ECE Seminars

Fall 2021 Seminars:

Wednesday, September 29, 2021 at 4:25pm 
Speaker: Dr. Yu Yang
Cyber-Physical Systems for Smart Cities


Abstract: In this talk, I will introduce our work on the foundations and applications of Cyber-Physical Systems to Smart Cities. For any city-scale systems, obtaining the accurate real-time status of interested entities (e.g., real-time locations of vehicles, devices, and users) is essential for downstream applications. However, the existing status sensing approaches in both industry and academy have limited scalability to city-scale due to cost issues. By using city-scale on-demand gig delivery as a concrete use case, I will introduce how we obtain real-time gig worker status (e.g., locations and routes) in a cost-efficient approach to enable timely delivery and efficient order scheduling. Based on our collaboration with Alibaba On-demand Delivery Platform, we design, deploy, and evaluate a nationwide sensing system called aBeacon exploring a hybrid solution of hardware, software, and human participation. aBeacon detects and infers the status of more than 3 million workers in 364 cities in China via a sophisticated tradeoff between performance, cost, and privacy. I will provide some lessons learned and insights when aBeacon evolves from conception to design, deployment, validation, and operation. Finally, I will discuss some ongoing directions.

Bio: Yu Yang is an Assistant Professor in the Department of Computer Science and Engineering at Lehigh University. He is broadly interested in Mobile Sensing, Cyber-Physical Systems, Cyber-Human Systems, and Data Science, with a focus on sensing, prediction, and decision-making for cross-domain urban systems including cellular networks, mobile payment, taxis, buses, subways, bikes, personal vehicles, and trucks with applications to Smart Cities, Gig Economy, and Community Services. Details:



Wednesday, October 6, 2021 at 4:25pm 
Speaker: Dr. Rahul Mangharam 
Solving for Problem X: 3 Data-Driven Cyber-Physical Systems Challenge Problems
Abstract: This talk focuses on 3 life-critical system problems across autonomous vehicles, medical devices and energy systems. We will understand how modeling for such Cyber-Physical Systems (CPS) requires a combination of formal methods, controls, and machine learning. We highlight fundamental challenges in guarantees of safety and performance in data-driven CPS.  
Theme 1 – Safe Autonomy: A Driver’s License Test for Driverless Vehicles
Autonomous vehicles (AVs) have already driven millions of miles on public roads, but even the simplest maneuvers such as a lane change or vehicle overtake have not been certified for safety. To capture the long tail of safety cases we describe the design of a search engine for AV crashes. 
Theme 2 – Safe Medical Devices: Computer-aided Clinical Trials
Clinical trials can cost $10-20 million, last anywhere from 4-6 years and over 35% fail. We investigate how computer models and simulations of the physiology and medical devices in the closed-loop conduct in-silico trials and can be used as regulatory-grade evidence. 
Theme 3 – Energy Systems: Learning and Control using Gaussian Processes
Electricity markets have become increasingly volatile where 20-40X price spikes have become the norm. We explore data-driven approaches that bridge machine learning and controls for volatile energy markets.
Bio: Rahul builds safe autonomous systems at the intersection of formal methods, machine learning and controls. He applies his work to safety-critical autonomous vehicles, urban air mobility, life-critical medical devices, IoT4Agriculture, and AI Co-designers for complex systems. He is the Penn Director for the Department of Transportation's $14MM Mobility21 National University Transportation Center which focuses on technologies for safe and efficient movement of people and goods. Rahul received the 2016 US Presidential Early Career Award (PECASE) from President Obama for his work on Life-Critical Systems. He also received the 2016 Department of Energy’s CleanTech Prize (Regional), the 2014 IEEE Benjamin Franklin Key Award, 2013 NSF CAREER Award, 2012 Intel Early Faculty Career Award and was selected by the National Academy of Engineering for the 2012 and 2017 US Frontiers of Engineering. He has won several ACM and IEEE best paper awards in Cyber-Physical Systems, controls, machine learning, and education. Rahul is an Associate Professor in the Dept. of Electrical & Systems Engineering and Dept. of Computer & Information Science at the University of Pennsylvania. He received his Ph.D. in Electrical & Computer Engineering from Carnegie Mellon University. He enjoys organizing autonomous racing competitions at


Wednesday, October 13, 2021 at 4:25pm 
Speaker: Dr. Jiang Hu 
Machine Learning Techniques for Chip Design: From Graph Neural Network to Linear Regression
Packard Lab 466
Abstract: Machine learning becomes a popular approach to solving many complicated problems where conventional techniques failed, and chip design is no exception. The application of machine learning techniques is not necessarily simply plug-in use and often has significant room for maneuver. This presentation will cover a few recent results in this regard. The first part will be focused on the application and customization of graph neural network techniques for design predictions of both digital and analog circuits. The second part will show that a machine learning technique as simple as linear regression, with an elaborate use, can facilitate surprisingly strong results on simultaneous power modeling and monitoring for processor architecture designs.
Bio: Jiang Hu is currently a professor in the Department of Electrical and Computer Engineering at Texas A&M University. His research interests include design and automation of VLSI circuits and systems, computer architecture optimization and hardware security. He has published over 220 technical papers. He received a best paper award at the ACM/IEEE Design Automation Conference (DAC) in 2001, an IBM Invention Achievement Award in 2003, a best paper award at the IEEE/ACM International Conference on Computer‑Aided Design (ICCAD) in 2011 and a best paper award at the IEEE International Conference on Vehicular Electronics and Safety in 2018. He served as associate editor for the IEEE Transactions on CAD and the ACM Transactions on Design Automation of Electronic Systems. He was the technical program chair and the general chair of the ACM International Symposium on Physical Design (ISPD) in 2011 and 2012, respectively. He received Humboldt Research Fellowship in 2012. He was named IEEE fellow in 2016.

Wednesday, October 20, 2021 at 4:25pm 
Speaker: Dr. Vijaykrishnan Narayanan
Design of Processing-in-Memory Architectures for Deep Learning and Graph Applications
Abstract: Processing-in-memory (PIM) architectures are becoming increasingly relevant to reduce the cost of data movement in data intensive applications in machine learning and graph analytics. This talk will introduce approaches that support processing at different levels of the memory hierarchy and focus on one approach that supports incache SRAM computations. The talk will also cover accelerators designed for sparse matrix computations, and graph analytics using cross-point memory technologies.
Bio: Vijaykrishnan Narayanan is the Robert Noll Chair Professor in the school of EECS at the Pennsylvania State University. He is a fellow of the National Academy of Inventors, IEEE and ACM. He is an IEEE CEDA Distinguished Lecturer. Vijay received his Bachelors in Computer Science & Engineering from University of Madras, India in 1993 and his Ph.D. in Computer Science & Engineering from the University of South Florida, USA, in 1998.


Wednesday, November 3, 2021 at 4:25pm 
Speaker: Dr. Michael Brodsky 
Fiber-Optic Cables as Quantum Channels- Good or Bad?
Christmas-Saucon Hall 201
Abstract: Quantum networks carry tantalizing promise of unsurpassable capabilities in distributed computing, cryptography and sensing. In these futuristic networks the information is carried by quantum bits (qubits) over quantum communication channels. The qubits are not necessarily independent like classical bits are. In fact, individual qubits could share a special quantum connection, or, in quantum parlor, be entangled with one another. Once entanglement is distributed to remote network nodes and stored in quantum memories it serves as a valuable resource for promising applications. Quantum networks naturally require robust quantum channels for fast and reliable entanglement distribution over long distances. As quantum communication technology matures, it moves towards utilizing actual fibers and free-space optical channels. In this talk I will review the progress of my group in our research on quantum networks with a special focus on different quantum channels, the pertinent decoherence mechanisms in fiber-optic channels and various schemes for quantum information recovery.
Bio:  Dr. Michael Brodsky leads research in quantum information, communications and quantum networks at the US Army Research Laboratory. Michael’s current research expertise is in photonics and optical physics, quantum information processing and communication technologies focused on creation, manipulation and transmission of entangled states, as well as in devices and technologies for switching, routing, and buffering of quantum information. Prior to that he was a member of technical staff at AT&T Labs, where his contributions to fiber optic communications were focused on optical transmission systems and the physics of fiber propagation, most notably through his work on polarization effects in fiberoptic networks. Dr. Brodsky spent 2019-2020 academic year teaching physics to cadets at the US Military Academy at West Point, NY. Also during 2013-2014 he taught physics to undergraduate engineers at the NYU Tandon School of Engineering and Cooper Union. Dr. Brodsky has authored or co-authored over 150 journal and conference papers, a book chapter and holds over 40 granted U.S. patents. He served as a topical editor for Optics Letters and has been active on numerous program committees for the IEEE Photonics Society and OSA conferences. Dr. Brodsky is a Fellow of OSA and holds a PhD in Physics from MIT.