Reinforcement learning (RL) is a machine learning technique that teaches agents how to make decisions that lead to good outcomes. This course will introduce fundamental concepts, important RL algorithms, and key challenges (e.g., exploration and generalization). The course will also highlight applications of RL to real-world problems, including health care and molecular science. Assignments will entail implementation of RL algorithms and mathematical analysis of these algorithms. Students will complete an open-ended final group project.

Recent advances in neural rendering have made it possible to generate novel photo-realistic views of real-world 3D scenes just from a set of regular images. The most successful approaches combine ideas from computer graphics, machine learning, and optimization. Specifically, neural rendering methods such as NeRF or DeepFusion combine conventional volumetric rendering methods with a coordinate-based neural networks trained to predict continuous density and radiance estimates. These learned scene representations also have broad application across a wide set of domains, including virtual reality, robotics, health, and computer vision.

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

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.

This course provides the students with a broad and solid background in electromagnetism, including both statics and dynamics, as described by Maxwell's equations. Fundamental concepts of diffraction theory, Fourier optics, polarization of light, and geometrical optics will be discussed. Emphasis is on engineering principles, and applications will be discussed throughout. A laboratory component will acquaint students with modern simulation tools for modeling optical phenomena. Examples include cavities, waveguides, antennas, fiber optic communications, and imaging.

This course introduces the fundamental mathematical principles and methods that play a central role in modern signal and information processing. Specific topics include random processes, linear regression and estimation, hypothesis testing and detection, and shrinkage methods.

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.

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.

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

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

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.

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.

The increasing demand for AI, genomics, real-time data analytics, and other emerging applications has made parallel computing indispensable-from multi-core CPUs/GPUs in local environments to cloud infrastructures and the largest supercomputers.This course aims to develop a fundamental understanding of the principles of parallelism and design trade-offs in modern computing systems, covering hardware architecture, software efficiency, and algorithm optimization. Students will acquire practical parallel programming techniques (including GPU programming) and explore key performance metrics beyond speed, such as scalability and energy efficiency.

Introduction to smart healthcare; health decision support system; wearable medical sensors and deep neural network based disease detection; continual learning based multi-headed neural networks for multi-disease detection; interpretability through differentiable logic networks; interpretability through conformal predictions; medical images and convolutional neural network based disease detection; natural language processing for healthcare; foundation models for healthcare; counterfactual reasoning based personalized medical decision-making.

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.

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.

We introduce tools from image sciences, sensing, and machine learning to study, analyze, and positively affect human behavior. Emphasis is on health and wellbeing related behaviors. We study how computer vision, NLP, causal inference, LLMs, and other tools can be developed and used to understand and modify human behavior. We also investigate how to incorporate behavioral sciences into ML and related fields. Applications range from developmental and eating disorders to fitness and aging. We host experts that study the many components of this field, from clinical to ethical and behavioral sciences.

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

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.

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.

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).

The focus of this course is the art of metrology. In particular: 1) What are the important performance metrics of various integrated circuits and systems? 2) Why are these parameters significant and how they relate to overall application performance? 3) How do we measure and verify these metrics using a wide range of instrumentations? 4) How do various instruments work internally and methods to increase measurement confidence?. The course also has a hands-on lab. The students must design a band-pass filter on PCB and have it manufactured. They then assemble the PCB and measure it in the lab.

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.

Principles and design of solar energy conversion systems. Quantity and availability of solar energy. Physics and chemistry of solar energy conversion: solar optics, optical excitation, capture of excited energy, and transport of excitations or electronic charge. Conversion methods: thermal, wind, photoelectric, photoelectrochemical, photosynthetic, biomass. Solar energy systems: low and high temperature conversion, photovoltaics. Storage of solar energy. Conversion efficiency, systems cost, and lifecycle considerations.

This course covers the theory and numerical algorithms of nonlinear filtering and smoothing, starting with the discrete-time linear Gaussian case and advancing through the general continuous-time nonlinear non-Gaussian case. Variants of Kalman and ensemble methods will be covered with derivations and sketches of important proofs. A review of the necessary elements from probability and stochastic processes is included. Following the theory, numerical algorithms are regularly demonstrated on a suite of problems that include aerospace and geoscience applications.