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.

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.

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 how continuous signals can be perfectly represented through sampling, leading to 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.

Comprehensive, laboratory-based course in electronic system design and analysis. Covers formal methods for the design and analysis of moderately complex real-world electronic systems. Course is centered around a semester-long design project involving a computer-controlled vehicle designed and constructed by teams of two students. Integrates microprocessors, communications, and control.

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 operate in dynamic environments, interact with other agents, and accomplish complex tasks, these systems require sophisticated decision-making. This course delves into the core concepts and techniques underpinning modern autonomous robots, including planning under uncertainty, active perception, learning-based control, and multiagent decision-making. Lectures cover the theoretical foundations and the practical component introduces the Robot Operating System (ROS) framework through hands-on assignments with mobile robots.

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.

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.

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

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

Introduction; Cyber-Physical Systems (CPS); Internet-of-Things; Real-Time Systems; Software Performance Analysis; Hardware-Software Co-Design; Embedded Processors; Power/Energy Consumption; Low-Power Embedded System Design; Reliable/Available Embedded Systems; Reinforcement Learning and its Applications; CPS Controller Synthesis.

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.

This course will present data analytics perspectives of electric power systems. The course offers an introduction to the basic concepts of power system operation and planning, along with necessary theories and methods in optimization. Topics include modeling and optimization of power networks, power flow analysis, state estimation and observability, bad data detection, introduction to the electricity market, and selected topics in smart grids. Strong emphasis will be placed on developing practical techniques to solve convex and stochastic optimization problems.

Senior Thesis Course. The student has the opportunity to do a self driven project by proposing a topic and finding a faculty member willing to supervise the work, or, the student may do a project in conjunction with a faculty member's research. A second reader will be required for both the midterm report and final thesis report. Students will be required to enroll in ECE 498 in the spring.

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.

The course is a graduate level course, focusing on the optimization theory (algorithms and complexity analysis) that arise in machine learning. It covers topics such as convex/nonconvex optimization, gradient methods, accelerations, stochastic algorithms, variance reduction, minimax optimization, etc. The course is proof-based, and mathematical oriented. A similar version of this course has been previously given by Prof. Elad Hazan in CS department in Spring 2019.

Wireless communications and machine learning are the most transformative technologies of our time. Recent advances in both fields have created challenges and opportunities that are driving the merger of these technologies: 1. machine learning can play a major role in the optimization of wireless networks, as their design and operation involve many problems that are becoming increasingly difficult to solve using traditional model-based techniques: 2. wireless networks can serve as platforms for machine learning and responding to the facts that much useful data is born at the network edge and that many applications are deployed there.

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.

An introductory course for the first and second year graduate students to understand the theory and application of the photonic materials and devices, which have feature size smaller than the wavelength of light, termed "subwavelength photonics (SWP) or "subwavelength optical elements" (SOE). The subwavelength materials/devices include dielectric and metallic (plasmonic) structures. Main topics covered: 1. rigorous Maxwell theory for SWPs, 2. mean-field theory for SWPs, 3. non-resonant SWPs, 4. resonant SWPs 5. applications of SWP devices and 6. numerical simulation.

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.

Robotic systems are quickly becoming more capable and adaptable, entering new domains from transportation to healthcare. To operate in dynamic environments, interact with other agents, and accomplish complex tasks, these systems require sophisticated decision-making. This course delves into the core concepts and techniques underpinning modern autonomous robots, including planning under uncertainty, active perception, learning-based control, and multiagent decision-making. Lectures cover the theoretical foundations and the practical component introduces the Robot Operating System (ROS) framework through hands-on assignments with mobile robots.

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.

The course explores basic and advanced topics on MLP (NN1.0), CNN (NN2.0), and NAS (Neural Architecture Search) for deep learning. Basic topics: Sigmoid/ReLU activations, dropout, regularization, and BP learning of net's parameters. More advanced: (1) unifying MLP and CNN learning methods, (2) unifying classification and regression applications, and (3) balancing training and generalization, and (4) applying input/output residual learning to mitigate curse of depth. This ultimately leads to an architecture engineering system (XNAS), a combination of joint parameter/structure X-learning and reinforcement learning paradigms.

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 hands-on course introduces students to analysis and actions required to launch and commercialize a tech company, through the use of Harvard Business School cases, visits from entrepreneurs, and two "field assignments". You will learn conceptual frameworks and analytical techniques for evaluating technologies, markets, and commercialization strategies. Additionally, you will learn how to attract and motivate the resources needed to start a company (e.g. people, corporate partners and venture capital), prepare business plans, structure relationships, refine product-market fit, and create and grow enterprise value.

This course provides a broad overview of power electronics and smart grids. We introduce the connection between Watts and Bytes in intelligently controlled power electronics and smart energy systems, develop a fundamental understanding about power electronics from devices, circuits, systems to control, and review operation principles of the modern energy systems from power generation, transmission, to utilization. Numerous examples will be presented, including power electronics for renewable energy systems, information systems, robotics, and transportation electrification. Students build a real power converter at the end of the course.

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.