discuss both continuous signals and digital signals. Major focus points are the Fourier transform, linear time-invariant systems, frequency domain, and filtering. We use MatLab for laboratory exercises. Three lectures, one laboratory.

Introduction to electronic circuits and systems. Methods of circuit analysis to create functions from devices, including resistors, capacitors, inductors, diodes, and transistors, in conjunction with op-amps. Quantitative focus on DC and higher-frequency signals using linear systems theory with major emphasis on intuition. Students pursue design (using op-amps and micro controllers), simulations (using SPICE), and analysis in labs.

Provides an opportunity for a student to concentrate on a state of the art project in electrical engineering. Topics may be selected from suggestions by faculty members or proposed by the students. The final choice must be approved by the faculty advisor. There is no formal reading list; however, a literature search is a normal part of most projects.

The course will cover topics related to electronic system design through the various layers of abstraction from devices to ICs. The emphasis will be on understanding fundamental system-design tradeoffs, related to the speed, precision, power with intuitive design methods, quantitative performance measures, and practical circuit limitations. The understanding of these fundamental concepts will prepare students for a wide range of advanced topics from circuits and systems such as wireless and wired communications, sensors and power management.

Fundamentals of quantum mechanics and statistical mechanics needed for understanding the principles of operation of modern solid state and optoelectronic devices and quantum computers. Topics covered include Schrödinger Equation, Operator and Matrix Methods, Quantum Statistics and Distribution Functions, and Approximation Methods, with examples from solid state and materials physics and quantum electronics. The course complements ECE 396, Introduction to Quantum Computing as well as ECE 445, Solid State Devices, and prepares the student for more advanced courses (e.g., ECE 441, ECE 442, ECE 453, ECE 456).

Robotic systems are quickly becoming more capable and adaptable, entering new domains from transportation to healthcare. To reliably carry out complex tasks in changing environments and around people, these systems rely on increasingly sophisticated artificial intelligence. This course covers the core concepts and techniques underpinning modern robot autonomy, including planning under uncertainty, imitation and reinforcement learning, multiagent interaction, and safety. The lab component introduces the Robot Operating System (ROS) framework and applies the learned theory to hands-on autonomous driving assignments on 1/16-scale robot trucks.

Communication systems have become a ubiquitous part of modern life. This course introduces students to the basics of digital communication and wireless systems. Topics include concepts from signal modulation and radio propagation to wireless networks and mobile systems. Students will also gain hands-on experience through working with software-defined radios and will learn about the implementation aspects of practical systems. Additionally, students will learn data analysis and learning techniques relevant to perceiving wireless signals.

Provides an opportunity for a student to concentrate on a "state-of-the-art" project in electrical engineering. Topics may be selected from suggestions by faculty members or proposed by the student. The final choice must be approved by the faculty member.

Reinforcement learning (RL) is a core technology at the heart of modern AI about using machine learning and AI methods to make decisions that lead to good outcomes. In this course, we aim to give an introductory overview of reinforcement learning, fundamentals concepts and algorithms, as well as core challenges in RL including exploration and generalization. The course will also highlight case studies of RL applications to real-world problems, including health care and molecular science. Assignments for this lecture-based course will include written mathematical exercises, implementing RL algorithms, as well as a final group project.

Bioelectronics plays an increasingly vital role in fundamental research, therapeutics, and everyday life. This course will explore the basic principles of bioelectronics and their applications in biomedicine. The first part of the course will cover the fundamentals of bioelectricity, different types of biosensors, and related signal processing. The second part of the course will introduce the interface between bioelectronics and biological systems and the applications of bioelectronics devices in neuroscience, cardiology, tissue engineering, and wearable technologies.

This course gives a general introduction to biological and biomedical imaging. Topics include basic imaging theory, microscopy, tomography, and imaging through tissue. Both physical and computational imaging will be covered, across a variety of different modalities (including visible light, x-ray, MRI, and ultrasound). The gaps between current technology and limits suggested by information theory will be discussed.

This class will teach you about optical and photonic sensing technologies and their applications to environmental monitoring. The course will contain elements of atmospheric science and Earth observation, fundamentals of optics, photonics and laser physics, as well as a survey of modern optical and spectroscopic sensing applications. In this course students will be asked to prepare two oral presentations and there will be three laboratory assignments focused on fundamentals of optical sensing

Semiclassical field theory of light-matter interactions (Maxwell-Bloch equations). Quantum theory of light, vacuum fluctuations and photons. Quantum states and coherence properties of the EM field, photon counting and interferometry. Quantum theory of light-matter interactions, Jaynes-Cummigns (JC) model. Physical realizations of JC model, case study:circuit QED. Quantum theory of damping. Resonance fluorescence. Coupled quantum non-linear systems: Lattice CQED, Superradiance

This course aims to introduce students to the basics of experimental quantum information processing. Students will gain hands-on experience with several qubit platforms, including single photons, nuclear spins (NMR), electron spins (NV centers in diamond), and superconducting qubits. Additionally, students will learn data analysis and signal processing techniques relevant for a wide range of quantum computing platforms.

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.

Blockchains are digital platforms whose consistency and liveness are maintained by a decentralized set of participants. The combination of programmability, permissionless access and the financial nature of the underlying token (e.g., ETH in the Ethereum blockchain) has led to tremendous innovation in financial products on the blockchain, broadly covered under the rubric of decentralized finance or simply DeFi. The purpose of this course is to introduce these developments classified as "elements" of DeFi, from a computer science point of view. Periodic programming assignments provide a hands-on instruction to the technical material.

An in-depth study of the fundamentals of modern computer processor and system architecture. Students will develop a strong theoretical and practical understanding of modern, cutting-edge computer architectures and implementations. Studied topics include: Instruction-set architecture and high-performance processor organization including pipelining, out-of-order execution, as well as data and instruction parallelism. Cache, memory, and storage architectures. Multiprocessors and multicore processors. Coherent caches. Interconnection and network infrastructures.

Provides an opportunity for a student to concentrate on a "state-of-the-art" project in electrical engineering. A student may propose a topic and find a faculty member willing to supervise the work. Or the student may select a topic from lists of projects obtained from faculty and off-campus industrial researchers, subject to the consent of the faculty advisor.

No description available

Full-time research internship at a host institution, to perform scholarly research relevant to student's dissertation work. Research objectives are 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.

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.

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.

This course surveys historical and recent developments in the field of quantum error correction and its application to fault-tolerant quantum computing. After establishing the basic formalism and reviewing some historically important results, we cover several types of error correcting codes (ie, topological codes, bosonic codes, quantum low-density parity check codes) and concepts in fault-tolerant computation (ie, transversal gates, lattice surgery, flag fault) tolerance, magic state injection, and measurement-based quantum computing). We also cover recent experimental demonstrations in a variety of hardware platforms.

This introductory graduate course focuses on theory and applications of subwavelength optical elements (SOEs) that have features smaller than the wavelength of light, and work with principles differently from traditional and diffractive optics, offering unique optical capabilities. For example, SOEs create new functionality ultra-thin optical systems in a thickness < 1 micron. The main course topics include: (1) rigorous Maxwell theory for SOEs, (2) mean-field theory for SOEs, (3) non-resonant SOEs, (4) resonant SOEs (photonic crystals), (5) nano-plasmonics, and (6) active optical devices using SOE.

Rapid advances in photonic chip integration has enabled the development of increasingly sophisticated photonic systems for communications and computing. This course covers: Silicon photonic chip design; photonic system fundamentals, 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.

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.

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.

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.

This course explores MLP (NN1.0), CNN (NN2.0) and Transformers. Basic topics: Sigmoid/ReLU activations, BP learning, dropout, regularization, generalization, classification and prediction. Advanced topics: (1) unifying MLP and CNN learning methods, (2) unifying classification and regression applications, and (3) input/output residual learning to mitigate curse of depth, (4) Hybrid NAS (Progressive & Regressive Neural Architecture Search) and (5) Generative AI, via transformer and stable diffusion, which can learn contextually from huge pretraining datasets by using Large Language Models (LLM), enabling generation of creative contents.

An in-depth study of the fundamentals of modern computer processor architecture. Students develop a strong theoretical and practical understanding of the design of modern, cutting-edge, computer architectures and implementations. Studied topics include: instruction-set architecture and high-performance processor organization including pipelining, out-of-order execution, as well as data and instruction parallelism, Cache, memory and storage architectures. Multiprocessors and multicore processors. Coherent caches, interconnection and network infrastructures.

This seminar course covers the key developments in hardware security to understand the problem space and the range of techniques used for their solutions. We review key papers in circuits (e.g. physically unclonable functions), logic design (e.g. logic encryption, Trojans), architecture (e.g. software-based attacks, timing side channels), and analysis techniques (SAT/SMT solvers, model checkers).

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

This course is designed to help SEAS graduate students cultivate ethical awareness, reflection, and practical tools regarding their research practices for future work at or beyond the University. It encourages graduate engineering students: to consider the social and ethical impact of their research; and to develop disciplines of 'ethical reflection and analysis' in their professional conduct and throughout the engineering process. Though specific Codes of Ethics within varying engineering societies are useful, they are not sufficient in preparing engineers for the social and ethical challenges that arise in today's complex systems.

This course explores broadly renewable energy systems and smart grids. Technical and operational principles of the modern electric grids will be introduced, followed by an overview of various energy sources from fossil-fuel generators to photovoltaic systems. The intermittency of renewable energy systems and its impact on the electric grid will be discussed together with its potential solutions: energy storage systems and demand response techniques. This course will also include a few experimental demo sessions in which students will gain hands-on experience in understanding the fundamental principles of power conversion.