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 2022 Series

Alex Nguyen-LeAlex Nguyen-Le

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.

Read Alex's 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.

Robert BrobergRobert Broberg

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.

Read Robert's 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.

Shaoru ChenShaoru Chen

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.

Read Shaoru's 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.

Eric LeiEric Lei

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.

Read Eric's 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.

Zhiyang WangZhiyang Wang

Friday, May 6, 2022
Wu & 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 manifolds.

Read Zhiyang's 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.

Fall 2021 Series

Harshat Kumar

Friday, October 1, 2021
Wu & Chen Auditorium

Zeroth-order Deterministic Policy Gradient

Bio: Harshat Kumar received the B.S. degree in electrical and computer engineering from Rutgers University in 2017 and MS degree in Robotics from the University of Pennsylvania in 2019. He has been working toward the Ph.D. in electrical and systems engineering at University of Pennsylvania, Philadelphia, PA, USA, since August 2017.

Read Harshat's Abstract

Deterministic Policy Gradient (DPG) removes a level of randomness from standard randomized-action Policy Gradient (PG), and demonstrates substantial empirical success for tackling complex dynamic problems involving Markov decision processes. At the same time, though, DPG loses its ability to learn in a model-free (i.e., actor-only) fashion, frequently necessitating the use of critics in order to obtain consistent estimates of the associated policy-reward gradient. In this work, we introduce Zeroth-order Deterministic Policy Gradient (ZDPG), which approximates policy-reward gradients via two-point stochastic evaluations of the -function, constructed by properly designed low-dimensional action-space perturbations. Exploiting the idea of random horizon rollouts for obtaining unbiased estimates of the -function, ZDPG lifts the dependence on critics and restores true model-free policy learning, while enjoying built-in and provable algorithmic stability. Additionally, we present new finite sample complexity bounds for ZDPG, which improve upon existing results by up to two orders of magnitude. Our findings are supported by several numerical experiments, which showcase the effectiveness of ZDPG in a practical setting, and its advantages over both PG and Baseline PG.

Raj Patel

Friday, October 8, 2021
Wu & Chen Auditorium

Probing the Optical Dynamics of Quantum Emitters in Hexagonal Boron Nitride

Bio: Raj received his M.Sc. in Physics and B.E. in Mechanical Engineering from BITS Pilani – Goa, India in 2015. He received an M.S.E. in Materials Science & Engineering from University of Pennsylvania in 2017. He is now pursuing his Ph.D. in Electrical & Systems Engineering, where he works in the Quantum Engineering Laboratory, advised by Prof. Lee Bassett. His research interests are in understanding spin-based quantum emitters in two-dimensional materials and other solid-state systems for quantum sensing and computation applications.

Read Raj's Abstract

Hexagonal boron nitride (h-BN) is a van der Waals material that hosts defect-based quantum emitters (QEs) at room temperature. Recent observations suggest the existence of multiple distinct defect structures hosting QEs. Theoretical proposals suggest vacancies, their complexes and substitutional atoms as likely defect candidates. However, experimental identification of the QEs’ electronic structure is lacking, and key details of the QEs’ charge and spin properties remain unknown. In this talk, we discuss probing the optical dynamics of QEs in h-BN using photon emission statistics and photoluminescence spectroscopy with the goal of predicting the electronic level structure. We probe the optical dynamics of the QEs at various excitation powers and wavelengths and propose an electronic level structure which could give rise to the experimental observations.

Juan Cervino

Friday, October 15, 2021
Wu & Chen Auditorium

Increase and Conquer: Training Graph Neural Networks in Growing Graphs

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.

Read Juan's Abstract

Graph neural networks (GNNs) use graph convolutions to exploit network invariances and learn meaningful features from network data. However, on large-scale graphs convolutions incur in high computational cost, leading to scalability limitations. Leveraging the graphon — the limit object of a graph — in this paper we consider the problem of learning a graphon neural network (WNN) — the limit object of a GNN — by training GNNs on graphs sampled Bernoulli from the graphon. Under smoothness conditions, we show that: (i) the expected distance between the learning steps on the GNN and on the WNN decreases asymptotically with the size of the graph, and (ii) when training on a sequence of growing graphs, gradient descent follows the learning direction of the WNN. Inspired by these results, we propose a novel algorithm to learn GNNs on large-scale graphs that, starting from a moderate number of nodes, successively increases the size of the graph during training. This algorithm is benchmarked on both a recommendation system and a decentralized control problem where it is shown to retain comparable performance, to its large-scale counterpart, at a reduced computational cost.

Nima LeclercNima Leclerc

Friday, October 22, 2021
Wu & Chen Auditorium

High fidelity single and two-qubit gates in silicon quantum processors

Bio: Nima Leclerc received his BS in Materials Science and Engineering and a minor in Computer Science at Cornell University in 2020. He is currently a PhD student in Electrical Engineering at the University of Pennsylvania in Anthony Sigillito’s group, working on improved quantum control and fabrication of spin-based quantum computers. His research interests intersect condensed matter physics, quantum control, machine learning, and optimization. He is also a 2021-2022 Graduate Associate at Penn’s Perry World House, developing policy around the national security threats of emerging quantum technologies.

Read Nima's Abstract

Quantum computers promise to revolutionize nearly every aspect of society through drug development, materials discovery and optimization, and the exponential speedup of certain computational tasks. However, current devices do not have sufficient numbers of qubits (quantum bits) or high enough control fidelities (accuracies) for fault-tolerant quantum computing. Silicon quantum dot (QD) qubits have long coherence times on the order of milliseconds, small feature sizes, and are compatible with standard semiconductor fabrication technologies making them a very promising candidate for intermediate scale quantum computing. Here, we present approaches leading to high-fidelity quantum control in silicon quantum dot processors by fabricating devices resilient to charge noise and developing numerically optimized control protocols. Demonstrating single and 2-qubit gates exceeding fault-tolerant fidelities (>99 %) is necessary for general-purpose quantum computation and is currently an active area of research. Two effects that lead to infidelity are noise and decoherence, this work will address the former.  We present a systematic approach to identify and suppress noise sources using optimized microwave pulse shapes and magnetic fields. Significant work over the past decade has been dedicated to fabricating high-quality silicon devices in a scalable manner and developing proof-of-concept protocols for single and 2-qubit gates, but their control fidelities remain significantly below the fault-tolerant thresholds. This work bridges this gap to achieve universal quantum computing in silicon above the fault-tolerant threshold by developing a set of techniques for high-fidelity quantum operations using new device architectures and optimized control pulses.

Yuanlong XiaoYuanlong Xiao

Friday, October 29, 2021
Wu & Chen Auditorium

Software-like Fast FPGA Compilation

Bio: Yuanlong Xiao received his Master’s Degree in Microelectronics from Fudan University, China in 2017 and Bachelor’s degree in Microelectronics from Sun Yat-sen University, China in 2014. His research interest is quick computing mapping, reconfigurable computing, and FPGA chip design. He is now a Ph.D. student at the University of Pennsylvania advised by Prof. Andre’ DeHon.

Read Yuanlong's Abstract

FPGA-based accelerators are demonstrating significant absolute performance and energy efficiency compared with general-purpose CPUs. While FPGA computations can now be described in the standard, programming languages, like C, development for FPGA accelerators remains tedious and inaccessible to modern software engineers. Slow compiles (potentially taking tens of hours) inhibit the rapid, incremental refinement of designs that is the hallmark of modern software engineering. To address this issue, we introduce separate compilation and linkage into the FPGA design flow, providing faster design turns more familiar to software development. To realize this flow, we provide abstractions, compiler options, and compiler flow that allow the same C source code to be compiled to processor cores in seconds and to FPGA regions in minutes, providing the missing -O0 and -O1 options familiar in software development. This raises the FPGA programming level and standardizes the programming experience, bringing FPGA-based accelerators into a more familiar software platform ecosystem for software engineers.

Dug NguyenDuc Nguyen

Friday, November5 , 2021
Wu & Chen Auditorium

Sums-of-squares and the proof-to-algorithm paradigm

Bio: Duc Nguyen is a 3rd year Ph.D. candidate in the Computer & Information System department of the University of Pennsylvania. He is advised by Prof. Shivani Agarwal. His research interests are in statistical machine learning and learning to rank.

Read Duc's Abstract

In recent years, the intersection between proof systems and algorithmic design has received a lot of interest within the computer science community. One of these proof systems, the sums-of-square hierarchy, has been applied with great success to many long standing problems in theoretical computer science and machine learning such as learning and clustering a mixture of Gaussians, matrix and tensor completion, etc. At the core lies a relatively straightforward but powerful idea: if there exists a sums-of-squares proof of a polynomial inequality, there exists a polynomial-time algorithm to find such a proof. In this talk, we will explore the basic foundations of the sums-of-squares proof system from a user’s point of view.

Tzu-Yung HuangTzu-Yung Huang

Friday, November 12 , 2021
Wu & Chen Auditorium

Material Retention Analysis using Dynamic Light Scattering and UV-vis Spectroscopy

Bio: Originally from Taipei, Taiwan, Yung received their B.S.E. in Electrical Engineering from Princeton University in 2015. Their undergraduate research focused on the environmental and biomedical applications of quantum cascade lasers, and on the development of GaN as novel materials for quantum cascade emitters. Since joining the Quantum Engineering Laboratory at Penn, they have shifted towards exploring the potential of bulk and nano diamonds as platforms for integrated devices. By leveraging advances in nanophotonics design and fabrication, as well as collaborations with Prof. Aflatouni’s and Porf. Tsourkas’ groups at Penn, Yung’s current research focuses on realizing novel architectures for compact, diamond-based quantum devices for applications in quantum communication and sensing.

Read Tzu-Yung's Abstract

Nanoparticles are emerging platforms for quantum sensing and targeted nanomedicine. Advances in integrated quantum sensors and functionalized nanoparticles have catalyzed a demand for colloidal nanoparticle devices and systems. Extraction of particle information, such as size, presence of agglomeration, and concentration, is an essential step in developing new protocols and processes for novel colloidal devices. However, existing characterization methods either require large amounts of source material, have material restrictions, or are destructive against the process being assessed. Here, we present an analytical method for evaluating change in total mass using dynamic light scattering and UV-vis measurements. We demonstrate the effectiveness of this method for dielectric and metallic nanoparticles and verify the results using ion-coupled plasma mass spectroscopy.

Alp AydinogluAlp Aydinoglu

Friday, November 19 , 2021
Wu & Chen Auditorium

Control of Multi-Contact Systems

Bio: Alp completed his B.S. in Control Engineering from Istanbul Technical University in 2017 and is currently pursuing his Ph.D. in Penn working with Michael Posa. His research emphasizes control of multi-contact systems.

Read Alp's Abstract

Many robotic tasks, like manipulation and locomotion, fundamentally include making and breaking contact with the environment. However, 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 combinatorial structure in the dynamics, are unsuitable for real-time control. A subset of controllers, such as neural network controllers, can achieve satisfactory performance but they lack guarantees. In this talk, I will present techniques for overcoming these challenges in order to design controllers and verify stability of multi-contact systems.

Asma FallahAsma Fallah

Friday, December 3 , 2021
Wu & Chen Auditorium

Nonreciprocity via Interaction of Electromagnetic Waves with Swift Electron Beams

Bio: Asma received her BSc degree in electrical engineering from University of Tehran, Iran in 2016. She is now a fourth-year Ph.D. student in Prof. Engheta group, in ESE department at the University of Pennsylvania. Her research interest are electromagnetic, wave matter interaction, and metamaterial.

Read Asma's Abstract

Breaking the reciprocity of electromagnetic interactions is of paramount importance in photonic and microwave technologies, as it enables unidirectional power flow and other unique electromagnetic phenomena. We have explored a novel method to break the reciprocity of electromagnetic guided waves utilizing an electron beam with a constant velocity. We theoretically explore the break of electromagnetic reciprocity in the presence of swift electron beams in different scenarios, including the interaction of the electron beams with guided and radiated waves, providing the possibility for strong nonreciprocal behavior.

Spring 2020 Series - Archive

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.

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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.

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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.

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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.