120 courses with the subject ESE, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
ESE 0099Undergraduate Research and/or Independent Study
An opportunity for the student to become closely associated with a professor in (1) a research effort to develop research skills and technique and/or (2) to develop a program of independent in-depth study in a subject area in which the professor and student have a common interest. The challenge of the task undertaken must be consistent with the student's academic level. To register for this course, the student and professor jointly submit a detailed proposal to the undergraduate curriculum chairman no later than the end of the first week of the term.
Primer on digital audio. Overview of signal processing, sampling, compression, human psychoacoustics, MP3, intellectual property, hardware and software platform components, and networking (i.e., the basic technical underpinnings of modern MP3 players and cell phones).
ESE 2030Linear Algebra with Applications to Engineering and AI
This first course in Linear Algebra will introduce students to key concepts of the field, including but not limited to vectors, vector norms and inner s products, matrices, matrix-vector and matrix-matrix multiplication, matrix inverses, solving systems of linear equations, vector spaces, orthogonality, least-squares, eigenvalues and eigenvectors, singular value decompositions, and principal component analysis. These theoretical tools will be grounded in exciting problems from the sciences, engineering, machine learning, data science, logistics, and economics. Through application-based case studies, you will be shown how to model problems using linear algebra and how to solve the resulting problem using standard Python scientific computing modules. Enrollment in this course assumes students have comfort with programming at the level of
This first course in decision making will introduce you to quantitative models for decision and design in the sciences, engineering, machine learning, data science, logistics, and economics. Through application- based case studies, you will be shown how to (i) formalize a decision problem as a mathematical optimization problem, and (ii) solve the resulting optimization problem using Python scientific computing modules. You will also be given a brief introduction to the optimization algorithms and programming tools underpinning contemporary deep learning and shown how to apply them to decision and design problems. Not Offered Every Year 1 Course Unit
Students must take MATH 1400 before taking ESE 2040.
ESE 2100Introduction to Dynamic Systems
This first course in systems modeling covers linear and nonlinear systems in both continuous and discrete time. Topics covered include linearization and stability analysis, elementary bifurcations, and an introduction to chaotic dynamics. Extensive applications to mechanical, electrical, biological, social, and economic/financial systems are included. The course will use both analytical and numerical/symbolic tools.
ESE 2180Electronic, Photonic, and Electromechanical Devices
This first course in electronic, photonic and electromechanical devices introduces students to the design, physics and operation of physical devices found in today's applications. The course describes semiconductor electronic and optoelectronic devices, including light- emitting diodes, photodetectors, photovoltaics, transistors and memory; optical and electromagnetic devices, such as waveguides, fibers, transmission lines, antennas, gratings, and imaging devices; and electromechanical actuators, sensors, transducers, machines and systems. ESE 1120 is a prerequisite for this course, but students passing the ESE E&M review module may substitute an ESE approved E&M course.
& ESE 2910 Engineering Research Methodology and Introduction to Electrical and Systems Engineering Research and Design or ESE 3190 Fundamentals of Solid-State Circuits or ESE 3360 Nanofabrication of Electrical Devices or ESE 3500 Embedded Systems/Microcontroller Laboratory or ESE 4210 Control For Autonomous Robots or BE 4700 Medical Devices
ESE 2910Introduction to Electrical and Systems Engineering Research
and Design Students contract with a faculty mentor to conduct scaffolded original research in a topic of mutual interest. Prepare project report on research findings. Not Offered Every Year 1 Course Unit
Students must take ESE 2900 before taking ESE 2910.
ESE 2920Invention Studio
This is a project-centric course for ESE majors to engage in circuit layout and prototype design skills. Students will work in teams to develop printed circuit boards using industry standard tools like Altium and learn mechanical prototyping skills using Solidworks . Emphasis will be on developing sound printed circuit board layout practices using circuitry knowledge that they acquire in ESE 2150 and ESE 3700. A module on using Cypress PSoC will introduce students to recent developments in analog/digital co-design.
ESE 3030Stochastic Systems Analysis and Simulation
This analysis is usually complemented with numerical analysis of experimental outcomes.This class covers topics in probability and random processes, Markov chains, Poisson processes, stationary and Gaussian processes. Besides the theoretical toolbox that we build, we explore applications in communication networks, search engines, deciphering algorithms, molecular biology and more.
This course is an introduction to linear optimization and its extensions emphasizing the underlying mathematical structures, geometrical ideas, algorithms and solutions of practical problems. The topics covered include: formulations, the geometry of linear optimization, duality theory, the simplex method, sensitivity analysis, robust optimization, large scale optimization network flows, solving problems with an exponential number of constraints and the ellipsoid method, interior point methods, semidefinite optimization, solving real world problems problems with computer software, discrete optimization formulations and algorithms.
Introduction to a broad range of tools to analyze large volumes of data in order to transform them into actionable decisions. Using case studies and hands-on exercises, the student will have the opportunity to practice and increase their data analysis skills. Not Offered Every Year 1 Course Unit
ESE 3200Qubit Lab – A Hands on Introduction to Quantum Devices
Quantum technologies are rapidly evolving and there are many competing physical systems being used to construct the fundamental unit of quantum information – the qubit. In this course we will discuss several of the key technologies used to build quantum processors. Students will also be exposed to applications for quantum devices beyond computing including environmental sensing. Students will participate in a weekly lab including an introduction to instrumentation and the subsequent programming and control of a single qubit register based on a tabletop NMR spectrometer. Students will also gain access to a cloud-based superconducting quantum computer, which will be used to perform pulse- level experiments on few qubit systems.
This course is an intermediate undergraduate course in the , understanding, fabrication, and characterization of electrical, optical, electromagnetic, and/or electromechanical nanodevices; i.e., micro- and nanoscale devices which have significant relevance to electrical engineering. Example devices of interest include transistors, microelectromechanical systems (MEMS), and optical and optoelectronic devices (including photovoltaic devices). Weekly laboratory sessions will enable the fabrication and characterization of a subset of electrical nanodevices. Students will learn basic physics and modeling of electrical nanodevices as well as acquire hands-on skill in their fabrication and characterization. Prerequisite: If course requirements not met, permission of instructor required.
With the demand for personalized medicine and health care, the need for consumer medical devices has risen. Traditionally devices have been designed from the ground up, but with more standardized components and software tools devices can be built to fulfill this need. This course will introduce design of medical devices. Students will learn the basics of sensors, signal conditioning, data acquisition and analysis, biopotential, biopotential electrodes, biomedical instrumentation, examples of biological signal measurement and electronics safety. This will be a lab based inquiry into medical device design. Prerequisites: Some exposure to circuit/electronics; Calculus and familiarity with signals 1 Course Unit
ESE 3500Embedded Systems/Microcontroller Laboratory
An introduction to interfacing real-world sensors and actuators to embedded microprocessor systems. Concepts needed for building electronic systems for real-time operation and user interaction, such as digital input/outputs, interrupt service routines, serial communications, and analog-to-digital conversion will be covered. The course will conclude with a final project where student-designed projects are featured in presentations and demonstrations. Prerequisite: Prior programming experience in any language
ESE 3600TinyML: Tiny Machine Learning for Embedded Systems
Tiny Machine Learning for Embedded Systems is a cutting-edge field that brings the transformative power of machine learning (ML) to the performance-constrained and power-constrained domain of embedded systems to develop useful and exciting Internet of Things solutions. This is an introductory course at the intersection of Machine Learning (ML) and Embedded Internet of Things (IoT) Devices which covers machine learning applications and algorithms using embedded hardware, sensors, actuators and software. Embedding machine learning in a device at the extreme end point - right at the data source - is fundamentally different from general data-center style machine learning. Embedded ML is all about real-time processing of time-series data that comes directly from sensors. By the end of this course, students will collect and preprocess data to build a dataset, design a model, train a model, evaluate and optimize the pipeline, convert the model to run on hardware, deploy the model on a microcontroller, make inference and roll out applications. This will enable future applications development across medical devices, home appliances, industrial automation, wild-life conservation, smart agriculture and many more. Prerequisites: Basic knowledge of programming (CIS1100 or equivalent) and basic knowledge of Python and basic knowledge of electronics and circuits. We provide the background, tools and assignments for machine learning and embedded systems using TensorFlow, Google Colab, and ARM Cortex32 hardware platforms. 1 Course Unit
The course covers the methodological foundations of network formation and utilization. It introduces various mathematical models for random and strategic, static and dynamic formation of networks. The models for random static formation span, Erdos Renyi Graphs and Power law topologies. Threshold properties underlying these formations will be rigorously proved. The dynamic formations will introduce mean field based deterministic models for network evolution. Techniques for approximately analyzing various key network features such as component sizes will be introduced. These analyses will culminate in tools for approximate analysis of efficacy of various immunization strategies considering epidemic disease spread over networks. A solid background in undergraduate probability is required (e.g. ESE 3010, STAT 4300, ENM 3210, CIS 2610 or equivalent).
This is an introductory course on packet networks and associated protocols, with a particular emphasis on IP-based networks such as the Internet. The course introduces design and implementation choices that underlie the development of modern networks, and emphasizes basic analytical understanding of the concepts. Topics are covered in a mostly "top down" approach starting with web HTTP protocol followed by transport layer protocols such as TCP and UDP. Congestion control of TCP is extensively covered. Network layer solutions, including IP addressing and routing are covered next, before exploring link layer solutions including multiple access strategies, local area networks (Ethernet and 802.11). The objectives of the course include basic understanding of the network protocol stack and hands-on experience analyzing protocol behavior using wireshark.
Design of analog circuits and subsystems using primarily MOS technologies at the transistor and higher levels. Transistor level design of building block circuits such as op amps, current references, capacitors and resistor and class AB output stages. The Cadence Design System will be used to capture schematics and run simulations using Spectre for some homework problems and for the course project. Topics of frequency response, stability, noise, and device matching through good layout practice will also be covered. Students who take ESE 4190 will not be able to take ESE 5720 later. Prerequisite: If course requirement not met, permission of instructor required.
Quantum engineering - the design, fabrication, and control of quantum coherent devices - has emerged as a multidisciplinary field spanning physics, electrical engineering, materials science, chemistry, and biology, with the potential for transformational advances in computation, secure communication, and nanoscale sensing. This course surveys the state of the art in quantum hardware, beginning with an overview of the physical implementation requirements for a quantum computer and proceeding to a synopsis of the leading contenders for quantum building blocks, including spins in semiconductors, superconducting circuits, photons, and atoms. The course combines background material on the fundamental physics and engineering principles required to build and control these devices with readings drawn from the current literature, including promising architectures for scaling physical qubits into larger devices and secure communication networks, and for nanoscale sensing applications impacting biology, chemistry, and materials Prerequisite: If course requirement not met, permission of instructor required. Not Offered Every Year Mutually Exclusive: ESE 5230 1 Course Unit
This is the first of a two-semester sequence in electrical and systems engineering senior design. Student work will focus on project/team definition, systems analysis, identification alternative design strategies and determination (experimental or by simulation) or specifications necessary for a detailed design. Project definition is focused on defining a product prototype that provides specific value to a least one identified user group. Students will receive guidance on preparing professional written and oral presentations. Each project team will submit a project proposal and two written project reports that include coherent technical presentations, block diagrams and other illustrations appropriate to the project. Each student will deliver two formal Powerpoint presentations to an audience comprised of peers, instructors and project advisors. During the semester there will be periodic individual-team project reviews.
This hands-on course covers advanced topics in chip packaging, characterization, and measurements, including DC characterization, time domain measurements, frequency domain measurements, and small and large signal measurements as needed. This is a follow up course to
Addressing today's energy and environmental challenges requires efficient energy conversion techniques. This course will discuss the circuits that efficiently convert ac power to dc power, dc power from one voltage level to another, and dc power to ac power. The lecture will discuss the components used in these circuits (e.g., transistors, diodes, capacitors, inductors) in detail to highlight their behavior in a practical implementation. In addition, the class will have lab sessions where students will obtain hands-on experience with power electronic circuits. Students should have taken an introductory circuits course like ESE 2150 or equivalent.
Networks constitute an important component of modern technology and society. Networks have traditionally dominated communication technology in form of communication networks, distribution of energy in form of power grid networks, and have more recently emerged as a tool for social connectivity in form of social networks. In this course, we will study mathematical techniques that are key to the design and analysis of different kinds of networks. First, we will investigate techniques for modeling evolution of networks. Specifically, we will consider random graphs (all or none connectivity, size of components, diameters under random connectivity), small world problem, network formation and the role of topology in the evolution of networks. Next, we will investigate different kinds of stochastic processes that model the flow of information in networks. Specifically, we will develop the theory of markov processes, renewal processes, and basic queueing, diffusion models, epidemics and rumor spreading in networks.
This course provides a study of discrete-event systems simulation in the areas of queuing, inventory and reliability systems as well as Markov Chains, Random-Walks and Monte-Carlo systems. The course examines many probability distributions used in simulation studies as well as the Poisson process. Fundamental to most simulation studies is the ability to generate reliable random numbers and so the course investigates the basic properties of random numbers and techniques used for the generation and testing of pseudo-random numbers. Random numbers are then used to generate other random variable using the methods of inverse-transform, convolution, composition and acceptance/rejection. Finally, since most inputs to simulation are probabilistic instead of deterministic in nature, the course examines some techniques used for identifying the probabilistic nature of input data. These include identifying distributional families with sample data, using maximum- likelihood methods for parameter estimating within a given family and testing the final choice of distribution using chi-squared goodness-of-fit.
Introduction to mathematical optimization for graduate students who would like to be intelligent and sophisticated users of mathematical programming but do not necessarily plan to specialize in this area. Linear, integer and nonlinear programming are covered, including the fundamentals of each topic together with a sense of the state-of-the- art and expected directions of future progress. Homework and projects emphasize modeling and solution analysis, and introduce the students to a large variety of application areas.
This is an introductory course on packet networks and associated protocols, with a particular emphasis on IP-based networks such as the Internet. The course introduces design and implementation choices that underlie the development of modern networks, and emphasizes basic analytical understanding of the concepts. Topics are covered in a mostly "bottom-up" approach starting with a brief review of physical layer issues such as digital transmission, error correction and error recovery strategies. This is followed by a discussion of link layer aspects, including multiple access strategies, local area networks (Ethernet and 802.11 wireless LANs), and general store-and-forward packet switching. Network layer solutions, including IP addressing, naming, and routing are covered next, before exploring transport layer and congestion control protocols (UDP and TCP). Finally, basic approaches for quality-of-service and network security are examined. Specific applications and aspects of data compression and streaming may also be covered.
Quantum information processing promises new paradigms in secure communication, powerful new simulation techniques, and exponential speedups over classical techniques for a select range of problems. This course will cover the basics of quantum mechanics and introduce students to a circuit-based model for quantum computing. In the course, several of the key algorithms that have motivated the pursuit of large- scale universal quantum computers will be explored. The scalability of quantum computers from a circuits perspective will be covered including error correction techniques. Students will also gain hands-on experience in programming cloud-based quantum computers. Students should have previously taken an undergraduate course in linear algebra such as MATH 2400 or ESE 2240.
ESE 5120Dynamical Systems for Engineering and Biological Applications
This midlevel course in nonlinear dynamics focuses on the analysis of low dimensional, continuous time models for describing and understanding complex behavior in physical, biological and engineered systems. We assume some background knowledge of ordinary differential equations, and develop at an engineering applications level the concepts and tools of qualitative dynamical systems theory with major focus on analysis and some on synthesis. Not Offered Every Year 1 Course Unit
Graph Neural Networks (GNNs) are information processing architectures for signals supported on graphs. They have been developed and are presented in this course as generalizations of the convolutional neural networks (CNNs) that are used to process signals in time and space. The focus of this course is in large scale problems involving high dimensional signals. In these settings fully connected neural networks fail to scale. CNNs are the tool for enabling scalable learning for signals in time and space. GNNS are the tool for enabling scalable learning for signals supported on graphs.
The course is designed to introduce sensors and their networks and systems that are increasingly pervasive and form the physical device layer of the Internet of Things. Sensors transduce input signals into measured outputs within and between chemical, thermal, mechanical, optical, electrical, and magnetic domains. The course will describe the physical principles of operation, the characteristics, and the figures of merit of different sensors and their integration in networks and systems, highlighting common electronic interfaces that are used. The sensors and systems will be described as case studies to show how these devices are used to monitor and regulate processes in applications in agriculture, the environment, the home, manufacturing, health, transportation, and human activity. The course is structured with a combination of lectures, in-class and at-home labs, and research paper reading/in-class discussion.
This course was developed to bring lessons learned from the product design industry into the classroom - specifically focusing on Internet of Things (IoT) device development and deployment. To achieve the highest level of knowledge transfer, the course will incorporate device design theory with discussions of real-world product failures and successes - as well as a heavy hands-on component to build a device from end to end. Students will learn to use industry standard tools, such as Altium, Atmel Studio, and IBM Watson - allowing them the same level of power and customization at the disposable of startups and Fortune 500 companies alike. Prerequisite: If course requirement not met, permission of instructo required.
ESE 5180 answers the questions engineers have about scaling from a single prototype up to thousands of devices in the field. This class will cover automating build systems with Continuous Integration (CI), investigating various wireless protocols, integrating IoT security, and device fleet management. A final project will show the intersection of technical design with business planning in order to launch a device.
An embedded system is the product of a marriage between hardware and software. Embedded systems have grown to be ubiquitous in the modern world - from simple temperature controlled kettles to intricate smart watches with a plethora of functions squeezed into one small package to complex rovers for space exploration. This course introduces the theory and practice of developing embedded systems through exploration of modern microcontroller architectures and culminates in a final project where students have the opportunity to synthesize and apply their knowledge in a project of their own design. Previous programming experience (Preferably C); Some exposure to circuit/ electronics; Undergraduates who have taken ESE 3500 are not permitted to take this course.
An advanced undergraduate course or graduate level course on the fundamental physical principles underlying the operation of traditional semiconducting electronic and optoelectronic devices and extends these concepts to novel nanoscale electronic and optoelectronic devices. The course assumes an undergraduate level understanding of semiconductors physics, as found in ESE 2180 or PHYS 1240. The course builds on the physics of solid state semiconductor devices to develop the operation and application of semiconductors and their devices in energy conversion devices such as solar photovoltaics, thermophotovoltaics, and thermoelectrics, to supply energy. The course also considers the importance of the design of modern semiconductor transistor technology to operate at low-power in CMOS. Prerequisite: If course requirement not met, permission of instructor required.
Statistical decision making constitutes the core of multiple engineering systems like communication, networking, signal processing, control, market dynamics, biological systems, data processing, etc. We strive to introduce mathematical theories that formulate statistical decision and obtain decision making algorithms with application to one or more of the above domains. This course will be offered every other year.
This rapidly moving course provides a rigorous development of fundamental ideas in probability theory and random processes. The course is suitable for students seeking a rigorous graduate level exposure to probabilistic ideas and principles with applications in diverse settings. The topics covered are drawn from: abstract probability spaces; combinatorial probabilities; conditional probability; Bayes's rule and the theorem of total probability; independence; connections with the theory of numbers, Borel's normal law; rare events, Poisson laws, and the Lovasz local lemma; arithmetic and lattice distributions arising from the Bernoulli scheme; limit laws and characterizations of the binomial and Poisson distributions; continuous distributions in one and more dimensions; the uniform, exponential, normal, and related distributions; random variables, distribution functions; orthogonal and stationary random processes; the Gaussian process, Brownian motion; random number generation and statistical tests of randomness; mathematical expectation and the Lebesgue theory; expectations of functions, moments, convolutions; operator methods and distributional convergence, the central limit theorem, selection principles; conditional expectation; tail inequalities, concentration convergence in probability and almost surely, the law of large numbers, the law of the iterated logarithm; Poisson approximation, Janson's inequality, the Stein- Chen method; moment generating functions, renewal theory; characteristic functions.
Motivation, design, programming, optimization, and use of modern System-on-a-Chip (SoC) architectures. Hands-on coverage of the breadth of computer engineering within the context of SoC platforms from gates to application software, including on-chip memories and communication networks, I/O interfacing, RTL design of accelerators, processors, concurrency, firmware and OS/infrastructure software. Formulating parallel decompositions, hardware and software solutions, hardware/ software tradeoffs, and hardware/software codesign. Attention to real- time requirements. Undergraduates: CIS 240, ESE 350; Graduate: Working knowledge of C.
Stochastic modelling and analysis is key in understanding physical phenomena as well as designing new systems and quantifying various trade-offs and aspects of those designs. The course develops the foundations of stochastic processes and aims to provide engineering students with a mathematical, yet intuitive, toolbox to work with random processes. Topics covered include random walks, counting processes, renewal processes, Markov models and Markov decision processes, and martingales. Tools and techniques studied in this class are at the core of various fields ranging from engineering to social sciences and biology. Solid background in probability, preferably advanced probability, is required (e.g. ESE 3010 or equivalent). Some calculus and linear algebra will be needed (e.g. MATH 1040 and MATH 2400) Not Offered Every Year 1 Course Unit
Formulation, automation, and analysis of design mapping problems with emphasis on VLSI and computational realizations. Major themes include: formulating and abstracting problems, figures of merit (e.g. Energy, Delay, Throughput, Area, Mapping Time), representation, traditional decomposition of flow (logic optimization, covering, scheduling, retiming, assignment, partitioning, placement, routing), and techniques for solving problems (e.g., greedy, dynamic programming, search, (integer) linear programming, graph algorithms, randomization, satisfiability). Digital logic, Programming (need to be Not Offered Every Year 1 Course Unit
Modern computing devices and infrastructure manage and mediate critical systems and important information. How do we assure that these systems are available when we need them, are used only as intended, and only allow changes and disclosure of data as intended? Security is a cross-layer issue spanning the hardware-software stack for both attacks and protection. The root of many security vulnerabilities, as well as many potential solutions to address them, lie in the design of the hardware that supports the systems. This course reviews attacks and vulnerabilities and techniques to address them. It lays the groundwork to systematically address security from the hardware up. It reviews traditional challenges (e.g. buffer overflow, control flow hijacking), information leakage (e.g. timing, power consumption, RF emissions), emerging side-channel leakage (e.g. SPECTRE/Meltdown), and physical attacks (e.g., RowHammer, power, cryogenic) as well as well as various approaches to address them (e.g., Virtual Memory, Virtual Machines, capabilities, tagging, obfuscation, encryption). Concerns and solutions will include processor design, as well as custom hardware, networking, systems, and SoCs. Recommended prerequisites: CIS 4480/5480 and CIS 4710/5710
This course is an introduction to human systems engineering, examining the various human factors that influence the spectrum of human performance and human systems integration. We will examine both theoretical and practical applications, emphasizing fundamental human cognitive and performance issues. Specific topics include: human performance characteristics related to perception, attention, comprehension, memory, decision making, and the role of automation in human systems integration.
ESE 5450Data Mining: Learning from Massive Datasets
Many scientific and commercial applications require us to obtain insights from massive, high-dimensional data sets. In this graduate-level course, students will learn to apply, analyze and evaluate principled, state-of- the-art technique s from statistics, algorithms and discrete and convex optimization for learning from such large data sets. The course both covers theoretical foundations and practical applications. Not Offered Every Year 1 Course Unit
This course reviews three decades' development of agile legged machines, treating past and recent advances as well as remaining formidable challenges in the materials selection, design, and programming of robots that can run, leap and climb through complicated, unstructured terrain. Emphasis is on developing understanding of and facility using key dynamical primitives whose composition allows more complicated behaviors to emerge from simpler constituents. Several historical case studies will be used to illustrate how advances have rewarded interdisciplinary thinking about animals, materials, mathematics and mechatronics. Course credit will be based on problem sets and coding exercises. 1 Course Unit
This course introduces students to the development and uses of the 4- step urban transportation model (trip generation-trip distribution-mode choice-traffic assignment) for community and metropolitan mobility planning. Using the VISUM transportation desktop planning package, students will learn how to build and test their own models, apply them to real projects, and critique the results. Prerequisite: CPLN 5050 or other planning statistics course.
This course will cover advanced topics in transportation planning and systems analysis. Through the lens of air transportation, rail transportation, logistics, and safety, students will learn and engage in concepts in economics and behavioral modeling, operations research, statistics, environmental planning, and human factors that are used in transportation systems. Topics will include airport and intercity multimodal planning, transportation-environmental planning, network design and reliability, disaster recovery and recovery from irregular operations, scheduled transportation operations, economics, fuel, transportation sustainability, and land use issues related to transportation systems. The course will emphasize learning through lessons, guest lecturers, case studies and an individual group and research project.
The human brain produces complex functions using a range of system components over varying temporal and spatial scales. These components are couples together by heterogeneous interactions, forming an intricate information-processing network. In this course, we will cover the use of network science in understanding such large- scale and neuronal-level brain circuitry. Prerequisite: Graduate standing or permission of the instructor. Experience with Linear Algebra and MATLAB.
Climate change represents one of the most urgent threats to humanity’s future. Transforming the global economy to manage this threat will require trillions of dollars in capital, creating unprecedented risks as well as opportunities in financial markets. This course uses the tools of financial economics to understand strategies for managing risks and financing climate technologies across a range of asset classes, including carbon markets, project finance, venture capital, private equity, public equities, fixed income, and real assets. Students will also explore how financing strategies interact with public policy and political risk in both the developed and emerging market contexts. The course concludes with debates on corporate purpose, including what role businesses and financial institutions should play in addressing climate change.
ESE 5700Digital Integrated Circuits and VLSI-Fundamentals
Explores the design aspects involved in the realization of an integrated circuit from device up to the register/subsystem level. It addresses major design methodologies with emphasis placed on the structured design. The course includes the study of MOS device characteristics, the critical interconnect and gate characteristics which determine the performance of VLSI circuits, and NMOS and CMOS logic design. Students will use state-of-the-art CAD tools to verify designs and develop efficient circuit layouts.
ESE 5740/4740 where students designed and taped-out a chip. In this
course, students will design PCBs and mount their packaged chip on the designed PCB and conduct characterization and measurements. The proposed chip systems will be demonstrated at the end of the course and the final report will be submitted.Students are welcome to use this course to design a chip towards their senior design project or master’s thesis (in coordination with their advisor).
This hands-on course covers advanced topics in chip packaging, characterization, and measurements, including DC characterization, time domain measurements, frequency domain measurements, and small and large signal measurements as needed. This is a follow up course to
ESE 5760Semiconductor Memory Devices and Circuit Design
Computing system performance and energy efficiency is heavily dependent upon memory subsystem characteristics. This course introduces students to various semiconductor memories including: (1) Ubiquitous technologies such as static random-access memory (SRAM), dynamic RAM (DRAM), and Flash. (2) Emerging and recently commercialized non-volatile memories (NVMs) built using resistive (RRAM), conductive bridge (CBRAM), phase change (PCRAM), magnetoresistive (MRAM), and ferroelectric (FeRAM) devices. (3) Other memory devices including ferroelectric transistors (FeFET), ferrodiodes (FeD), and semivolatile gain cells (GC). The course will cover memory design considerations, including memory device, array, and peripheral circuit operation, co-design, scaling and fabrication, and system-level memory optimizations. The course will also give an overview of recent progress in memory-centric design (such as near- and in-memory computing) using these technologies, with a focus on machine learning applications. Recommended Prerequisite: ESE 2180 or ESE 5720. ESE 5760. 1 Course Unit
Students must take ESE 3700 or ESE 5700 before taking
ESE 5780RFIC (Radio Frequency Integrated Circuit) Design
Introduction to RF (Radio Frequency) and Microwave Theory, Components, and Systems. The course aims at providing knowledge in RF transceiver design at both microwave and millimeter-wave frequencies. Both system and circuit level perspective will be addressed, supported by modeling and simulation using professional tools (including Agilent ADS, Sonnet, and Cadence Design Systems). Topics include: Transmission Line Theory, S-parameters, Smith Chart for matching network design, stability, noise, and mixed signal design. RF devices covered will include: hybrid/Wilkinson/Lange 3dB couplers, Small Signal Amplifiers (SSA), Low Noise Amps (LNA), and Power Amps (PA). CMOS technology will be largely used to design the devices mentioned.
Addressing today's energy and environmental challenges requires efficient energy conversion techniques. This course will discuss the circuits that efficiently convert ac power to dc power, dc power from one voltage level to another, and dc power to ac power. The lecture will discuss the components used in these circuits (e.g., transistors, diodes, capacitors, inductors) in detail to highlight their behavior in a practical implementation. In addition, the class will have lab sessions where students will obtain hands-on experience with power electronic circuits. Students should have taken an introductory circuits course like ESE 2150 or equivalent.
The course will cover polyhedral theory, structural results and their applications to designing algorithms. Specific topics to be covered include: matchings and their applications, connectivity properties of graphs, matroids and optimization including matroid intersection and union, submodular set functions and applications, arborescences and branchings. ESE 6060. 1 Course Unit
Students must take ESE 5060 or ESE 6050 before taking
ESE 6110Nanophotonics: Light at the Nanoscale
This course is intended for first and second year graduate students interested in nanoscale optics and photonics. Building on prior coursework in electromagnetism, this course provides a theoretical foundation and up-to-date survey of the key principles and phenomena relevant to the field of nanophotonics. Topics discussed include light- matter interaction through Maxwell's equations, photonic band theory and photonic crystals, plasmonic structures and devices, metamaterials and metasurfaces, PT-symmetric & topological photonic systems. Applications of nanophotonic devices and principles to a wide range of scenarios will also be explored in depth, including for renewable energy, information processing, imaging and sensing. Experimental techniques used in nanophotonics will be concurrently introduced and discussed. Not Offered Every Year 1 Course Unit
This hands-on, lab-centered course is for senior undergraduates and graduate students interested in the fields of artificial perception, motion planning, control theory, and applied machine learning. It is also for students interested in the burgeoning field of autonomous driving. This course introduces the students to the hardware, software and algorithms involved in building and racing an autonomous race car. Every week, students take two lectures and complete an extensive hands-on lab. By Week 6, the students will have built, programmed and driven a 1/10th scale autonomous race car. By Week 10, the students will have learned fundamental principles in perception, planning and control and will race using map-based approaches. In the last 6 weeks, they develop and implement advanced racing strategies, computer vision and machine learning algorithms that will give their team the edge in the race that concludes the course. Prerequisites: C++ and Python programming, Matrix algebra, Differential equations, Signals and Systems
The course provides a basic understanding of nonlinear systems , phenomena and studies analysis and control design problems of nonlinear systems. The main analysis tools that will be presented are Lyapunov theory for stability, including the well known LaSalle's invariance principle, and barrier function theory for safety of both autonomous and non-autonomous systems. Further topics include input-output stability, passivity, and the center manifold theorem. The main control tools that will be presented are feedback linearization, backstepping, as well as recent results on learning control Lyapunov and control barrier functions from data. Examples will be taken from mechanical and robotic systems. Not Offered Every Year Also Offered As: MEAM 6130 1 Course Unit
This course will provide students an introduction to the emerging area at the intersection of machine learning, dynamics, and control. We will investigate machine learning and data-driven algorithms that interact with the physical world, with an emphasis on a holistic understanding of the interplay between concepts from control theory (e.g., feedback, stability, robustness) and machine learning (e.g., generalization, sample- complexity). Topics of study will include learning models of dynamical systems, using these models to robustly meet performance objectives, optimally refining models to improve performance, and verifying the safety of machine learning enabled control systems. The course will also expose students to the ethical considerations that need to be considered when designing learning algorithms that interact with and are placed in feedback with the world. The course will consist of lectures, and students will be evaluated based on traditional and programming assignments, as well as a final project. Not Offered Every Year 1 Course Unit
This is a graduate level course on fundamental operating principles and physics of semiconductor devices in reduced or highly scaled dimensions. The course will include topics and concepts covering basic quantum mechanics and solid state physics of nanostructures as well as device transport and characterization, materials and fabrication. A basic knowledge of semiconductor physics and devices is assumed. The course will build upon basic quantum mechanics and solid state physics concepts to understand the operation of nanoscale semiconductor devices and physics of electrons in confined dimensions . The course will also provide a historical perspective on micro and nanoelectronics, discuss the future of semiconductor computing technologies, cutting edge research in nanomaterials, device fabrication as well as provide a perspective on materials and technology challenges. Prerequisite: If course requirement not met, permission of instructor required. Not Offered Every Year 1 Course Unit
Nanorobotics is a field at the forefront of nano-science and engineering that seeks to create synthetic systems that sense and respond to their environment at dimensions comparable to biological microorganisms. This course explores the topic of small machines: What materials should we use to make these devices? How should they be powered or locomote? What capacities can they have for memory or information processing? How can they be made to interface safely with biological systems? This course covers the major frameworks for building small machines, including self-assembled systems (DNA nanotechnology, biohacking) and those fabricated by top-down lithography (self-folding systems, synthetic micro-swimmers, smart-dust). Particular emphasis is given to exploring physical principles that can be used to analyze the strengths and limitations of current robot designs at the micro and nanoscale. 1 Course Unit
This research seminar deals with tools, methods, and algorithms for analysis and design of distributed dynamical systems. These are large collections of dynamical systems that are spatially interconnected to form a collective task or achieve a global behavior using local interactions. Over the past decade such systems have been studied in disciplines as diverse as statistical physics, computer graphics, robotics, and control theory. The purpose of this course is to build a mathematical foundation for study of such systems by exploring the interplay of control theory, distributed optimization, dynamical systems, graph theory, and algebraic topology. Assignments will consist of reading and researching the recent literature in this area. Topics covered in distributed coordination and consensus algorithms over networks, coverage problems, effects of delay in large scale networks. Power law graphs, gossip and consensus algorithms, synchronization phenomena in natural and engineered systems, etc. Not Offered Every Year 1 Course Unit University of Pennsylvania Catalog 1727
The course covers advanced topics in machine learning systems with an emphasis on the interplay between models and system-level support. This course surveys recent advances in efficient machine learning computing techniques including model compression, pruning, quantization, neural architecture search, knowledge distillation, distributed training, and parallelism. Discussion-oriented classes focus on in-depth analysis of readings. Final project and paper required. Appropriate for graduate and advanced undergraduate students. After completing this course, students should be able to: 1) understand general research and development trends in machine learning systems; 2) develop intuition on how to optimize algorithms with hardware in mind; 3) read machine learning system papers critically; 4) write constructive paper reviews; 5) design and execute a research project to address an open research problem in machine learning systems; 6) develop self- learning skills for continuous growth beyond the course. CIS 5190 or CIS 5200 or CIS 5480 or CIS 5710 or ESE 3050 or ESE 5320) before taking ESE 6390. 1 Course Unit
Students must take (CIS 4190 or CIS 4480 or CIS 4710 or
ESE 6450Deep Generative Models1
Total Course Units 4 Students can pursue an optional concentration by selecting 4 of their required 6 AI electives courses from the list of approved courses for the concentration of their choice. Note that only courses taken towards the AI Elective category are eligible to count for a concentration
ESE 6510Physical Intelligence: Science and Systems
This course provides an in-depth exploration of advanced topics in robot learning, an area of artificial intelligence that focuses on the agentic aspects of real-world decision making. Students will learn what are the challenges in applying foundational concepts in imitation and reinforcement learning, including policy optimization, value-based methods, model-based RL, and exploration-exploitation trade-offs, when applied to real robots. In the final part of the course, we will cover recent advancements in the field, with a particular focus on learning from high-dimensional sensory observations, e.g., vision, sound, or touch. Alongside a theoretical understanding of the foundations, students will gain experience with practical implementations of robot learning algorithms via assignments and a final project. Recommended
Coding theory for telecommunications with emphasis on the algebraic theory of cyclic codes using finite field arithmetic, decoding of BCH and Reed-Solomon codes, finite field Fourier transform and algebraic geometry codes, convolutional codes and trellis decoding algorithms, graph based codes, Berrou codes and Gallager codes, turbo decoding, iterative decoding. And belief propagation. Not Offered Every Year 1 Course Unit
ESE 6800Special Topics in Electrical and Systems Engineering
Advanced and specialized topics in both theory and application areas. Students should check Graduate Group office for offerings during each registration period. Not Offered Every Year 1 Course Unit
For students who are studying a specific advanced subject area in electrical engineering. Students must submit a proposal outlining and detailing the study area, along with the faculty supervisor's consent, to the graduate group chair for approval. A maximum of 1 c.u. of ESE 8990 may be applied toward the MSE degree requirements. A maximum of 2 c.u.'s of ESE 8990 may be applied toward the Ph.D. degree requirements.
Total Course Units 19 Students should view the 'Course Requirements' to view courses listed for the Depth, Breadth, and Critical Thinking Requirements. The Candidacy Examination should be completed by August of the student's fifth year. The student's Dissertation Defense/Oral Exam should be completed by August of the student's sixth year. Materials Science and Engineering,
