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.

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 operations of semiconductor devices and sensors 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 devises 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.

This course provides the students with a broad and solid background in electromagnetics, 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. Examples include cavities, waveguides, antennas, fiber optic communications, and imaging. There will be several laboratory assignments supporting the material covered in lectures.

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.

The 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.

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.

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.

The lectures will cover: (1) Basic principles of digital signal processing. (2) Design of digital filters. (3) Fourier analysis and the fast Fourier transform. (4) Roundoff errors in digital signal processing. (5) Applications of digital signal processing.

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.

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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.

The course covers the mathematical foundations of dynamical system safety analysis and modern algorithmic approaches for robotic decision making in safety-critical contexts. The focus is on safe robot learning, multiagent systems, and interaction with humans, paying special attention to uncertainty and the reality gap between mathematical models and the physical world.

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.

This debate-style class covers emerging topics in wireless communication, sensing, and security. Example topics include millimeter-wave networking, smart surfaces, wireless sensing & radar, visible light communication, terahertz wireless networks, 5G/6G, wireless security, wireless energy harvesting, and autonomous networked UAVs. The students develop knowledge and skills to understand and critically evaluate research advances in emerging topics related to wireless systems. This course is held in a debate format and students are able to defend and critically analyze research, both in writing and verbally.

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 are discussed. Charge transport, light absorption and emission and photoinduced charge transfer are examined. Interface properties of organic materials are examined. Archetype organic devices such as light emitting diodes, solar cells, photodetectors and transistors are described. We introduce and discuss metal halide perovskites, a class of semiconductors currently extensively researched for thin film optoelectronics application.

This course introduces students to state-of-the-art techniques in spectroscopy and imaging of solid-state quantum materials, including material systems for quantum information processing, topological and 2D materials, and strongly correlated systems. Lectures focus on both theoretical and practical understanding of the primary materials spectroscopy tools, complemented by a literature survey of current topics. Particular emphasis is placed on novel techniques such as nanoscale quantum sensing, low dimensional systems, spectroscopy of nanostructures, and understanding sources of decoherence in quantum information processing platforms.

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.

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.

This course presents advanced topics of power electronics covering advanced design techniques, advanced models, and the interdisciplinary areas of power electronics covering devices, packaging, control, thermal, and magnetics. Example topics include: (1) switched-capacitor circuits; (2) advanced power magnetics design; (3) advanced control methods; (4) very high frequency power electronics; (5) principles of wide bandgap semiconductor devices. In the final project, students perform thorough literature review on recent research frontiers and give a final presentation.

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.

The class reads seminal papers on different parallel programming models and parallel computer architectures. In addition, we explore different parallel programming models via programming assignments. Finally the course culminates in a project where students create a research-grade experiment and write a full length conference-style paper. One of the goals of this class is to get students introduced to writing a complete conference style computer architecture/CS paper.

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 covers several topics on statistical machine learning. Topics include (1) Robust covariance regularizations and graphical model. (2) Factor models and their applications. (3) Matrix completion. (4) Graphical clustering and community detection. (5) Item ranking. (6) Deept Neurlal network. Students are expected to participate in paper surveying and presentation.