Graduate Courses and Descriptions | Electrical and Computer Engineering (2024)

Fundamentals of linear system concepts via solution of linear differential and difference equations. State space approach for multi-input multi-output (MIMO) linear systems. Introduction to concepts of linear system stability, controllability, observability, and minimal realization.

Credits: 3

Structure and framework of entrepreneurial endeavors. Phases of a startup, business organization, intellectual property, financing, financial modeling, and business plan writing.

Credits: 3

Fundamentals of object-oriented programming and C++ with an emphasis in numerical computing and computational finance. Design Oriented. Topics include: C++ basics, objected oriented concepts, data structures, algorithm analysis and applications.

Credits: 3

The course will develop skills in designing, programming, and testing self-configurable communication protocols and distributed algorithms for wireless sensor networks enabling environmental, health, and seismic monitoring, surveillance, reconnaissance, and targeting.

Corequisite: 16:332:543
Syllabus: 16:332:504 syllabus

Credits: 3

Review of basic feedback concepts and basic controllers. State space and transfer function approaches for linear control systems. Concepts of stability, controllability, and observability for time-invariant and time-varying linear control systems. Pole placement technique. Full and reduced-order observer designs. Introduction to linear discrete-time systems.

Credits: 3

Review of state space techniques; transfer function matrices; concepts of controllability, observability and identifiability. Identification algorithms for multivariable systems; minimal realization of a system and its construction from experimental data. State space theory of digital systems. Design of a three mode controller via spectral factorization.

Credits: 3

Essential principles, techniques, tools, and methods for systems security engineering. Students work in small collaborative design teams to propose, build, and document a project focused on securing systems. Students document their work through a series of written and oral proposals, progress reports, and final reports. Basics of security engineering, usability and psychology, human factors in securing systems, mobile systems security, intersection of security and privacy, security protocols, access control, password security, biometrics, and topical approaches such as gesture--based authentication.

Credits: 3

Review of linear discrete-time systems and the Z-transform. Sampling of continuous-time liner systems and sampled-data linear systems. Quantization effects and implementation issues. Computer controlled continuous-time linear systems. Analysis and design of digital controllers via the transfer function and state space techniques. Linear-quadratic optimal control and Kalman filtering for deterministic and stochastic discrete-time systems.

Credits: 3

The course develops the necessary theory, algorithms and tools to formulate and solve convex optimization problems that seek to minimize cost function subject to constraints. The emphasis of the course is on applications in engineering applications such as control systems, computer vision, machine learning, pattern recognition, financial engineering, communication and networks.

Credits: 3

Formulation of both deterministic and stochastic optimal control problems. Various performance indices; calculus of variations; derivation of Euler-Lagrange and Hamilton-Jacobi equations and their connection to two-point boundary value problems, linear regulator and the Riccati equations. Pontryagin's maximum principle, its application to minimum time, minimum fuel and "bang-bang" control. Numerical techniques for Hamiltonian minimization. Bellman dynamic programming; maximum principle.

Credits: 3

Nonlinear servo systems; general nonlinearities; describing function and other linearization methods; phase plane analysis and Poincare's theorem. Liapunov's method of stability; Popov criterion; circle criterion for stability. Adaptive and learning systems; identification algorithms and observer theory; input adaptive, model reference adaptive and self-optimizing systems. Estimation and adaptive algorithms via stochastic approximation. Multivariable systems under uncertain environment.

Credits: 3

Response of linear and nonlinear systems to random inputs. Determination of statistical character of linear and nonlinear filter outputs. Correlation functions; performance indices for stochastic systems; design of optimal physically realizable transfer functions. Wiener-Hopf equations; formulation of the filtering and estimation problems; Wiener-Kalman filter. Instabilities of Kalman filter and appropriate modifications for stable mechanization. System identification and modeling in presence of measurement noise.

Credits: 3

This course will introduce students to fundamentals of Cloud Computing Concepts. It will emphasize both in the distributed communication and coordination aspects of clouds and the new parallel and distributed technologies, concepts, techniques, and algorithms that make-up the Cloud Infrastructure as we have built it up or … envision it today. We will investigate more or less in depth how each individual component works and contributes to the Cloud and also how it works in symphony with all the remaining components of the Cloud Framework. We will ask ourselves at the beginning of the class… What is a Cloud? We will attempt to describe. We will ask ourselves again at the end of the course: What is a Cloud after all? And the goal is that everyone now will color this answer with his/her own personal experience on working, touching, approaching, altering some parts of the Cloud!

Credits: 3

This course introduces computing principles in mobile embedded systems and artificial intelligence (AI) technologies on mobile devices. It focuses on emerging computing paradigms in the areas of context-aware pervasive systems, spatiotemporal access control with distributed software agents, mobile sensing, and trust and privacy in mobile environments. It also introduces techniques for implementing AI and developing deep learning models on resource-constrained mobile devices.

Credits: 3

Prerequisites/Corequisites:
Graduate student or undergraduate senior only

Course Description:
High-Performance Computing (HPC) is the ability to perform computations at high speeds. While a desktop that runs at 1 GHz can process calculations of around 1 billion per second, which is already much faster than any human being can achieve, today's supercomputer can process beyond 1 billion billion operations per second. Such remarkable processing speeds have made many game-changing innovations possible and improved the quality of life for billions of people around the globe, e.g., HPC has fueled the groundbreaking revolutions for deep learning. In general, HPC is the foundation for scientific, industrial, and societal advancements. In this course, the instructor will provide the students with knowledge about the recent advances in HPC. We will use a three-pronged approach to teaching this course. First, the instructor will give a presentation to the students about how to read papers, discover new ideas, implement them, write technical papers and present them at international venues. Second, the instructor will create a list of well-known HPC papers for the students to choose as their research projects. Note the students are welcome to bring their own projects to this course. Third, the students must finish their projects via proposal, midterm, and final report/presentations. The instructor will encourage and help the students to publish their findings on international venues.

Topics Covered:
This course covers system optimizations to accelerate deep learning, general machine learning, graph analytics, data mining, algorithms, quantum computing, linear algebra and applications on supercomputers.

Credits: 3

Prerequisite: 16:332:501

Sampling and quantization of analog signals; Z-transforms; digital filter structures and hardware realizations; digital filter design methods; DFT and FFT and methods and their application to fast convolution and spectrum estimation; introduction to discrete time random signals.

Credits: 3

Introduction to robotics; robot kinematics and dynamics. Trajectory planning and control. Systems with force, touch and vision sensors. Telemanipulation. Programming languages for industrial robots. Robotic simulation examples.

Credits: 3

Prerequisite: 16:332:521

Acoustics of speech generation; perceptual criteria for digital representation of audio signals; signal processing methods for speech analysis; waveform coders; vocoders; linear prediction; differential coders (DPCM, delta modulation); speech synthesis; automatic speech recognition; voice-interactive information systems.

Credits: 3

Prerequisites: 16:332:521, 16:642:550, (16:332:535 recommended)

Visual information, image restoration, coding for compression and error control, motion compensation, and advanced television.

Credits: 3

Prerequisite/Corequisite:
Electronic devices

Course Description:
This course seeks to introduce the major biochemical and molecular processes relevant in molecular diagnostics. Additionally, this course provides an understanding of emerging micro- and nanotechnologies for biomarker-based disease diagnosis and gives insight and understanding to participants to quantitatively evaluate and design biosensing solutions in medical diagnostics. The course covers the interface of biology and engineering, in particular microfluidics, sample preparation, and biosensing in current and emerging technologies.

Topics Covered:
Intro to Molecular Biology and Physiology, Intro to Cancer Biology, Traditional Diagnostics, Microfluidics: Hydrodynamic Physics, Mass Transfer Affects and Biosensor Performance Limits, Interfacial Electrochemistry/Electrical Biosensing, In-vitro and In-vivo Bioelectronic Devices and Interfaces, Electronic Biosensors, Noise Analysis, Signal Conditioning, Low-Noise Electronic Circuits for Biosensing, Electric Field/Fluid Interactions: Electrokinetics, Micro/Nanofabrication Techniques, Electrokinetics and Sample Preparation, Nanoelectronic Biosensing Devices, Optical Microscopy and Nanophotonic Micromechanical and Magnetic Sensing Techniques

Credits: 3

This course provides a “mathematical toolkit” for analyzing large-scale signal processing and machine learning algorithms. Topics in this course include: concentration of measure, high-dimensional geometry, packings and coverings, random matrices, random processes, and application

Credits: 3

This graduate-level course teaches multimodal machine learning and sensor data analysis through signal processing, control, and machine learning techniques. Students will gain hands-on experience in filters, time series analysis, and deep learning models for sensor fusion and inference.

Credits: 3

Prerequisites/Corequisites:
Senior and graduate student only
Course: Linear Algebra experience necessary

Course Description:
In computational imaging, optical encoding methods, as done in digital holography or magnetic resonance imaging (MRI), allow us to capture images from the depths of the human body to the far reaches of space. In such systems, the acquired signals are not the desired images, but functions of them, as determined by the optical encoding. Solving the corresponding inverse problems allows us to recover the desired images. Throughout this course, we’ll examine a variety of inverse problems, looking at both classic and modern computational imaging techniques. We’ll dissect the imaging systems tied to these problems and delve into algorithm development for solving them. With machine learning leading the charge in today’s imaging solutions, we will also explore how these techniques are integrated into solving inverse problems.

Topics Covered:
• Introduction to computational imaging and inverse problems
• Compressed sensing
• Complex source structures
• Deep learning for solving inverse problems (End-to-end methods, Unrolled solutions, Iterative solutions)
• Phase retrieval
• Coherent imaging methods

Credits: 3

Prerequisites: 16:332:521 or Permission of instructor. Corequisite: 16:642:550

Wavelets and subband coding with applications to audio, image, and video processing. Compression and communications issues including low-bit-rate video systems. Design of digital filters for systems with 2 or more channels. Matlab and matrix algorithms for analysis, design, and implementation.

Credits: 3

Prerequisites/Corequisites:
Fluency with Probability Theory and Linear Algebra

Course Description:
Probabilistic Graphical Models (PGM) are based on graph, probability, estimation, and information theory, as well as elements of machine learning and offer a fascinating unifying theoretical framework exploited in a rich variety of (challenging) engineering applications, including communications, computer vision, natural language processing, bioinformatics, social networks, and big data analysis. They are particularly useful in problems that can be described as graphs of random variables, and their theory is currently an active topic of research. More specifically, PGMs encode (conditional) dependencies among random variables on carefully crafted graphs. Such description is powerful enough to describe a variety of many famous algorithms, such as (Gaussian) Belief Propagation, Kalman Filtering, Viterbi, Expectation-Maximization (EM). This class offers an introduction in representation with PGMs, algorithms for exact inference, approximate inference, and learning/estimation.

Topics Covered:
Directed acyclic graphs (Bayesian Nets) factorization theorem and semantics (I-map, d-separation, P-map). Undirected graphs (Markov Blanket, Hammersley-Clifford theorem), factor graphs (and techniques to convert), Gaussian Graphical Models. Exact Inference (elimination algorithm, sum-product/belief propagation, max-product on Trees, HMMs and Kalman Filtering, Junction Tree algorithm). Approximate Inference: Loopy Belief Propagation, Sampling Methods (Particle Filtering, Metropolis-Hastings). Intro to learning graphs: ML Techniques, Chow-Liu, BIC-based Techniques, EM. Term projects will thoroughly study application examples in diverse domains.

Axioms of probability; conditional probability and independence; random variables and functions thereof; mathematical expectation; characteristic functions; conditional expectation; Gaussian random vectors; mean square estimation; convergence of a sequence of random variables; laws of large numbers and Central Limit Theorem; stochastic processes, stationarity, autocorrelation and power spectral density; linear systems with stochastic inputs; linear estimation; independent increment, Markov, Wiener, and Poisson processes.

Credits: 3

Prerequisite:16:332:541

Noiseless channels and channel capacity; entropy, mutual information, Kullback-Leibler distance and other measures of information; typical sequences, asymptotic equipartition theorem; prefix codes, block codes, data compression, optimal codes, Huffman, Shannon-Fano-Elias, Arithmetic coding; memoryless channel capacity, coding theorem and converse; Hamming, BCH, cyclic codes; Gaussian channels and capacity; coding for channels with input constraint; introduction to source coding with a fidelity criterion.

Credits: 3

Prerequisite: 14:332:226 or equivalent or 16:332:541 or equivalent

Introduction to telephony and integrated networks. Multiplexing schematics. Circuit and packet

switching networks. Telephone switches and fast packet switches. Teletraffic characterization.. Delay and blocking analysis. Queueing network analysis.

Credits: 3

Prerequisite: 16:332:541

Network and protocol architectures. Layered connection management, including network design, path dimensioning, dynamic routing, flow control, and random access algorithms. Protocols for error control, signaling, addressing, fault management, and security control. This course is intended to provide an in-depth and practical understanding of modern computer networks that constitute the Internet. The scope includes network architecture, key technologies, layer 2 and layer 3 protocols, and examples of specific systems. Emphasis will be on network protocols and related software implementation. The course includes a hands-on “clean-slate” network prototyping project involving specification, standardization and software implementation.

Credits: 3

Prerequisite:16:332:541
Syllabus: 16:332:545 syllabus

Signal space and Orthonormal expansions, effect of additive noise in electrical communications vector channels, waveform channels, matched filters, bandwidth and dimensionality. Digital modulation techniques. Optimum receiver structures, probability of error, bit and block signaling, Intersymbol interference and its effects, equalization and optimization of baseband binary and M-ary signaling schemes; introduction to coding techniques.

Credits: 3

Propagation models and modulation techniques for wireless systems, receivers for optimum detection on wireless channels, effects of multiple access and intersymbol interference, channel estimation, TDMA and CDMA cellular systems, radio resource management, mobility models.

Credits: 3

Application of information-theoretic principles to communication system analysis and design. Source and channel coding considerations, rudiments of rate-distortion theory. Probabilistic error control coding impact on system performance. Introduction to various channel models of practical interest, spread spectrum communication fundamentals. Current practices in modern digital communication system design and operation.

Credits: 3

Prerequisite:16:332:541

Statistical decision theory, hypothesis testing, detection of known signals and signals with unknown parameters in noise, receiver performance and error probability, applications to radar and communications. Statistical estimation theory, performance measures and bounds, efficient estimators. Estimation of unknown signal parameters, optimum demodulation, applications, linear estimation, Wiener filtering, Kalman filtering.

Credits: 3

Prerequisites:14:332:349 and 14:332:450 or equivalent

Cellular mobile radio; cordless telephones; systems architecture; network control; switching; channel assignment techniques; short range microwave radio propagation; wireless information transmission including multiple access techniques, modulation, source coding, and channel coding.

Credits: 3

Prerequisite:16:332:580 or equivalent

Overview of modern microwave engineering including transmission lines, network analysis, integrated circuits, diodes, amplifier and oscillator design. Microwave subsystems including front-end and transmitter components, antennas, radar terrestrial communications, and satellites.

Credits: 3

Prerequisites/Corequisites:
Undergraduate Probability and Linear Algebra

Course Description:
This course teaches students the very basics of quantum information science. Its purpose is to supply the material required to enter the field. This course first addresses the three essential questions of quantum computing: How is quantum information represented? How is quantum information processed? How is classical information extracted from quantum states? It then introduces the most fundamental quantum algorithms and protocols that illustrate the advantages of quantum information processing over the classical.

Topics Covered:
We discuss several basic quantum algorithms that offer computing advantages over their classical counterparts, such as the Deutsch-Jozsa, Bernstein-Vazirani, Simon, Shor factoring, and Grover search algorithms. The class focuses on algorithms for quantum error-correcting codes.

Credits: 3

Prerequisites/Corequisites:
Undergraduate Probability and Linear Algebra

Course Description:
This course focuses on scenarios with multi-party communication and multi-part computing systems.

Topics Covered:
Quantum key distribution and elements of quantum information theory
multi-player games and quantum correlations (e.g., CHSH and monogamy of entanglement)
multi-level and multi-mode representation of quantum information (harmonic oscillator)
Noisy Intermediate-Scale Quantum (NISQ) systems, processing information in multiple hybrid quantum/classical iterations.

Credits: 3

Computer display systems, algorithms and languages for interactive graphics. Vector, curve, and surface generation algorithms. Hidden-line and hidden-surface elimination. Free-form curve and surface modeling. High-realism image rendering.

Credits: 3

Image processing and pattern recognition. Principles of image understanding. Image formation, boundary detection, region growing, texture and characterization of shape. Shape from monocular clues, stereo and motion. Representation and recognition of 3-D structures.

Credits: 3

Advanced visualization techniques, including volume representation, volume rendering, ray tracing, composition, surface representation, advanced data structures. User interface design, parallel and object-oriented graphic techniques, advanced modeling techniques.

Credits: 3

Fundamentals of computer architecture using quantitative and qualitative principles. Instruction set design with examples and measurements of use, basic processor implementation: hardwired logic and microcode, pipelining; hazards and dynamic scheduling, vector processors, memory hierarchy; caching, main memory and virtual memory, input/output, and introduction to parallel processors; SIMD and MIMD organizations.

Credits: 3

Prerequisite:16:332:563

Advanced hardware and software issues in main-stream computer architecture design and evaluation. Topics include register architecture and design, instruction sequencing and fetching, cross-branch fetching, advanced software pipelining, acyclic scheduling, execution efficiency, predication analysis, speculative execution, memory access ordering, prefetch and preloading, cache efficiency, low power architecture, and issues in multiprocessors.

Credits: 3

Prerequisites: 16:332:563

Principles of neural-based computers, data acquisition, hardware architectures for multilayer, tree and competitive learning neural networks, applications in speech recognition, machine vision, target identification and robotics.

Credits: 3

Syllabus16:332:566 syllabus

Introduction to the fundamental of parallel and distributed computing including systems, architectures, algorithms, programming models, languages and software tools. Topics covered include parallelization and distribution models; parallel architectures; cluster and networked meta-computing systems; parallel/distributed programming; parallel/distributed algorithms, data-structures and programming methodologies, applications; and performance analysis. A "hands-on" course with programming assignments and a final project.

Credits: 3

Overview of software development process. Formal techniques for requirement analysis, systemspecification and system testing. Distributed systems. System security and system reliability. Software models and metrics. Case studies.

Credits: 3

The course focus is on Web software design with particular emphasis on mobile wireless terminals. The first part of the course introduces tools; Software component (Java Beans), Application frameworks, Design patterns, XML, Communication protocols, Server technologies, and Intelligent agents. The second part of the course presents case studies of several Web applications. In addition, student teams will through course projects develop components for an XML-Based Web, such as browsers, applets, servers, and intelligent agents.

Credits: 3

Syllabus:16:332:569syllabus

Relational data model, relational database management system, relational query languages, parallel database systems, database computers, and distributed database systems.

Credits: 3

Prerequisite:16:332:561

A toolbox of advanced methods for computer vision, using robust estimation, clustering, probabilistic techniques, invariance. Applications include feature extraction, image segmentation, object recognition, and 3-D recovery.

Credits: 3

Introduction to Virtual Reality. Input/Output tools. Computing architectures. Modeling. Virtual Reality programming. Human factors. Applications and future systems.

Credits: 3

Study of the theory and practice of applied parallel/distributed computing. The course focuses on advanced topics in parallel computing including current and emerging architectures, programming models application development frameworks, runtime management, load-balancing and scheduling, as well as emerging areas such as autonomic computing, Grid computing, pervasive computing and sensor-based systems. A research-oriented course consisting of reading, reviewing and discussing papers, conducting literature surveys, and a final project.

Credits: 3

Syllabus16:332:573syllabus

The objective is to take graduate students in all graduate School of Engineering fields with a good undergraduate data structures and programming background and make them expert in programming the common algorithms and data structures, using the C and C++ programming languages. The students will perform laboratory exercises in programming the commonplace algorithms I C and C++. The students will also be exposed to computation models and computational complexity.

Credits: 3

Advanced computer-aided VLSI chip design, CMOS and technology, domino logic, pre-charged busses, case studies of chips, floor planning, layout synthesis, routing, compaction circuit extraction, multi-level circuit simulation, circuit modeling, fabrication processes and other computer-aided design tools.

Credits: 3

Prerequisite:16:332:574

VLSI technology and algorithms; systolic and wavefront-array architecture; bit-serial pipelinedarchitecture; DSP architecture; transputer; interconnection networks; wafer-cscale integration; neural networks.

Credits: 3

Prerequisite:16:332:563

Testing of Ultra Large Scale Integrated Circuits (of up to 50 million transistors) determines whether a manufactured circuit is defective. Algorithms for test-pattern generation for combinational, sequential, memory, and analog circuits. Design of circuits for easy testability. Design of built-in self-testing circuits.

Credits: 3

Transistor design and chip layout of commonly-used analog circuits such as OPAMPS, A/D and D/A converters, sample-and-hold circuits, filters, modulators, phase-locked loops, and voltage-controlled oscillators. Low-power design techniques for VLSI digital circuits, and system-on-a-chip layout integration issues between analog and digital cores.

Credits: 3

Advanced topics in deep submicron and nanotechnology VLSI design and fabrication. Logic and state machine design for high performance and low power. Tree adders and Booth multipliers. Memory design. Timing testing for crosswalk faults. Design economics. Emergining nanotechnology devices.

Credits: 3

Prerequisites/Corequisites:
Students must have taken undergraduate courses in probability theory and linear algebra. Extensive programming is required for reinforcing concepts introduced in the course, with all programming to be done in notebooks (e.g., Jupyter, Google Colab) using Julia, Python, or R.

Course Description:
This cross-listed course delves into the study of multimodal learning and its applications within IoT and sensor systems. The curriculum encompasses sensor data basics, advanced feature learning, fusion techniques, and practical uses of multimodal learning via neural architectures, sequence transformers, and graph neural networks. Integrating theoretical concepts with hands-on exercises and case studies, culminating in a project presentation, the course aims to furnish students with the necessary expertise to apply machine learning strategies to IoT and sensor data, particularly emphasizing multimodal learning.

Topics Covered:
• Introduction to Multimodal Learning and Sensor Data
• Sensor Data Processing and Time Series Analysis
• Advanced Feature Learning and Sequence Alignment
• Multimodal Learning Fusion Techniques
• Early and Late Fusions in Multimodal Learning
• Multimodal Learning in Distributed Sensing Systems
• Neural Architectures for Multimodal Learning
• Sequence Transformers in Multimodal Learning
• Graph Neural Networks (GNNs) in Multimodal Learning
• Multimodal Learning in Reinforcement Learning
• Transfer Learning and Domain Adaptation in Multimodal Learning
• Transformers and Attention Mechanisms in Multimodal Learning
• Practical Applications of Multimodal Learning with Case Studies

Prerequisites/Corequisites:
Senior level status

Course Description:
This course focuses on the introduction and research discussions of hardware and system security. We will review the state-of-the-art practices and research efforts on hardware and system attacks and countermeasures to motivate research interests and insights in building secure and trustworthy hardware systems.

Topics Covered:
• Physical Unclonable Functions
• Trusted Platform Module
• ARM TrustZone
• Intel SGX
• Hardware Trojans
• Hardware Physical Attacks
• IC Piracy and Logic Locking
• Memory Security
• Architecture Security
• Multimedia Security
• Machine Learning/AI System Security

Credits: 3

Prerequisites:
Digital Logic Design, Digital Logic Design Lab, Basic software programming

Course Description:
The aim of the course is to provide a practical view of building embedded systems through several real examples (hardware and software) with hands on Digilent Zybo Zynq FPGA implementation in labs. The course builds on “Digital Logic Design” and will introduce logic design using hardware description language such as VHDL and will also provide insight into computer architecture. The course will cover the VHDL language in depth and will explain on how to build complex combinational and sequential circuits to be applied on a SoC programmable device.

Topics Covered:
• Fundamentals of embedded systems from both hardware and software perspectives
• Digital circuit design and complex state machines with VHD
• Verification in simulation software and prototype digital designs on an FPGA and using peripheral devices (PMODs)
• Logic simulation and synthesis flows for FPGAs
• Basic aspects of embedded processors (ARM) and Bus Interfaces

Prerequisites/Corequisites:
Prior Python and C programming experience is required Basic knowledge of linear algebra (at the level of 01:640:250 - Introductory Linear Algebra) Basic knowledge of calculus (at the level of 01:640:251 - Multivariable Calculus) Basic knowledge of parallel/distributed programming is recommended Basic knowledge of high-performance computing architecture is recommended Prior CUDA knowledge is recommended.

Course Description:
This is a cross-listed (graduate level course with the focus of system perspectives in distributed deep learning. The goal of this course is to develop comprehensive and deep understanding of internals of deep learning systems to inspire and foster students’ future research direction. This course covers a wide range of topics of neural network architecture, optimization methods, parallel training paradigms, high-performance computing architecture, and communication algorithms. This course conveys the principles of distributed/parallel system design with the state-of-the-art deep learning progress.

Topics Covered:
Deep Learning Overview Distributed Neural Network Training Paradigms First-/second-order optimization of neural network at large scale Memory management in large scale neural network training Performance profiling and modeling Reinforcement learning systems High-performance computing architecture Communication in large-scale neural network training Gradient Sparsification I/O in large-scale neural network training

Prerequisite: A course in elementary electromagnetics

Static boundary value problems, dielectrics, wave equations, propagation in lossless and lossy media, boundary problems, waveguides and resonators, radiation fields, antenna patterns and parameters, arrays, transmit-receive systems, antenna types.

Credits: 3

Introduction to quantum mechanics; WKB method; perturbation theory; hydrogen atom; identical particles; chemical bonding; crystal structures; statistical mechanics; free-electron model; quantum theory of electrons in periodic lattices.

Credits: 3

Charge transport, diffusion and drift current, injection, lifetime, recombination and generation processes, p-n junction devices, transient behavior, FET's, I-V, and frequency characteristics, MOS devices C-V, C-f and I-V characteristics, operation of bipolar transistors.

Credits: 3

Review of microwave devices, O and M-type devices, microwave diodes, Gunn, IMPATT, TRAPATT, etc., scattering parameters and microwave amplifiers, heterostructures and III-V compound based BJT's and FET's.

Credits: 3

This course is designed for the student interested in an overview of the technological methods for obtaining energy from non-renewable and renewable energy sources. The course is divided into three components: Energy Analysis Toolbox, Non-renewable (Fossil) Energy Sources and Renewable Energy Sources.

Credits: 3

Prerequisites/Corequisites:
Electronic devices

Course Description:
This course seeks to introduce the major biochemical and molecular processes relevant in molecular diagnostics. Additionally, this course provides an understanding of emerging micro- and nanotechnologies for biomarker-based disease diagnosis and gives insight and understanding to participants to quantitatively evaluate and design biosensing solutions in medical diagnostics. The course covers the interface of biology and engineering, in particular microfluidics, sample preparation, and biosensing in current and emerging technologies.

Topics Covered:
Intro to Molecular Biology and Physiology, Intro to Cancer Biology, Traditional Diagnostics, Microfluidics: Hydrodynamic Physics, Mass Transfer Affects and Biosensor Performance Limits, Interfacial Electrochemistry/Electrical Biosensing, In-vitro and In-vivo Bioelectronic Devices and Interfaces, Electronic Biosensors, Noise Analysis, Signal Conditioning, Low-Noise Electronic Circuits for Biosensing, Electric Field/Fluid Interactions: Electrokinetics, Micro/Nanofabrication Techniques, Electrokinetics and Sample Preparation, Nanoelectronic Biosensing Devices, Optical Microscopy and Nanophotonic Micromechanical and Magnetic Sensing Techniques

Credits: 3

Design of discrete transistor circuits; amplifiers for L.F., H.F., tuned and power applications biasing; computer-aided design; noise; switching applications; operational amplifiers; linear circuits.

Credits: 3

Design of digital integrated circuits based on NMOS, CMOS, bipolar BiCMOS and GaAs FETs;

fabrication and modeling; analysis of saturating and non-saturating digital circuits, sequential logic circuits, semiconductor memories, gate arrays, PLA and GaAs LSI circuits.

Credits: 3

Basic concepts in RF design, analysis of noise, transceiver architectures, analysis and design of RF integrated circuits for modern wireless communications systems: low noise amplifiers, mixers, oscillators, phase-locked loops.

Credits: 3

Students of the “Socially Cognizant Robotics” course will learn basic principles and state-of-the-art developments of robotics so as to learn the expected trajectory of this technology and its impact on individuals and society. The course is designed for both STEM students as well as computationally-oriented cognitive and social science students. The interdisciplinary curriculum has seven underlying disciplines spanning STEM fields to social and behavioral sciences. It includes traditionally technical disciplines, such as robot embodiment and control, and extends to areas which support human interaction, such as visual learning and language processing, to cognitive modeling, which enables more high level human-robot cooperation, and finally to areas such as behavioral research and public policy. The course will utilize open-source software libraries in robotics, computer vision, and deep learning. Recent innovations at the intersection of deep reinforcement learning and human behavior modeling will be explored in the context of optimizing collaborative robot action.

Credits: 3

Prerequisite:16:332:580

Waveguides and optical filters, optical resonators, principles of laser action, light emitting diodes, semiconductor lasers, optical amplifiers, optical modulators and switches, photodetectors, wavelength- division-multiplexing and related optical devices.

Credits: 3

Prerequisite:16:332:591

Photonic crystals: photonic bandgap, photonic crystal surfaces, fabrication, cavities, lasers, modulators and switches, superprism devices for communications, sensing and nonlinear optics, channel drop filters; advanced quantum theory of lasers: Ferim’s golden for laser transition, noise, quantum well lasers, quantum cascade lasers. Nonlinear optics: parametric amplification, stimulated Raman/Brillouin scattering, Q-switching, mode-locked lasers.

Credits: 3

Prerequisite:16:332:583 or equivalent

Photovoltaic material and devices, efficiency criteria, Schottky barrier, p-n diode, heterojunction and MOS devices, processing technology, concentrator systems, power system designs and storage.

Credits: 3

Prerequisite:16:332:590

Students of the “Design Methods in Socially Cognizant Robotics” course will be exposed to basic principles and state-of-the-art developments of robotics through a hands-on, experiential process. The objective is to learn the expected trajectory of this technology, which will impact individuals and society, and gain the experience of putting together robotics systems that are socially-aware. Learning goals include Develop and utilize socially cognizant design principles, learning to develop and control robotic systems which interact with humans, iplement methods of robot control in the context of human-robot collaboration that emphasizes pro-social performance metrics, developing coding skills in python to integrate vision libraries (opencv), robotics libraries (ROS), or machine learning libraries (pytorch), and demonstrating use of cognitive modeling of human behavior in order to design better collaborative robotic systems that are tuned to human desires and that can be used to learn human intent.

Credits: 3

Prerequisite:16:332:581

Preparation of elemental and compound semiconductors. Bulk crystal growth techniques. Epitaxial growth techniques. Impurities and defects and their incorporation. Characterization techniques to study the structural, electrical and optical properties.

Credits: 3

Prerequisites/Corequisites:
Graduate student or undergraduate senior only

Course Description:
This is an interdisciplinary course that introduces students to the field of biomedical technologies. Students learn fundamental concepts in the areas of bioelectrical engineering, point-of-care sensors, fabrication, micro/ nano technologies, microfluidics, data processing, and global healthcare applications. The course will provide a detailed background on the engineering principles used for biosensor development. Microelectronic sensor fabrication and characterization of the point-of-care biosensors will be taught. The course also will also introduce students to the on-chip sample processing, surface functionalization techniques, label-free detection of biomolecules, electronic instrumentation, and data processing. Course will highlight the development of personalized predictive systems for health care using machine learning techniques. Course also includes case studies of point-of-care sensors. The course is cross listed for senior undergraduates and starting graduate students.

Topics Covered:
• Introduction to unmet needs in the global healthcare and role of biomedical technologies.
• Point-of-care biosensors and intro to micro-nano bio-technologies
• Role of biomarkers in sensing
• Stepwise process for a modular design of a point-of-care (POC) biomedical sensor
• Microfabrication techniques and rapid device prototyping & additive manufacturing
• Introduction to microfluidics
• Unique architecture design for on-chip sample processing and simulations in COMSOL
• Electrical biosensing principles (Electrochemical, conductance, and impedance sensors)
• Instrumentation design and signal/ data processing. ML integration.
• Optical biosensing principles (fluorescence detection & Raman spectroscopy)
• Bio-Instruments: Fluorescence Microscope and Flow Cytometer etc.
• Specific leukocytes capture and counting
• Surface functionalization and proteins quantification
• DNA identification and PCR assays
• Instrumentation design and signal processing
• Biostatistics to evaluate performance of biosensors

Prerequisites/Corequisites:
Electronic Devices or equivalent

Course Description:
The course introduces basic principles governing microelectronic processing technology.

Topics Covered:
Microelectronic Processing covers overview of microelectronic processing technology, including lithography, etching, oxidation, diffusion, implantation and annealing, film deposition, metallization, and integration.

Presentation involving current research given by advanced students and invited speakers. Term papers required.

Credits: 1

Presentation involving current research given by advanced students and invited speakers. Term papers required.

Credits: 1

Presentation involving current research given by advanced students and invited speakers. Term papers required.

Credits: 1

Presentation involving current research given by advanced students and invited speakers. Term papers required.

Credits: 1

Presentation involving current research given by advanced students and invited speakers. Term papers required.

Credits: 1

Prerequisite: Permission of instructor

Investigation in selected areas of electrical engineering.

Research presentations by distinguished lecturers.

Credits: 0

Research supervised by faculty in the Department of Electrical and Computer Engineering.

Typically 1 to 3 credits per semester.