News Highlights

The latest updates and briefs from the Department of Electrical and Computing Engineering.

    Armita Salahi Applies Machine Learning to Improve Patient Outcomes from Chemotherapeutic Treatments

    September 23, 2020

    Congratulations to Armita Salahi, who has earned a Sture G. Olsson Fellowship to develop systems approaches in biomedical engineering. Salahi is a Ph.D. student of electrical engineering advised by Nathan Swami, professor of electrical and computer engineering at the University of Virginia. Salahi joined Swami’s lab in 2015 to pursue her interest in developing devices for disease diagnostics, earning her M.S. degree within two years. To understand disease onset and progression, Salahi develops label-free microfluidic methods to measure and analyze the biophysical properties of single cells to characterize the role of heterogeneity in diseases. 

    Cong Shen’s Trifecta of NSF Grants Accelerates Machine Learning in Wireless Communications

    September 22, 2020

    Cong Shen, assistant professor of electrical and computer engineering at the University of Virginia, has earned three grants from the National Science Foundation to meet rising demands on wireless networks and advance machine learning. Shen is an expert in machine learning for wireless networks who possesses extensive industry R&D experience and 17 U.S. patents in wireless communication and networking.


    Shuo Li Provides Winning Answer to IEEE SSCS Challenge Problem

    September 21, 2020

    Congratulations to Shuo Li, winner of the IEEE SSCS International Student Circuit Contest. The contest engages undergraduate and graduate students in thought-provoking circuit analysis and design. Students were invited to provide an intuitive response to a challenge problem: a feedback amplifier that at first glance seems to be unstable but is perfectly stable. Li was one of two contestants whose explanation earned an award. Li is working toward his Ph.D. in electrical engineering in the Robust Low Power VLSI group led by Benton H. Calhoun, professor of electrical and computer engineering. Li’s research interests include analog/mixed-signal integrated circuit and system design, particularly in areas of energy-harvesting, DC-DC converters, sensor interfaces, and system-on-chips for ultra-low-power IoT applications. Learn more:

    IEEE Signal Processing Society Launches PROGRESS, a Diversity and Inclusion Initiative

    September 19, 2020

    UVA electrical and computer engineers support the launch of PROGRESS, a diversity, equity and inclusion initiative of the IEEE Signal Processing Society. Nikos Sidiropoulos, Louis T. Rader Professor and department chair, serves on the 2020 PROGRESS oversight committee. PROGRESS aims to motivate and support women and under-represented minorities to pursue academic careers in signal processing. An inaugural PROGRESS workshop will be held Oct. 26-27 during the 2020 IEEE International Conference on Image Processing, to provide information, tools and networking opportunities. A similar workshop is planned for the IEEE 2021 International Conference on Acoustics, Speech and Signal Processing. Learn more about PROGRESS and apply to participate in the IEEE ICIP PROGRESS workshop:

    Team Demonstrates Unconventional Approach to Solve Hard Computational Problems

    September 18, 2020

    Not all computing problems are created equal. Certain problems—commonly known as NP-hard problems—are considered intractable to solve using digital machines and require exponentially increasing resources for increasing sizes of the input. A team of electrical and computer engineers from the University of Virginia and Notre Dame demonstrate an unorthodox approach to help solve some of the toughest problems in computing. Published September 17 in Nature Communications, their paper, Using Synchronized Oscillators to Compute the Maximum Independent Set, presents an integrated circuit comprised of 30 oscillators that are able to synchronize with each other in highly reconfigurable patterns on a 1.44 square millimeter chip.

    Research Team’s Deep Learning Algorithms Facilitate Learning on Graph Data

    September 16, 2020

    Jundong Li, assistant professor of electrical and computer engineering at the University of Virginia, develops novel deep learning algorithms to glean more actionable patterns from graph-structured data sets. Li holds joint appointments in UVA’s Department of Computer Science and School of Data Science. Li and Shuiwang Ji, associate professor of computer science and engineering at Texas A&M, have earned a National Science Foundation grant to improve the essential building blocks of deep learning algorithms for graphs.

    Tensor Decomposition for Signal Processing and Machine Learning Is a Go-To Primer for Students and Practitioners

    September 02, 2020

    Each year, hundreds if not thousands of papers are published on tensor-related topics, easily overwhelming students and practitioners alike. Nikolaos Sidiropoulos, professor and chair of the Charles L. Brown department of electrical and computer engineering at the University of Virginia, led an effort to create an easy on-ramp for those eager to learn about and work with tensors. Sidiropoulos, a fellow of the Institute of Electrical and Electronics Engineers, collaborated with a small group of IEEE co-authors and other colleagues to publish Tensor Decomposition for Signal Processing and Machine Learning. The paper is ranked #1 in Google Scholar metrics for Transactions on Signal Processing, the flagship journal of the IEEE Signal Processing Society, for 2015-2020, and is the journal's most popular and frequently accessed paper in IEEE Xplore. It is recommended reading for anyone who needs to incorporate tensors in their research and algorithm development.