Ph.D. Colloquium Archive

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Friday, November 5, 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.

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.

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.

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.

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.

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.