This course provides a comprehensive and practical background for students interested in continuous mathematics for computer science. The goal is to prepare students for higher-level subjects in artificial intelligence, machine learning, computer vision, natural language processing, graphics, and other topics that require numerical computation. This course is intended students who wish to pursue these more advanced topics, but who have not taken (or do not feel comfortable) with university-level multivariable calculus (e.g., MAT 201/203) and probability (e.g., ORF 245 or ORF 309). See "Other Information"

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 how continuous signals can be perfectly represented through sampling, leading to digital signals. Major focus points are the Fourier transform---how, when, and why to use it, linear time-invariant systems, modulation, and stability. We use MatLab for design projects. 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. A major emphasis on intuition, with labs organized as mini projects (1. Touch-screen controller, 2. Radio, 3. Brain-machine interface), where students pursue design (using op-amps and micro controllers), simulations (using SPICE), and analysis (both by hand and using Python).

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 341, 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 fundamental of digital communication and wireless systems. Topics include concepts from information, compression, channel, modulation, radio propagation to principles of wireless cellular, and WiFi systems. At the end of the semester, students are expected to gain a deep understanding of the basis of wireless communication systems and the connection between theoretical concepts and real-world systems.

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

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.

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.

Our goal is to design security into all computers. For modern computer systems, we study cache side-channel attacks, speculative execution attacks like Spectre and Meltdown, zero-day attacks, and their defenses. Topics include the design of secure processors, secure caches, trusted execution environments, anomaly detection, self-protecting data, deep learning for security, and smartphone architecture and security. We study fundamental security concepts like cryptography, security policies and protocols, and how to apply them in secure hardware and system design.

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.

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.

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.

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.

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.

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.

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 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.