Electrical and Systems Engineering
Thursday, December 5th Raisler Lounge (Towne 225)
Multiferroic MEMS Magnetic Field Sensors for Biomedical Applications
Bio: Sydney Acosta is a fifth-year Ph.D. student under the guidance of Prof. Troy Olsson. She earned her bachelor’s degree in Biomedical Engineering at Purdue University in 2020. Her research interests include the design and development of multiferroic microelectromechanical systems (MEMS) sensors for biomedical applications. She is a National Defense Science and Engineering Graduate Fellow.
Abstract: The human body produces magnetic fields wherever ion exchange occurs. Detecting these pico-Tesla level magnetic fields enables non-invasive monitoring of brain and heart health, but medical-grade sensing methods require large equipment with high power consumption. This talk will detail a solution using microelectromechanical systems (MEMS) composed of a magnetostrictive and piezoelectric material. After an introduction to the sensor design and operation, we will discuss the design process, electrical and magnetic characterization, and results.
Thursday, November 21st Raisler Lounge (Towne 225)
How Much Data Is Enough? A Convex Relaxation Approach to Generalization
Bio: Uday Kiran is a second-year Ph.D. student under the supervision of Prof. René Vidal. He earned his bachelor’s degree in Electrical Engineering from the Indian Institute of Technology, Hyderabad, in 2023. His research interests include optimization techniques and establishing statistical guarantees for control systems, machine learning, and signal processing applications.
Abstract: Many prediction problems rely on experimental data, and intuitively, the more complex the model, the more data is needed for consistent performance. This talk addresses the critical issue of quantifying the amount of data sufficient to achieve such consistency. We begin with foundational concepts from classical statistical learning theory and progress to advanced, problem-specific measures developed in recent research. While these measures are often tailored to specific problems, this talk presents our recent work on a general framework for generalization theory through convex relaxations. This framework applies to a certain broad class of models, ranging from linear models in signal processing to highly non-linear models in deep learning
Tuesday, November 5th Raisler Lounge (Towne 225)
Prehistory of Continual Learning and All Else That We Forget
Bio: Liangzu Peng is a fourth-year PhD student working with Rene Vidal. He received his master’s degree from ShanghaiTech University and his undergraduate at Zhejiang University. He has co-authored over 20 papers on machine learning, computer vision, optimization, and signal processing. His current research focus is on continual learning.
Abstract: I would probably “forget” what I will say in the abstract, and you would, too. Translation: deep neural networks can “forget”, meaning they might perform poorly on previously learned tasks when learning a new task. A major goal of the subject now known as deep continual learning is to address this issue. In order to alleviate forgetting, the subject seems to forget that it has a prehistory (1960 – 1980). In this talk, we will recollect a few historical pieces and compare them with their modern counterparts. If time allows, I shouldn’t forget and should be excited to share my recent work on continual learning with you.
Thursday, October 24th Raisler Lounge (Towne 225)
Learning Local Control Barrier Functions for Safety-Critical Hybrid Systems
Bio: Shuo Yang is a Ph.D. student at the University of Pennsylvania, where he is advised by Professor George J. Pappas. Shuo is affiliated with the GRASP Lab and PRECISE Center. Previously, he obtained his Bachelor’s degree (Summa Cum Laude) from Shanghai Jiao Tong University in 2021. He has also spent time at Toyota Research and Tencent AI. He is broadly interested in formal methods, machine learning, control theory, and algorithmic game theory, with their applications to robotic and multi-agents systems.
Abstract: Safety-critical control is one of the fundamental problems in autonomous systems. A special class of autonomous systems is the class of hybrid dynamical systems, which involves both continuous dynamic flow and discrete dynamical mode jumps for state evolution. I will introduce how to synthesize safe controllers for hybrid dynamical systems based on local control barrier functions (CBFs), and such a framework enjoys flexibility, non-conservativeness, and computational advantage compared with existing safety-critical methods. Then, I will show how to learn local CBFs for hybrid systems through self-supervision techniques. Finally, I will briefly share some ideas on learning safe and adaptive controllers in multi-agents systems.
Tuesday, March 26, 2024 Singh 313
Degradable Active Soil Monitoring
Bio: Elizabeth Schell is a 4th year PhD student in Prof. Mark Allen’s group working on degradable electronics as part of IoT4Ag, an NSF research center for precision agriculture. She started her adventures in research at MIT (B.S. in Electrical Engineering) and Stanford (M.S. in Electrical Engineering) working on flexible and stretchable electronics. Outside the lab, she likes to go on outdoor adventures and craft. Inside the lab, she also likes to work on crafts.
Abstract: Farming requires a balance of providing enough inputs, like water and fertilizer, to ensure sufficient yield while minimizing the consequences of overusing these resources, which can be costly, scarce, or lead to adverse environmental effects, such as eutrophication caused by nutrient run off. Current soil monitoring practices involve manually collecting soil samples that are sent to off site labs to be tested. In this talk, I’ll discuss the design of a platform for in-situ soil nutrient monitoring, which will enable the collection of high spatial resolution information about multiple soil parameters.
Tuesday, March 12, 2024 Singh 313
“GraphIC Design”: using silicon architectures to create reliable graphene magnetic sensor arrays”
Bio: Vasant is a PhD student in Electrical and Systems Engineering at the University of Pennsylvania, co-advised by Firooz Aflatouni and David Issadore. He received his BS degree in Electrical Engineering from the California Institute of Technology in 2017. His research focuses on the integration of CMOS electronic circuits with 2D materials and silicon photonics to implement new devices for biosensing and metrology.
Abstract: Graphene and other two-dimensional materials are highly appealing for electronics due to their extraordinary properties, but their 2D nature leads to heterogeneity between devices and limits their practical utility. For example, graphene Hall-effect magnetic sensors offer significant advantages over commercial Hall sensors but are not as widely used, since imperfections in the graphene result in huge performance variations between devices. In this talk, we discuss how the effects of these imperfections can be reduced with variation-resilient circuit architectures. We characterize a tuning technique that reduces variability between graphene Hall sensors on the same substrate to <2% without compromising their performance. We also show progress towards integrating graphene Hall sensors onto a mm-scale CMOS chip which vertically connects each sensor with silicon circuits that reduce variability, noise, and offset, enabling scalable sensor arrays for magnetic imaging and biosensing.
Tuesday, February 27, 2024 Singh 313
Multi-Agent Reinforcement Learning for Large-Scale Markov Potential Games
Bio: Dongsheng Ding is a Postdoctoral Researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He received his Ph.D. in Electrical Engineering from the University of Southern California. His research interest lies in developing reinforcement learning approaches for learning to control constrained and multi-agent dynamical systems.
Abstract: Multi-agent reinforcement learning (RL) has found widespread success in multi-agent systems with decision-making agents. Its applications span diverse domains, ranging from playing games to navigating robotics to making economic policy. Unfortunately, the curse of dimensionality and/or multiagency poses scalability challenges for classical RL methods, particularly when dealing with large state spaces and/or numerous agents. This talk addresses the scalability challenges in the framework of independent learning (i.e., no coordination among agents), and focuses on Markov potential games for multi-agent RL. Firstly, we introduce an independent policy gradient method, where each agent updates its policy myopically using policy gradients. When the policy gradient is evaluated exactly, this method demonstrates non-asymptotic convergence to a near-Nash policy, with a polynomial dependency on the number of agents. Interestingly, the iteration complexity is not explicitly dependent on the state space size. Secondly, when the exact policy gradient is unavailable, we propose the function approximation of the value function to handle large state spaces and present polynomial sample complexity of our method in finding a near-Nash policy, showcasing the efficacy of our method in large-scale Markov games. Finally, we conclude with the game-agonistic convergence property for a variant of our method, finding a near-Nash policy in more than two types of games.
Tuesday, February 13, 2024 Singh 313
Layered Control Architectures in Trajectory Optimization for Underactuated Robotic Systems
Bio: Anusha Srikanthan is a 3rd year PhD student in Electrical and Systems Engineering working with Dr. Nikolai Matni and Dr. Vijay Kumar. Her research interests are at the intersection of robot navigation, learning for control, and multi-agent systems. Previously, she completed her Masters Thesis at Georgia Institute of Technology advised by Dr. Harish Ravichandar and Dr. Sonia Chernova. The focus of her thesis was on the design of algorithms for learning implicit task requirements for multi-agent task allocation using data from expert demonstrations. Outside of academia, she was a classical dance performer and headed the dance team during her undergraduate years at NIT Trichy, India.
Abstract: We consider joint trajectory generation and tracking control for under-actuated robotic systems. A common solution is to use a layered control architecture, where the top layer uses a simplified model of system dynamics for trajectory generation, and the low layer ensures approximate tracking of this trajectory via feedback control. While such layered control architectures are standard and work well in practice, selecting the simplified model used for trajectory generation typically relies on engineering intuition and experience. First, we propose a layered control architecture for exact reference tracking control using a suitable augmented Lagrangian reformulation of a linear optimal control problem and solve via primal-dual optimization without assuming a simplified model. We show that the resulting controller is an optimal linear feedback controller stabilizing system trajectories around dynamically feasible reference trajectories. We discuss implications of this result for quantifying sub-optimality of existing layered architectures for robot navigation, considering the context of a cartpole, a unicycle, and a quadrotor control problem in simulation. Next, we propose an alternative data-driven approach to dynamics-aware trajectory generation and show that applying a penalty method to a global nonlinear optimal control problem results in a relaxed layered decomposition of the overall problem into trajectory planning and feedback control layers. Crucially, the resulting trajectory optimization is dynamics-aware, in that it is modified with a tracking penalty regularizer encoding the dynamic feasibility of the generated trajectory. We show that this tracking penalty regularizer can be learned from system rollouts for independently-designed low layer feedback control policies. Furthermore, we showcase our framework on two quadrotor hardware platforms to illustrate that our approach i) avoids conservatism from ad hoc maximum speed and acceleration constraints and, ii) outperforms baselines in practical considerations such as aerodynamic drag and controller saturation.
