Electrical and Systems Engineering
Wednesday, October 19, 2022 Towne 225/Raisler Lounge
Fast and Flexible FPGA development
Bio: DJ Park is a PhD student in the Implementation of Computation group, advised by Prof. André DeHon. He received his B.S. in Electrical and Computer Engineering at Carnegie Mellon University. He also has experience as a SoC engineer in industry. His research interests include FPGAs and Partial Reconfiguration.
Abstract: FPGA (Field-Programmable-Gate-Array) is an interesting hardware platform that has a flexibility of processors and a performance of hardware. To enable wider FPGA utilization in academia/industry, this work focuses on resolving a well-known issue: a long compilation time. In the presentation, I will illustrate the limitations of the previous approaches and how our new approach can provide a fast and flexible FPGA development environment. Even if you are not familiar with FPGA or computer architecture, please come and visit because the presentation is modified to target a wider range of audience!
Wednesday, November 2, 2022 Towne 225/Raisler Lounge
Distribution shift in imitation learning and how to control it
Bio: Thomas Zhang is a 3rd-year PhD student advised by Prof. Nikolai Matni. His research interests involve some hodge podge of dynamical systems, statistical learning, and control theory. In particular, he is interested in studying the properties of learning-based methods applied on non-i.i.ddata generated by dynamical (control) systems. Prior to Penn, Thomas received BSc’s in Mathematics and Statistics & Data Science from Yale University, where he then spent a year as a research scientist in the Applied Mathematics Program.
Abstract: In this talk, we will set up the imitation learning problem in the context of continuous control, and demonstrate how distribution shift fundamentally hinders out-of-the-box attempts at establishing provable generalization guarantees. Therefore, the key statistical learning problem we want to answer is: “when does low training error along the expert’s trajectory imply low test error on the learned trajectory?” We will then show how nailing down a notion of an expert demonstrator’s ability to recover from small errors translates to a sufficient condition for controlling the distribution shift. This naturally leads to the introduction of TaSIL: Taylor Series Imitation Learning. We will show why, in addition to standard regression on the expert’s outputs, regression on the expert’s higher order derivatives naturally pops up as a way to provably mitigate distribution shift.
Learning globally smooth functions on manifolds
Bio: Juan received the B.Sc. degree in electrical engineering from the Universidad de la Republica Oriental del Uruguay, Montevideo, in 2018. He is now a PhD student in the Department of Electrical and Systems Engineering at the University of Pennsylvania, supervised by Professor Alejandro Ribeiro. Juan’s current research interests are in machine learning, optimization and control.
Abstract: Smoothness and low dimensional structures play central roles in improving generalization and stability in learning and statistics. The combination of these properties has led to many advances in semi-supervised learning, generative modeling, and control of dynamical systems. However, learning smooth functions is generally challenging, except in simple cases such as learning linear or kernel models. Typical methods are either too conservative, relying on crude upper bounds such as spectral normalization, too lax, penalizing smoothness on average, or too computationally intensive, requiring the solution of large-scale semi-definite programs. These issues are only exacerbated when trying to simultaneously exploit low dimensionality using, e.g., manifolds. This work proposes to overcome these obstacles by combining techniques from semi-infinite constrained learning and manifold regularization. To do so, it shows that, under typical conditions, the problem of learning a Lipschitz continuous function on a manifold is equivalent to a dynamically weighted manifold regularization problem. This observation leads to a practical algorithm based on a weighted Laplacian penalty whose weights are adapted using stochastic gradient techniques. We prove that, under mild conditions, this method estimates the Lipschitz constant of the solution, learning a globally smooth solution as a byproduct. Numerical examples illustrate the advantages of using this method to impose global smoothness on manifolds as opposed to imposing smoothness on average.
Wednesday, November 16, 2022 Towne 225/Raisler Lounge
The Traveling Salesman Problem and Space-Filling Curves
Bio: Farhad Nawaz is a 2nd year Ph.D. student in the GRASP lab advised by Dr. Nikolai Matni. His research interests include control and learning theory for dynamical systems. In particular, he is interested in exploring efficient and robust planning and learning strategies for robotic tasks. Before joining Penn, he received a Master’s degree in Aerospace Engineering from the University of Illinois Urbana-Champaign, where he worked on planning algorithms for multi-agent systems operating in uncertain and stochastic environments.
Abstract: In this talk, I will present the routing of vehicles to various delivery points as the Travelling salesman problem (TSP), which is a widely studied NP-hard problem in combinatorial optimization. I will discuss a practical heuristic for the TSP that was implemented in a routing system for food delivery. The heuristic algorithm is based on space-filling curves — a mathematical construction that generates a path to cover a planar region. I will also skim through the analysis of how the space-filling curves and a concentration inequality demonstrate that the length of the optimal tour is approximately constant.
Friday, March 4, 2022 Wu & Chen Auditorium
Fast Discovery of Latent Switching States
Bio: Alex Nguyễn-Le is a second year PhD student currently advised by Victor Preciado. He received a BS in Electrical Engineering from UCSB in 2017, worked in the pharmaceutical industry for Roche, and subsequently returned to UCSB for a MS in Electrical Engineering which he received in 2020. His research interests primarily center around Optimization, Control, and Neuroscience.
Abstract: Many signals can be reasonably characterized by a small number of dominant modes that produce time-evolving behaviors, and identifying these modes is paramount to modeling systems simply. In this work, we present some algorithms and heuristics that enable discovery of these modes. Theoretical properties of the algorithms are explored and validation of its effectiveness is demonstrated on synthetic data. Finally, we combine our algorithm with heuristic techniques to discover latent states in neurological time series gathered from epileptic patients; despite the perceived complexity of neural systems, we demonstrate that the signals associated with seizures can be described by relatively few dominant modes.
Friday, April 15, 2022 Wu & Chen Auditorium
Quantum Networking: Why, What and How
Bio: Robert entered the networking industry in 1984 as a test engineer. During his 38 year career in what became the internet industry he installed the first TCP/IP NIC at Berkeley University, developed the first interoperable TCP/IP NICs, Mainframe Channel attach and X.25 gateways for the Japanese market, led the installation of the first trans-Atlanic IP fiber link, and ran a number of applied academic research projects. 5 years ago he was introduced to quantum networking and has since come to Penn to learn about quantum light/matter interfaces.
Abstract: Over the past ~40 years the internet’s hyperexponential growth was enabled by factors such as coexistence with telecommunications infrastructure, interoperability and the ability to scale. Quantum networks have shown coexistence with internet infrastructure but interoperability has not been demonstrated; scaling of geographically distributed Hilbert spaces has no digital analogue. We introduce these topics and discuss an Atomic Ensemble network under development at Stony Brook University and a Diamond Vacancy center network under development at TU-Delft.
Friday, April 22, 2022 Wu & Chen Auditorium
Bridging Learning and Control with Safety Guarantees
Bio: Shaoru Chen is a Ph.D. candidate at the University of Pennsylvania in the Department of Electrical and Systems Engineering, working with Prof. Victor M. Preciado. Prior to his Ph.D., he received a B.E. degree in Automation from Zhejiang University, China. His research focuses on safe learning for control with his work spanning neural network verification, safety analysis of learning-enabled systems, and robust model predictive control.
Abstract: Learning-enabled systems are emerging in many real-world applications where learning modules such as deep neural networks are deployed as feedback controllers, perception modules, or motion planners in complex dynamical systems. Providing formal safety guarantees for such systems is crucial since neural networks can behave in unexpected ways under small input perturbation and cause the closed-loop system to be unsafe. Due to the coupling between nonlinear dynamics and complex, large-scale learning modules, providing formal guarantees for learning-enabled systems is challenging and calls for the development of new theoretical and computational frameworks on the interface between machine learning, control, and optimization.
My research focuses on designing numerically efficient tools for providing formal guarantees for learning-enabled systems. In this talk, I will discuss how control-theoretic analysis and machine learning can be combined to address this challenge by solving hierarchical problems including (1) scalable verification of neural network input-output properties, (2) closed-loop reachability and stability analysis of neural network controlled systems, and (3) robust model predictive control of uncertain dynamical systems with safety guarantees. At the end of the talk, I will discuss how the research on the above three problems can complement each other and relate to broader research areas.
Friday, April 29, 2022 Towne 337
Neural Estimation of the Rate-Distortion Function With Applications to Operational Source Coding
Bio: Eric is a 2nd-year Ph.D. student at University of Pennsylvania in the electrical and systems engineering department, advised by Shirin Saeedi Bidokhti and Hamed Hassani, and supported by the NSF Graduate Research Fellowship. Previously, he received his B.S. in electrical and computer engineering at Cornell University in 2020. His current research interests are in data compression, information theory, and machine learning.
Abstract: A fundamental question in designing lossy data compression schemes is how well one can do in comparison with the rate-distortion function, which describes the known theoretical limits of lossy compression. Motivated by the empirical success of deep neural network (DNN) compressors on large, real-world data, we investigate methods to estimate the rate-distortion function on such data, which would allow comparison of DNN compressors with optimality. While one could use the empirical distribution of the data and apply the Blahut-Arimoto algorithm, this approach presents several computational challenges and inaccuracies when the datasets are large and high-dimensional, such as the case of modern image datasets. Instead, we reformulate the rate-distortion objective, and solve the resulting functional optimization problem using neural networks. We show that the resulting rate-distortion estimator, called NERD, is a strongly consistent estimator, and provide evidence that NERD can accurately estimate the rate-distortion function on popular image datasets. Using our estimate, we show that the rate-distortion achievable by DNN compressors are within several bits of the rate-distortion function for real-world datasets. Additionally, NERD provides access to the rate-distortion achieving channel, as well as samples from its output marginal. Therefore, using recent results in reverse channel coding, we describe how NERD can be used to construct an operational one-shot lossy compression scheme with guarantees on the achievable rate and distortion. Experimental results demonstrate competitive performance with DNN compressors.
Friday, May 6, 2022Wu & Chen Auditorium
Learning Decentralized Wireless Resource Allocations with Graph Neural Networks
Bio: Zhiyang is a 3rd-year Ph.D. student at the University of Pennsylvania in the Electrical and Systems Engineering Department, advised by Alejandro Ribeiro. Previously, she received B.E. and M.E. degrees in 2016 and 2019 respectively, from the Department of Electronic Engineering and Information Science, University of Science and Technology of China. She has been working on the use of graph neural networks to design algorithms for distributed allocation of resources in wireless communication networks. More recently, she is working on analyses of the limits of graph neural networks when graphs are sampled from
Abstract: We consider the broad class of decentralized optimal resource allocation problems in wireless networks, which can be formulated as a constrained statistical learning problems with a localized information structure. We develop the use of Aggregation Graph Neural Networks (Agg-GNNs), which process a sequence of delayed and potentially asynchronous graph aggregated state information obtained locally at each transmitter from multi-hop neighbors. We further utilize model-free primal-dual learning methods to optimize performance subject to constraints in the presence of delay and asynchrony inherent to decentralized networks. We demonstrate a permutation equivariance property of the resulting resource allocation policy that can be shown to facilitate transference to dynamic network configurations. The proposed framework is validated with numerical simulations that exhibit superior performance to baseline strategies.
