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"

An introduction to computer architecture and organization. Instruction set design; basic processor implementation techniques; performance measurement; caches and virtual memory; pipelined processor design; design trade-offs among cost, performance, and complexity.

An introduction to algorithmic techniques for reasoning about software. Basic concepts in logic-based techniques including model checking, invariant generation, symbolic execution, and syntax-guided synthesis; automatic decision procedures in modern solvers for Boolean Satisfiability (SAT) and Satisfiability Modulo Theory (SMT); and their applications in automated verification, analysis, and synthesis of software. Emphasis on algorithms and automatic tools.

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.

This course is an introductory course in programming and computing concepts for engineering students who have little or no experience in computing and programming and are interested in learning programming in the context of a robotic autonomous vehicle system. Introduction to fundamental programming concepts: control flow, iteration, abstraction, sub-routines, functions, recursion, lists and arrays. This course is tightly integrated with a real robotic platform: an autonomous Unmanned Aerial Vehicle which the students will program and fly in lab as they learn programming. ECE 115 can be taken in lieu of COS 126.

Introduction of the basic concepts in logic design that form the basis of computation and communication circuits. This course will start from scratch and end with building a working computer on which we will run small programs.

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.

Intro to fundamentals and operation of semiconductor devices and the micro/nano fabrication technologies used to make them. The devices include field-effect transistors, photodetectors and solar cells, light-emitting diodes and lasers. Applications to be discussed include computing and microchips, optical transmission of info (the internet backbone), displays and renewable energy. Students will fabricate their own devices in a clean room and test them via microprobes. Special emphasis will be placed on the interplay between the material properties, fabrication capabilities, device performance and ultimate system performance.

Machine learning for predictive data analytics; information-based learning; similarity-based learning; probability-based learning; error-based learning; deep learning; evaluation.

This course will introduce the matrix form of quantum mechanics and discuss the concepts underlying the theory of quantum information. Some of the important algorithms will be discussed, as well as physical systems which have been suggested for quantum computing.

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 covers fundamental results in statistical learning theory: 1. Supervised learning: generalization, uniform concentration, empirical risk minimizer, Rademacher complexity, VC theory, reproducing Hilbert kernel space and several applications including neural networks, sparse linear regression, and low-rank matrix problems; 2. Online learning: sequential Rademacher complexity, Littlestone dimension, online algorithms and applications; 3. Unsupervised learning: latent variable models, maximum likelihood estimation, method of moments, tensor methods.

This course is an introduction to the theoretical foundations of machine learning. A variety of classical and recent results in machine learning and statistical analysis are discussed, including: classification, regression, regularization, optimization, gradient descent, neural networks, convolutional networks, and reinforcement learning.

An introduction to the properties of solids. Theory of free electrons--classical and quantum. Crystal structure and methods of determination. Electron energy levels in a crystal: weak potential and tight-binding limits. Classification of solids--metals, semiconductors and insulators. Types of bonding and cohesion in crystals. Lattice dynamics, phonon spectra and thermal properties of harmonic crystals.

The physics and technology of solid-state electronic devices. Covers electronic structure of semiconductors, energy bands and doping, followed by discussion of carrier transport by drift and diffusion and recombination/generation. Detailed analysis of p-n junctions, bipolar transistors and field effect transistors. Survey of a wide range of devices, including photodetectors, solar cells, light-emitting diodes and semiconductor lasers, highlighting contemporary concepts such as thin film electronics and 2D semiconductors.

Fundmentals of light-matter interactions, waveguides and resonators, nonlinear optics and lasers.

This course provides an introduction to the state-of-the-art in photonic technology and systems, focusing on high performance fiber-optic telecommunication systems of silicon photonics. The basic physical principles and performance characteristics of optical fibers, lasers, detectors, optical amplifiers and dispersion management will be discussed. The design and performance analysis of photonic systems will be presented. There will be four participatory lab demonstrations exposing students to the components in a fiber optic communication link. In lieu of a final exam, students do a lab project or term paper, and class presentation.

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 decentralized digital trust engines that are the underlying technology behind Web3, a loosely defined denotation of the Internet architecture in the years to come, including decentralization of the platform economy of the modern Internet (Web2). In this course, we conduct a full-stack study of blockchains, viewing them as a whole integrated computer system involving networking, incentives, consensus, data structures, cryptography and memory management. The course uses the Bitcoin architecture as a basis to construct the foundational design and algorithmic principles of blockchains.

Power electronics circuits are critical building blocks in a wide range of applications, ranging from mW-scale portable devices, W-scale telecom servers, kW-scale motor drives, to MW-scale solar farms. This course is a design-oriented course and will present fundamental principles of power electronics. Topics include: 1) circuit elements; 2) circuit topology; 3) system modeling and control; 4) design methods and practical techniques. Numerous design examples will be presented in the class, such as solar inverters, data center power supplies, radio-frequency power amplifiers, and wireless power transfer systems.

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.

This course covers the principles of quantum mechanics of relevance to students in applied physics, materials science and engineering. Topics include the concept of Hilbert Spaces, Schrodinger and Heisenberg Representations, Bound State and scattering problems in one, two and three dimensions, consequences of symmetry, Angular momentum algebra, Approximation methods for stationary states, Many-body systems, Time dependent Perturbation Theory, and Second Quantization.

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.

This course introduces first year graduate students to the research of the faculty in the area of Computer Engineering and Information Sciences and Systems. It helps first year graduate students find a research advisor.

Introduction to the topics and methods of research in electronic materials and devices, providing an overview of current research of the faculty in electronic materials and devices, and in optical and optoelectronic engineering.

This course is an introduction to the theoretical foundations of machine learning. A variety of classical and recent results in machine learning and statistical analysis are discussed, including: Bayesian classification, regression, regularization, sparse regression, support vector machines, kernels, neural networks and gradient descent.

Graduate level course focusing on theoretical foundations of deep learning with a focus on weakly supervised aspects. The class covers the theoretical analysis tools used in deep learning including stochastic gradient, uniform convergence theory and statistical learning. The course focuses primarily on unsupervised learning and weakly supervised learning. Topics include generative models, self-training, transfer learning, representation learning, semi-supervised learning and other forms of self-supervised learning. Prior knowledge on statistics, linear algebra, probability theory and optimization is required.

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.

Multiple services we use every day, from Zoom to cryptocurrency wallets, rely on large-scale networked systems. These systems consist of a complex series of interdependent components and control algorithms, which make their management particularly challenging. In effect, we often observe disturbances such as cross-layer security vulnerabilities and unpredictable interferences across seemingly independent applications. In this course we revisit a few fundamental network topics (such as routing, monitoring, and congestion avoidance) aiming at revealing and addressing their performance and security implications.

An introduction to organic materials with application to active electronic and photonic devices. Basic concepts and terminology in organic materials, and electronic and optical structure-property relationships. Molecular doping is introduced. Charge transport, light absorption and emission and photoinduced charge transfer are examined as well as interface properties of organic materials. Archetype organic devices such as light emitting diodes, solar cells, photodetectors and transistors are described. We discuss metal halide perovskites, a class of semiconductors currently extensively researched for thin film optoelectronics application.

The course is an introduction course for engineers to understand some fundamental principles, recent advances, and applications in bio-sensing and diagnostics. The topics include biomarkers (small molecules, proteins, and nucleic acids), biomarkers detections, colorimetric assays, immunoassays, nucleic acid hybridization assays, PCR (polymerase chain reaction), microfluidics, microarrays, etc. Applications of engineering and nanotechnology in advancing bio-sensing and diagnostics are addressed.

The course focuses on various aspects of laser spectroscopic sensing. Topics include physical principles of atomic and molecular spectroscopy, fundamentals of high resolution lasers spectroscopy, spectroscopic measurement techniques and instrumentation, laser sources and practical applications of spectroscopic sensing. Example applications of laser spectroscopy to chemical analysis and trace gas detection in fundamental science, industrial and environmental monitoring and medical diagnostics are discussed.

For the past three decades have witnessed an explosion of new forms of optical microscopy that allows us to study living systems with unprecedented details. This course aims to cut through the confusion of the wide array of new imaging methods by offering both a unified theoretical framework and practical descriptions of the pros and cons of each. In addition, this course also explores advances in computational tools, especially recent advances in AI, for image visualization and quantification.

A general introduction to nonlinear optics, including harmonic generation, parametric amplification and oscillation, electro-optic effects, photorefractive materials, nonlinear spectroscopy, and nonlinear imaging.

This course provides an introduction to the state-of-the-art in photonic technology and systems, focusing on high performance fiber-optic telecommunication systems of silicon photonics. The basic physical principles and performance characteristics of optical fibers, lasers, detectors, optical amplifiers and dispersion management will be discussed. The design and performance analysis of photonic systems are presented. There are four participatory lab demonstrations exposing students to the components in a fiber optic communication link. In lieu of a final exam, students do a lab project or term paper, and class presentation.

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.

This course provides an overview of experimental approaches to quantum information processing and quantum computing. We discuss the basic principles of quantum computing to understand the physical requirements, and then survey current research on implementing quantum information processing in various physical systems, in part by reading recent experimental literature. Specific topics covered include gate-based and adiabatic quantum computing, topologically protected quantum architectures, as well as several physical qubit systems: trapped ions/atoms, superconducting circuits, and electron and nuclear spins in solids.

As our society transitions towards an information-driven paradigm, concerns about security and privacy of computing and communication have come to a forefront. This course exposes students to foundational principles and mechanisms that enable security and privacy in computing and communications. In addition, we study the interdisciplinary dimension of security and privacy by exploring its intersections with machine learning and computer networks.

This course aims to cover the fundamentals of the wireless and high-speed integrated circuits for future wireless technology. We cover analysis and design of high-speed and wireless ICs that enables modern wireless communication across device-circuits-system level abstractions. The understanding of these fundamental concepts prepares students for a wide range of advanced topics from circuits and systems for communication to emerging areas of sensing and biomedical electronics.

This course focuses on advanced and emerging topics in wireless systems. It covers millimeter-wave and terahertz communications, reconfigurable radio environments, wireless sensing, communications for robotics, multiple-input multiple output systems, visible light communication, 5G/6G, wireless security. The students develop skills to understand and critically evaluate research advances related to wireless systems. This course is half-lecture half-debate. We first cover the principles of wireless systems and then students read and debate over recent papers published at flagship conferences. The students learn to critically analyze research.

Graduate Project Course. Under the direction of a faculty member the student carries out a graduate-level project and presents their results.

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.

Robotics is a rapidly-growing field with applications including unmanned aerial vehicles, autonomous cars, and robotic manipulators. This course will provide an introduction to the basic theoretical and algorithmic principles behind robotic systems. The course will also allow students to get hands-on experience through project-based assignments on quadrotors. In the final project, students will implement a vision-based obstacle avoidance controller for a quadrotor. Topics include motion planning, control, localization, mapping, and vision.

This course seeks to build a bottom-up understanding of energy transport at small length scales by invoking fundamental principles of quantum mechanics, solid-state physics, and statistical mechanics, and combining them with device-relevant models. Wherever possible, the course makes connections to recent literature to familiarize students with the state-of-the-art and provide exposure to open questions. Topics include kinetic theory, thermal physics, electron transport, Boltzmann transport equation, thermoelectricity, nanoscale thermometry etc., and applications of these concepts to devices.