ESE PhD Colloquium Series

The PhD Colloquium Series provides a platform for PhD students to present their research interests, theories and progress to an audience of their peers.  This event is exclusively attended by current PhD students and their Postdoctoral colleagues.  During each weekly session, a PhD student presents a 30- to 45-minute presentation, allowing for 10-15 minutes of Q&A.  These discussions help students better define their research and implement innovative interdisciplinary approaches to their work, build relationships with students outside of their research laboratory, and prepare for their Proposal Examinations and Dissertation Defense.  A Committee of postdoctoral colleagues and faculty review video recordings from this series to determine the winners of the annual ESE PhD Colloquium Award.

If you are interested in presenting your work, email the ESE Graduate Program Coordinator at esegrad@seas.upenn.edu.

Spring 2020 Series

Luana Rubini Ruiz

Wednesday, February 5th, 2020
Towne 337

“Gated Graph Recurrent Neural Networks”

Bio: Luana received the B.Sc. degree in electrical engineering from the University of São Paulo, Brazil, and the M.Sc. degree in electrical engineering from the École Supérieure d’Electricité (now CentraleSupélec), France, in 2017. She is currently a Ph.D. candidate with the Department of Electrical and Systems Engineering advised by Prof. Alejandro Ribeiro. Her research interests are in the fields of graph signal processing and machine learning over network data. She was awarded an Eiffel Excellence scholarship from the French Ministry for Europe and Foreign Affairs between 2013 and 2015 and, in 2019, received a best student paper award at the 27th European Signal Processing Conference.

 

Read Luana's Abstract

Graph processes consist of sequences of graph signals that vary in time on top of a static graph. They can be used to model data such as climate variables on weather station networks and seismic wave readings on a network of seismographs. Our objective is to introduce a permutation equivariant Graph Recurrent Neural Network (GRNN). GRNNs learn representations of graph processes by taking both their sequential structure and the underlying graph topology into account, while also keeping the number of parameters independent of the length of the sequence and of the size of the graph. The stability of GRNNs to graph perturbations is analyzed, and their architecture is extended to include three gating strategies, which address the problem of vanishing/exploding gradients over time and across paths of the graph. The advantages of the GRNN parametrization are demonstrated in a regression and a classification experiment, where GRNNs outperform both GNNs and RNNs, and time, node and edge gating yield gains in performance in different time and spatial correlation scenarios.

Harshat Kumar

Wednesday, February 12th, 2020
Towne 337

Sample Complexity of Actor-Critic for Reinforcement Learning

Bio: Harshat Kumar received the B.Sc. degree in electrical and computer engineering from Rutgers University, New Brunswick, NJ, USA, in 2017. He has been working toward Ph.D. in electrical and systems engineering at the University of Pennsylvania, Philadelphia, PA, USA, since August 2017.

Read Harshat's Abstract

Reinforcement learning, mathematically described by Markov Decision Problems, may be approached either through dynamic programming or policy search. Actor-critic algorithms combine the merits of both approaches by alternating between steps to estimate the value function and policy gradient updates. Due to the fact that the updates exhibit correlated noise and biased gradient updates, only the asymptotic behavior of actor-critic is known by connecting its behavior to dynamical systems. This work puts forth a new variant of actor-critic that employs Monte Carlo rollouts during the policy search updates, which results in controllable bias that depends on the number of critic evaluations. As a result, we are able to provide for the first time the convergence rate of actor-critic algorithms when the policy search step employs policy gradient, agnostic to the choice of policy evaluation technique. In particular, we establish conditions under which the sample complexity is comparable to stochastic gradient method for non-convex problems or slower as a result of the critic estimation error, which is the main complexity bottleneck. These results hold for in continuous state and action spaces with linear function approximation for the value function. We then specialize these conceptual results to the case where the critic is estimated by Temporal Difference, Gradient Temporal Difference, and Accelerated Gradient Temporal Difference. These learning rates are then corroborated on a navigation problem involving an obstacle, which suggests that learning more slowly may lead to improved limit points, providing insight into the interplay between optimization and generalization in reinforcement learning.

Luiz Chamon

Wednesday, February 19th, 2020
Towne 337

Constrained Statistical Learning

Bio: Luiz Chamon is a Ph.D. candidate in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He received the B.Sc. and M.Sc. degree in electrical engineering from the University of São Paulo, Brazil, in 2011 and 2015 and was an undergraduate exchange student at the Masters in Acoustics of the École Centrale de Lyon, France, in 2009. In 2018, he was recognized by the IEEE Signal Processing Society for his distinguished work for the editorial board of the IEEE Transactions on Signal Processing. His research focuses on optimization theory with applications to signal processing, control, and statistics.

Read Luiz's Abstract

Abstract: Information processing and autonomous systems have become ubiquitous in modern life and as their societal impact increases, so does the need to curtail their behavior. Recent failures of learning-based solutions have shown that, left untethered, they are susceptible to tampering and prone to prejudiced and unsafe actions. Currently, this issue is tackled by leveraging domain expert knowledge to either construct models that embed the desired properties or tune the learning objective so as to promote them. However, the growing scale and complexity of modern information processing and autonomous systems renders this manual behavior tuning infeasible. Already today, explainability, interpretability, and transparency combined with human judgment are no longer enough to design systems that perform according to specifications. This talk therefore proposes to explicitly impose learning constraints instead. It discusses preliminary results and perspectives on the theory of constrained statistical learning that addresses the challenge of solving these statistical and often non-convex constrained problems. Infinite dimensionality and rich finite dimensional representations will be the key ingredients to tackle this issue in practical settings. This general theory can be applied to solve problems involving sparsity, nonlinear modeling, fairness in neural networks, and safe reinforcement learning.

Henry Shulevitz

Wednesday, February 26th, 2020
Towne 337

Directed Assembly of Quantum Emitters

Bio: Henry received a B.A. from Oberlin College and a B.S from Columbia University in 2017. He is now pursuing a Ph.D. in Electrical Engineering at the University of Pennsylvania, where he is co-advised by Professor Cherie Kagan and Professor Lee Bassett. Henry’s research interests involve the intersections of diamond photonics, plasmonic enhancements and multiparticle assembly with the goal of creating improved quantum device.

Read Henry's Abstract

Quantum technologies have the potential to revolutionize a wide range of fields, from computation to communication and sensing. The physical platforms for developing such technologies, however, remain in their infancy. The nitrogen-vacancy (NV) center in a nanodiamond functions as an optically addressable spin qubits with high levels of environmental sensitivity and room-temperature quantum coherence. Existing methods for synthesizing nanodiamonds yield particles that vary significantly in size and shape. The heterogeneity in nanodiamond morphology affects the quantum properties of the embedded spin qubits in ways that remain poorly understood. Accordingly, these material issues have stymied efforts to incorporate nanodiamonds in photonic structures or multiparticle assemblies. Here, we present work to assembly large area uniform arrays of a nanodiamonds. These templated assemblies provide not only a method for systematically studying nanodiamonds as a quantum material but also offer a method for the fabrication of more complex devices.

Shaoru Chen

Wednesday, March 4 2020
Towne 337

Robust model predictive control via system level synthesis

Bio: Shaoru Chen received his B.E. degree at Zhejiang University, China in 2017. He is currently a third-year PhD student in the ESE department, University of Pennsylvania, where he is advised by Prof. Victor Preciado. His research interests include data-driven control and nonlinear control.

Read Shaoru's Abstract

We consider the robust model predictive control (MPC) of a linear time-varying (LTV) system with norm bounded disturbances and model uncertainty, wherein a series of constrained optimal control problems (OCPs) are solved. Guaranteeing robust feasibility of these OCPs is challenging, due to both disturbances perturbing the predicted states, and model uncertainty which can render the closed-loop system unstable. As such, a trade-off between the numerical tractability and conservativeness of the solutions is often required. We use the System Level Synthesis (SLS) framework to reformulate these constrained OCPs over closed-loop system responses, and show that this allows us to transparently account for norm bounded additive disturbances and LTV model uncertainty by computing robust state feedback policies. We further show that by exploiting the underlying linear-fractional structure of the resulting robust OCPs, we can significantly reduce the conservatism of existing SLS-based robust control methods.

Fall 2019 Series - Archive

Juan Cervino

Wednesday, October 2nd, 2019
Raisler Lounge

“Meta-Learning through Coupled Optimization in Reproducing Kernel Hilbert Spaces”

Bio: Juan received received the B.Sc. degree in electrical engineering from the Universidad de la República 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.

 

Read Juan's Abstract

Abstract: In this talk we consider the problem of meta-learning, consisting of building policies that achieve good generalization performance and  adapt quickly to different tasks. We introduce meta-learning through the coupled optimization of a set of rewards that are defined for different tasks. This coupling is effected by a projection step that brings the task-specific policies close to a central one which combines the information collected across tasks. While our initial meta-learning formulation is widely general, and connects with state-of-the art strategies, we will focus on the case of reinforcement learning.

Ehsan Nahvi

Wednesday, October 16th, 2019
Raisler Lounge

“Photonic Doping of ENZ Metastructures & Potential Applications”

Bio:  Ehsan received his bachelor’s degree in electrical engineering from Sharif University of Technology, Iran in 2016. He is now a fourth year PhD student in the Engheta Lab, in the ESE department at the University of Pennsylvania. His core research projects focus on applications of doped ENZ metastructures, as well as extremely tunable Smith-Purcell radiation from an electron beam moving adjacent to spatio-temporally modulated substrates.

Read Ehsan's Abstract

Abstract:  Epsilon-near-zero (ENZ) hosts doped with non-magnetic dielectric inclusions have been shown to exhibit a highly tunable magnetic permeability. In this talk, we discuss how such a peculiar magnetic response may be exploited to realize a broad range of novel functionalities, such as impedance matching, enhanced magnetic nonlinearity, nonlinear absorbers, optical bistability, tunable electric field enhancement.

Fernando Gama

Wednesday, October 30th, 2019
Raisler Lounge

“Graph Convolutional Neural Networks”

Bio: Fernando Gama received the electronic engineer degree from the University of Buenos Aires, Argentina, in 2013, and the M.A. degree in statistics from the Wharton School, University of Pennsylvania, Philadelphia, PA, USA, in 2017. He is currently working towards the Ph.D. degree with the Department of Electrical and Systems Engineering, at the University of Pennsylvania. He has been a visiting researcher at TU Delft, the Netherlands, in 2017 and a research intern at Facebook Artificial Intelligence Research, Montreal, Canada, in 2018. His research interests are in the field of information processing and machine learning over network data. He has been awarded a Fulbright scholarship for international students.

Read Fernando's Abstract

Abstract: Convolutional neural networks (CNNs) restrict the linear operation of neural networks to be a convolution with a bank of learned filters. This makes them suitable for learning tasks based on data that exhibit regular structure. The use of convolutions, however, makes them unsuitable for processing data that do not exhibit such a regular structure. Graph signal processing (GSP) has emerged as a powerful alternative to process signals whose irregular structure can be described by a graph. Central to GSP is the notion of graph convolutions which can be used to define convolutional graph neural networks (GNNs). In this paper, we show that the graph convolution can be interpreted as either a diffusion or aggregation operation. When combined with nonlinear processing, these different interpretations lead to different generalizations which we term selection and aggregation GNNs. The selection GNN relies on linear combinations of signal diffusions at different resolutions combined with node-wise nonlinearities. The aggregation GNN relies on linear combinations of neighborhood averages of different depth. Both of these models particularize to regular CNNs when applied to time signals but are different when applied to arbitrary graphs. Numerical evaluations show different levels of performance for selection and aggregation GNNs.

Anastasios Tsiamis

Wednesday, November 6th, 2019
Raisler Lounge

“Finite Sample Analysis of System Identification”

Bio: Anastasios Tsiamis received the Diploma degree in electrical and computer engineering from the National Technical University of Athens, Greece, in 2014. He is now a fifth year PhD student in the Department of Electrical and Systems Engineering at the University of Pennsylvania. His research interests include learning in control, system identification, security and privacy in networked control systems.

Read Anastasios' Abstract

Abstract: System identification methods often return parameters which deviate from the true ones, especially when the number of input/output data is finite.
Previously, asymptotic methods have been employed to compute bounds for the system identification parameter errors. However, such methods are valid when the number of data goes to infinity. In this work, we analyze the finite sample complexity of stochastic system identification using modern tools from machine learning and statistics. An unknown discrete-time linear system evolves over time under Gaussian noise without external inputs. The objective is to recover the system parameters as well as the Kalman filter gain, given a single trajectory of output measurements over a finite horizon of length N. Based on a subspace identification algorithm and a finite number N of output samples, we provide non-asymptotic high-probability confidence intervals for the parameter estimation errors. Our analysis uses recent results from random matrix theory, self-normalized martingales and SVD robustness in order to show that with high probability the estimation errors decrease with a rate of inverse of the square root of N. Our non-asymptotic bounds not only agree with classical asymptotic results, but are also valid even when the system is mildly non-stationary (marginally stable and non-explosive).

Alp Aydinoglu

Wednesday, November 13th, 2019
Raisler Lounge

“Contact-Aware Controller Design for Complementarity Systems”

Bio: Alp Aydinoglu received B.S. degrees in control engineering and communications engineering from Istanbul Technical University in 2016 and 2017 respectively.  He is now a PhD student in the Department of Electrical and Systems Engineering at the University of Pennsylvania. His current research interests include hybrid dynamical systems, complementarity systems and contact mechanics.

Read Alp's Abstract

Abstract: While many robotic tasks, like manipulation and locomotion, are fundamentally based in making and breaking contact with the environment, state-of-the-art control policies struggle to deal with the hybrid nature of multi-contact motion. Such controllers often rely heavily upon heuristics or, due to the combinatoric structure in the dynamics, are unsuitable for real-time control. Principled deployment of tactile sensors offers a promising mechanism for stable and robust control, but modern approaches often use this data in an ad hoc manner, for instance to guide guarded moves. In this work, by exploiting the complementarity structure of contact dynamics, we propose a control framework which can close the loop on rich, tactile sensors. Critically, this framework is non-combinatoric, enabling optimization algorithms to automatically synthesize provably stable control policies.

Shaoru Chen

Wednesday, November 20th, 2019
Raisler Lounge

“Safety Verification of Nonlinear Polynomial System via Occupation Measures”

Bio: Shaoru Chen received his Bachelor of Engineering degree in Automation from Zhejiang University, China in 2017. He is now a third-year PhD student in the Department of Electrical and Systems Engineering at the University of Pennsylvania. His research interest is in robust and nonlinear control.

Read Shaoru's Abstract

Abstract: In this talk, we introduce a flexible notion of safety verification for nonlinear autonomous systems by measuring how much time the system spends in given unsafe regions. We consider this problem in the particular case of nonlinear systems with a polynomial dynamics and unsafe regions described by a collection of polynomial inequalities. In this context, we can quantify the amount of time spent in the unsafe regions as the solution to an infinite-dimensional linear program (LP). We approximate the solution to the infinite-dimensional LP using a hierarchy of finite-dimensional semidefinite programs (SDPs). The solutions to the SDPs in this hierarchy provide monotonically converging upper bounds on the optimal solution to the infinite-dimensional LP.

Yu-Ming Chen

Wednesday, November 20th, 2019
Raisler Lounge

“Optimal reduced-order modeling of bipedal locomotion”

Bio: Yu-Ming Chen is a PhD student in the Department of Electrical and Systems Engineering at the University of Pennsylvania, supervised by Professor Michael Posa. He has a Bachelor’s degree in Physics from National Taiwan University and a Master’s degree in Robotics from the University of Michigan, Ann Arbor. His interests include legged locomotion, optimization and control.

Read Yu-Ming's Abstract

Abstract: State-of-the-art approaches to legged robots are widely dependent on the use of models like the linear inverted pendulum (LIP) and the spring-loaded inverted pendulum (SLIP), popular because their simplicity enables a wide array of tools for planning, control, and analysis. However, they inevitably limit the ability to execute complex tasks or agile maneuvers. In this work, we aim to automatically synthesize models that remain low-dimensional but retain the capabilities of the high-dimensional system. For example, if one were to restore a small degree of complexity to LIP, SLIP, or a similar model, our approach discovers the form of that additional complexity which optimizes performance. We define a class of reduced-order models and provide an algorithm for optimization within this class. To demonstrate our method, we optimize models for walking at a range of speeds and ground inclines, for both a five-link model and the Cassie bipedal robot.

Yuanlong Xiao

Wednesday, December 4th, 2019
Raisler Lounge

“Reducing FPGA Compile Time with Separate Compilation for FPGA Building Blocks”

Bio: Yuanlong Xiao receives his Master Degree of Microelectronics from Fudan University, China in 2017 and Bachelor degree of Microelectronics from Sun Yat-sen University, China in 2014. His research interest is quick computing mapping, reconfigurable computing and FPGA chip design.

Read Yuanlong's Abstract

Abstract: In this talk, we are going to talk about how to realized quick FPGA computing mapping. Today’s FPGA compilation is slow because it compiles and co-optimizes the entire design in one monolithic mapping flow. This achieves high quality results but also means a long edit-compile-debug loop that slows development and limits the scope of design-space exploration. We introduce PRflow that uses partial reconfiguration and an overlay packet-switched network to separate the HLS-to-bitstream compilation problem for individual components of the FPGA design. This separation allows both the incremental compilation, where a single component can be recompiled without recompiling the entire design, and parallel compilation, where all the components are compiled in parallel. Both uses reduce the compilation time. Mapping the Rosetta Benchmarks to a Xilinx XCZU9EG, we show compilation times reduce from 42 minutes to 12 minutes (one case from 160 minutes to 18 minutes) when running on top of commercial tools from Xilinx. Using Symbiflow (Project X-Ray/Yosys/VPR), we show preliminary evidence we can further reduce most compile times under 5 minutes, with some components mapping in less than 2 minutes.

Shaoru Chen

Wednesday, December 11th, 2019
Raisler Lounge

“Safety Verification of Nonlinear Polynomial System via Occupation Measures”

Bio: Shaoru Chen received his Bachelor of Engineering degree in Automation from Zhejiang University, China in 2017. He is now a third-year PhD student in the Department of Electrical and Systems Engineering at the University of Pennsylvania. His research interest is in robust and nonlinear control.

Read Shaoru's Abstract

Abstract: In this talk, we introduce a flexible notion of safety verification for nonlinear autonomous systems by measuring how much time the system spends in given unsafe regions. We consider this problem in the particular case of nonlinear systems with a polynomial dynamics and unsafe regions described by a collection of polynomial inequalities. In this context, we can quantify the amount of time spent in the unsafe regions as the solution to an infinite-dimensional linear program (LP). We approximate the solution to the infinite-dimensional LP using a hierarchy of finite-dimensional semidefinite programs (SDPs). The solutions to the SDPs in this hierarchy provide monotonically converging upper bounds on the optimal solution to the infinite-dimensional LP.