ECE 598

Fall 2023 Part of Term 1

Part of Term 1
Aug 21-Dec 6

Special Topics in ECE

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Credit: 0 TO 4 hours.

Subject offerings of new and developing areas of knowledge in electrical and computer engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites.

May be repeated in the same or separate terms if topics vary.

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ECE 598 class schedule data for fall 2023
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
51575	Lecture	BC	12:30PM -1:50PM	TR	3013 Electrical & Computer Eng Bldg	Cunningham, B	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Advanced Biosensors Section Info: Fundamental principles at the intersection of engineering, biology, and data science that are pushing the forefront of biosensor technology used for diagnostics and life science research. Prerequisite: ECE/BIOE 416 (Biosensors) Restriction(s): Restricted to Graduate - Urbana-Champaign.
55808	Lecture	GM	2:00PM -3:20PM	TR	3015 Electrical & Computer Eng Bldg	Moustakides, G	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Data Driven Techniques Section Info: Data Driven Techniques for Decision Making and Estimation Learning algorithms for equation solving and function optimization, simplified convergence analysis, fair comparison methods. Decision making, Bayesian techniques, data-driven decision making, Bayes-consistent training methods, data-driven decision making for Markov processes. Realization of random variables, generative networks, adversarial and non-adversarial design of generative networks, probability density vs generative modeling for random data on manifolds. Parameter estimation, Bayesian and non-Bayesian estimators, data-driven parameter estimation, generative models for robust estimation and efficient solution of high-dimensional inverse problems. Data-driven estimation of conditional expectations, application to stochastic optimization. Prerequisites: Probability theory and elements of random processes Restriction(s): Restricted to Graduate - Urbana-Champaign.
72474	Lecture	HPN	1:00PM -2:20PM	WF	2015 Electrical & Computer Eng Bldg	Mittal, R	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: High-Speed/Progrmable Networks Section Info: The ever-increasing demand for higher performance, new functionality, and flexibility has given rise to radical new designs for networking infrastructure, that not only unleash exciting new opportunities, but also challenge conventional wisdom. The goal of this course is to introduce students to such recent research and industrial advancements in networking. In each lecture, we will discuss one or two recent papers that propose (or use) unconventional new designs for network stack, network interface cards, or switches. The papers are systems oriented, focusing on practical challenges associated with designing and implementing such network systems, and cover latest topics such as programmable switches, kernel-bypass networking, RDMA, and smart NICs. Prerequisites : ECE/CS 438 (Communication Networks).
56288	Lecture	HRI	3:30PM -4:50PM	TR	3013 Electrical & Computer Eng Bldg	Driggs-Campbell, K Huang, Z	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Human Robot Interaction Section Info: This course focuses on the mathematical and algorithmic tools that allow us to design and control robots that interact with people and gives an overview of what is require for guaranteeing safety in interactive settings on physical systems. Topics include advanced robotics, levels of autonomy, decision making and control, artificial intelligence, human-in-the-loop control, and human-robot interaction. Students will practice essential research skills including critiquing papers, debating, reviewing, writing project proposals, and presenting ideas effectively. Prerequisites:Graduate standing; An introductory robotics course (e.g., ECE470 (Introduction to Robotics), ABE424 (Principles of Mobile Robotics), ECE484 (Principles of Safe Autonomy)); ECE448/CS440 (Introduction to Artificial Intelligence) or equivalent; familiarity with controls (e.g., ECE515) and optimization (e.g., ECE490) is recommended, but not required Restriction(s): Restricted to Graduate - Urbana-Champaign.
47910	Lecture	PH1	9:30AM -10:50AM	TR	2017 Electrical & Computer Eng Bldg	Hanumolu, P	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Broadband Wireline Circuits Section Info: Broadband Wireline Communication Circuits This course provides a comprehensive study of wireline communication systems, including design, analysis, and implementation. The curriculum covers key concepts such as modulation, equalization, serialization/deserialization, and synchronization. It also focuses on the modeling and design of high-speed electrical and optical transceivers, characterizing communication channels, and examining the effects of impairments on system performance. Laboratory exercises and projects using simulation and design tools will be used. Prerequisites: ECE 483 or equivalent Restriction(s): Restricted to Graduate - Urbana-Champaign.
73943	Lecture	RKI	12:30PM -1:50PM	TR	4070 Electrical & Computer Eng Bldg	Iyer, R	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Dependable AI Systems Section Info: The emergence of AI systems and their ubiquitous adoption in automating tasks that involve humans in critical application domains (e.g., autonomous vehicles, medical assistants/devices, manufacturing, agriculture, and smart buildings) means that it is of paramount importance that we be able to place trust in these technologies. In a broad sense, a trustworthy AI system must be dependable (i.e., ensure safety, resilience, robustness, and security of its own and its operational environment) and reasonable (i.e., provide the reasoning behind produced decisions/actions). Indeed, the absence of such features not only makes people reluctant to deploy technology in the field despite successful demonstrations, but also leaves systems vulnerable to security hacks and crashes that ultimately impact human safety. As Schneir states, traditionally, computers have only outperformed humans at speed, scale, and scope whereas humans excelled at thinking, reasoning, adapting, and understanding. However, artificial intelligence (AI) changes the landscape, where computers can now infer relationships, discover patterns, react, and adapt to changes while keeping its strength in speed, scale, and scope. While AI application long remained withheld for its high computational cost, recent advances in computing (high speed network, big data storage, computation speed) led to a new era of smart systems. AI has successfully demonstrated possibility in accelerating and automating processes that were primarily a job of human actors (e.g., driving, diagnosis, or navigating). However, the proximity to and direct interaction with humans (and our surrounding environment) raises an issue of trustworthiness. This course addresses the challenge of design, implementation, and validation of dependable AI systems by providing students with an opportunity to study new challenges imposed by classic as well as emerging AI algorithms, decision making under uncertainty, and the consequent safety, reliability, and security issues. In this course, students will have an opportunity to work on real-world applications in transportation, health, and systems domain while interacting with domain experts. Restriction(s): Restricted to Graduate - Urbana-Champaign.
54453	Lecture	YPZ	5:00PM -6:20PM	TR	2015 Electrical & Computer Eng Bldg	Zhang, Y	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Adv Tpcs Applied Cryptography Section Info: Topic: Advanced Topics in Applied Chryptography. Prerequisites: Basic knowledge of algorithms nad complexities, data structure and programming is recommended. This course covers techniques in applied cryptography and their applications in machine learning and blockchain to enhance privacy, integrity and scalability. The cutting-edge cryptographic techniques such as zero-knowledge proofs and secure multiparty computations will be explored. We will discuss the basic concepts and state-of-the-art constructions of these cryptographic schemes. Additionally, we will talk about how to use these techniques to construct privacy-preserving blockchain and crypto-currencies, zkRollups and zkEVM, privacy preserving machine learning and zero-knowledge proofs for machine learning. We will focus on efficiency and scalability constraints in practice, and discuss challenges and solutions to efficiently realize these cryptographic protocols. Restriction(s): Restricted to Graduate - Urbana-Champaign.