Unless otherwise noted, Fall 2023 Distinguished Lecture Series events will be held on Thursdays at 12:45 pm in Stanley Thomas 316. All lectures will be available for in-person attendance as well as remote attendance via Zoom. Current Tulane faculty, staff, and students are encouraged to attend in person. Zoom details will be provided via the announcement listserv, or you may email firstname.lastname@example.org to request the corresponding link. If you would like to receive notifications about upcoming events, you can subscribe to the announcement listserv.
Fall 2023 Distinguished Lecture Series
Closing the Gap between Quantum Algorithms and Machines with Hardware-Software Co-Design
Fred Chong | The University of Chicago
Abstract: Quantum computing is at an inflection point, where 433-qubit machines are deployed, and 1000-qubit machines are only a few years away. These machines have the potential to fundamentally change our concept of what is computable and demonstrate practical applications in areas such as quantum chemistry, optimization, and quantum simulation. Yet a significant resource gap remains between practical quantum algorithms and real machines. A promising approach to closing this gap is to design software that is aware of the key physical properties of emerging quantum technologies. I will illustrate this approach with some of our recent work that focuses on techniques that break traditional abstractions and inform hardware design, including compiling programs directly to analog control pulses, computing with ternary quantum bits, 2.5D architectures for surface codes, and exploiting long-distance communication and tolerating atom loss in neutral-atom machines.
About the Speaker: Fred Chong is the Seymour Goodman Professor in the Department of Computer Science at the University of Chicago and the Chief Scientist for Quantum Software at Infleqtion. He is also the Lead Principal Investigator for the EPiQC Project (Enabling Practical-scale Quantum Computing), an NSF Expedition in Computing project. Chong is a member of the National Quantum Advisory Committee (NQIAC), which provides advice to the President on the National Quantum Initiative Program. In 2020, he co-founded Super.tech, a quantum software company, which was acquired by Infleqtion (formerly ColdQuanta) in 2022. Chong received his Ph.D. from MIT in 1996 and was a faculty member and Chancellor's fellow at UC Davis from 1997-2005. He was also a Professor of Computer Science, Director of Computer Engineering, and Director of the Greenscale Center for Energy-Efficient Computing at UCSB from 2005-2015. He is a fellow of the IEEE and a recipient of the NSF CAREER award, the Intel Outstanding Researcher Award, and 13 best paper awards. His research interests include emerging technologies for computing, quantum computing, multicore and embedded architectures, computer security, and sustainable computing. .
The Evolution of Computer Hardware and Software for Data-centric Computing
Xiaodong Zhang | The Ohio State University
Abstract: As Moore's Law ended, and Dennard Scaling reached its physical limitations, we have entered a new computing era that is marked by the coexistence of various parallel and highly specialized hardware accelerators with general-purpose processors. Over many years of development, the CPU-centric ecosystem has evolved into a one-size-fits-all environment, accommodating a diverse range of applications. However, the efficiency in performance, computing power and energy consumption has been continuously declining, making the general-purpose computing ecosystem unsustainable for the growing demands of data-centric computing.
In this presentation, I will explore the constraints and obstacles inherent in our current computing ecosystem, driven by Moore's Law. I will also provide case studies to support the evolution of computer hardware and software for high-performance data processing, featuring advanced hardware components such as GPUs, RDMA, and other relevant technologies. All associated algorithms and software implementations are open source, with some having been integrated into production systems.
About the Speaker: Xiaodong Zhang is a University Distinguished Scholar and the Robert M. Critchfield Professor in Engineering at the Ohio State University. His research interests focus on data management in computer and distributed systems. Driven by a commitment to translate his academic research solutions into cutting-edge technology, he has made continuous efforts in advancing the design and implementation of several major production systems. He was recognized by the 2020 ACM Microarchitecture Test of Time Award for his contributions on memory architecture design. He received his Ph.D. in Computer Science from University of Colorado at Boulder, where he was honored with a Distinguished Engineering Alumni Award in 2011. He received the Education Leadership Award from the Lutron Foundation for chairing the Department of Computer Science and Engineering at Ohio State from 2006 to 2018. He is a Fellow of the ACM, and a Fellow of the IEEE.
Fall 2022 Distinguished Lecture Series
Blockchains and Related Technologies: Which Ideas are Likely to Endure?
Maurice Herlihy | Brown University
Abstract: Blockchains and distributed ledgers have become the focus of much recent attention. Like many innovations, this field emerged from outside mainstream computer science, although almost all the component ideas were already well-known. As a new area driven mostly by technological and financial innovations, it can be difficult to distinguish accomplishment from aspiration, and especially difficult to tell which ideas are of transient versus lasting interest. This talk surveys the theory and practice of blockchain-based distributed systems from the point of view of classical distributed computing, along with opinions about promising future research directions. This talk is intended for a general audience.
About the Speaker: Maurice Herlihy has an A.B. in Mathematics from Harvard University, and a Ph.D. in Computer Science from M.I.T. He has served on the faculty of Carnegie Mellon University and the staff of DEC Cambridge Research Lab. He is the recipient of the 2003 Edsger W. Dijkstra Prize in Distributed Computing, the 2004 Gödel Prize in theoretical computer science, the 2008 ISCA Influential Paper Award, the 2012 Dijkstra Prize, and the 2013 W. Wallace McDowell Award. He received a 2012 Fulbright Distinguished Chair in the Natural Sciences and Engineering Lecturing Fellowship, and he is a fellow of the ACM, a fellow of the National Academy of Inventors, the National Academy of Engineering, and the National Academy of Arts and Sciences. In 2022, he won his third Dijkstra Prize.
Outracing Champion Gran Turismo Drivers with Deep Reinforcement Learning
Peter Stone | University of Texas at Austin
This talk will be held on Friday, December 2nd, at 4:00 p.m. CST in Stanley Thomas Hall, Room 302. Zoom details will also be provided via the announcement listserv, or you may email email@example.com to request the corresponding link.
Abstract: Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical manoeuvres to pass or block opponents while operating their vehicles at their traction limits. Racing simulations, such as the PlayStation game Gran Turismo, faithfully reproduce the non-linear control challenges of real race cars while also encapsulating the complex multi-agent interactions. Here we describe how we trained agents for Gran Turismo that can compete with the world's best e-sports drivers. We combine state-of-the-art, model-free, deep reinforcement learning algorithms with mixed-scenario training to learn an integrated control policy that combines exceptional speed with impressive tactics. In addition, we construct a reward function that enables the agent to be competitive while adhering to racing's important, but under-specified, sportsmanship rules. We demonstrate the capabilities of our agent, Gran Turismo Sophy, by winning a head-to-head competition against four of the world's best Gran Turismo drivers. By describing how we trained championship-level racers, we demonstrate the possibilities and challenges of using these techniques to control complex dynamical systems in domains where agents must respect imprecisely defined human norms.
About the Speaker: Dr. Peter Stone holds the Truchard Foundation Chair in Computer Science at the University of Texas at Austin. He is Associate Chair of the Computer Science Department, as well as Director of Texas Robotics. In 2013 he was awarded the University of Texas System Regents' Outstanding Teaching Award and in 2014 he was inducted into the UT Austin Academy of Distinguished Teachers, earning him the title of University Distinguished Teaching Professor. Professor Stone's research interests in Artificial Intelligence include machine learning (especially reinforcement learning), multiagent systems, and robotics. Professor Stone received his Ph.D in Computer Science in 1998 from Carnegie Mellon University. From 1999 to 2002 he was a Senior Technical Staff Member in the Artificial Intelligence Principles Research Department at AT&T Labs - Research. He is an Alfred P. Sloan Research Fellow, Guggenheim Fellow, AAAI Fellow, IEEE Fellow, AAAS Fellow, ACM Fellow, Fulbright Scholar, and 2004 ONR Young Investigator. In 2007 he received the prestigious IJCAI Computers and Thought Award, given biannually to the top AI researcher under the age of 35, and in 2016 he was awarded the ACM/SIGAI Autonomous Agents Research Award. Professor Stone co-founded Cogitai, Inc., a startup company focused on continual learning, in 2015, and currently serves as Executive Director of Sony AI America.