ECE 598

Spring 2021 All Classes

All Classes

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 spring 2021
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
68079	Online Lecture	MS	11:00AM -12:20PM	TR	n.a.	Huang, J	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Adv Memory & Storage Systems Section Info: Prerequisites: Maturity in understanding of operating systems, systems architecture, memory and storage systems. One of the following courses: ECE 391, ECE 411, or ECE 511. In this course, we will discuss advanced techniques for building memory and storage systems. The course will cover a variety of latest research topics centered around the memory and storage systems that include the new and emerging hardware architecture, memory/storage systems software, memory-centric applications, near-storage computing, rack-scale storage, storage security and reliability, mobile/wearable/IoT storage, and storage in large-scale data centers. In each lecture, we will discuss 2 research papers for each topic. In addition, we provide 2-4 more papers as optional reading for students who wish to dig deeper into these topics. Through this course, students will learn not only the fundamental concepts of memory and storage systems via the lecture materials, but also the hands-on experience of building and evaluating a memory/storage-centric system via projects.
49792	Online Lecture	PV	12:30PM -1:50PM	TR	n.a.	Viswanath, P	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Principles of Blockchains Section Info: Principles and Design of Blockchain. This course is an introduction to blockchains, with a concrete application focus on payment systems. Bitcoin is the original distributed payment system and significant research and engineering have transpired in the decade since its birth. This course studies all the layers of a modern distributed payment system, borrowing from recent research literature. In particular, the network stack, the consensus layer, storage and computation layers and privacy and anonymity stacks are viewed holistically. The course studies fundamental tradeoffs to concrete metrics of performance: throughput, latency, security, storage, compute, privacy and how the different designs proposed in recent literature compare. Both analytical (theoretical security guarantees) comparisons and implementation-based empirical performances will be explored in the course. Prerequisites: Probability (ECE 313), programming in Python/C++, algorithms (CS 473) and networking (ECE/CS 438).
72938	Online Lecture	ZZ	11:00AM -12:20PM	TR	n.a.	Zhao, Z	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: High Dim Geom Data Analysis Section Info: High Dimensional Geometric Data Analysis This course aims to establish the mathematical foundation of many recent algorithms for tasks such as organization and visualization of data clouds, dimensionality reduction, clustering, classification, and regression. "Data analysis" is an interdisciplinary field. It combines mathematics (both poor and applied), computer science (machine learning, theoretical CS, AI, computer vision), electrical engineering (signal and image processing) statistics, structural biology, neuroscience (fMRI), computation biology (microarray data for gene expression), finance (financial data), biophysics and chemical engineering (molecular dynamics simulations), World Wide Web and search (link analysis), social networks, psychology (MMPI questioners), and more. We will focus on a few particular methods and explain where they are most appropriately applied, what are their limitations, and what is the underlying math, so we will develop a good sense of when to apply them and have a sound basis for developing new algorithms. The course will have three main sections: 1) high dimensional probability, 2) geometric data analysis, and 3) other recent advances with applications. The high-dimensional probability section of the course aims at getting insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Drawing on ideas from probability, analysis, and geometry, it leads to many applications in statistics, signal processing, and optimization. In the second part of the course, we introduce spectral methods that are useful in the analysis of big data sets. Spectral methods involve the construction of matrices (or linear operators) directly from the data and the computation of a few leading eigenvectors and eigenvalues for information extraction. Prerequisites: ECE 313, ECE 534 or their equivalents. Programming in Python (Matlab).