Unless otherwise noted, Fall 2024 Distinguished Lecture Series events will be held on Tuesdays or 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 dramil1@tulane.edu to request the corresponding link. If you would like to receive notifications about upcoming events, you can subscribe to the announcement listserv.
Fall 2024 Distinguished Lecture Series
Nov 7
Enhancing healthcare with AI-in-the-loop
Sriraam Natarajan | University of Texas at Dallas
This talk will be held on Thursday, November 7th at 12:45 pm in Stanley Thomas 316. Sponsored in part by the Jurist Center for Artificial Intelligence and the Center for Community-Engaged AI.
Abstract: Historically, Artificial Intelligence has taken a symbolic route for representing and reasoning about objects at a higher-level or a statistical route for learning complex models from large data. To achieve true AI in complex domains such as healthcare, it is necessary to make these different paths meet and enable seamless human interaction. First, I will introduce learning from rich, structured, complex and noisy data. One of the key attractive properties of the learned models is that they use a rich representation for modeling the domain that potentially allows for seam-less human interaction. I will present the recent progress that allows for more reasonable human interaction where the human input is taken as “advice” and the learning algorithm combines this advice with data. I will present these algorithms in the context of several healthcare problems -- learning from electronic health records, clinical studies, and surveys -- and demonstrate the value of involving experts during learning.
About the Speaker: Sriraam Natarajan is a Professor and the Director for Center for ML at the Department of Computer Science at University of Texas Dallas, a hessian.AI fellow at TU Darmstadt and a RBDSCAII Distinguished Faculty Fellow at IIT Madras. His research interests lie in the field of Artificial Intelligence, with emphasis on Machine Learning, Statistical Relational Learning and AI, Reinforcement Learning, Graphical Models and Biomedical Applications. He is a AAAI senior member and has received the Young Investigator award from US Army Research Office, President's award for excellence in graduate teaching, several industry awards, ECSS Graduate teaching award from UTD and the IU trustees Teaching Award from Indiana University. He was the program chair of AAAI 2024, the general chair of CoDS-COMAD 2024, program co-chair of SDM 2020 and ACM CoDS-COMAD 2020 conferences. He was the specialty chief editor of Frontiers in ML and AI journal, and is an associate editor of JAIR, DAMI and Big Data journals.
Nov 18
Title: Traversing the AI Innovation to Translation Journey: Advancing Common Good
Nitesh Chawla | University of Notre Dame
This talk will be held on Monday, November 18th, at 11:00 am in Stibbs 203, Lavin-Bernick Center. Sponsored in part by the Jurist Center for Artificial Intelligence and the Center for Community-Engaged AI.
Abstract: In this talk, I will present our work on fundamental advances in AI, inspired by interdisciplinary problem statements and societal challenges. I will highlight our innovation journey that encapsulates both the opportunities and challenges inherent in harnessing the full potential of AI in addressing a wicked problem, in particular highlighting our work in healthcare and scientific discovery.
About the Speaker: Nitesh Chawla is the Frank M. Freimann Professor of Computer Science and Engineering and the Founding Director of the Lucy Family Institute for Data and Society at the University of Notre Dame. His research is focused on artificial intelligence and data science and is also motivated by the question of how technology can advance the common good through convergence research. He is a Fellow of ACM, AAAI, AAAS, and IEEE. He is the recipient of multiple awards, including the National Academy of Engineers New Faculty Fellowship, IEEE CIS Outstanding Early Career Award, Rodney F. Ganey Community Impact Award, IBM Watson Faculty Award, and the 1st Source Bank Technology Commercialization Award. He is co-founder of Aunalytics, a data science software and cloud computing company.
Nov 21
Quantum Topology and ISA Collaborative Optimizations for Reduced Noise NISQ Circuits
Alex K. Jones | Syracuse University
This talk will be held on Thursday, November 21st at 12:45 pm in Stanley Thomas 316.
Abstract: The promise of quantum computation comes from the ability to entangle multiple qubits to solve complex computing problems. Current quantum computer offerings, such as the superconducting-based quantum machines offered by IBM and Google, are strongly entrenched in the Noisy Intermediate Scale Quantum (NISQ) class of machines. Unfortunately, the noise in these machines limits the size of quantum circuits that can be realized before noise channels overcome the work by the quantum circuit. By working collaboratively between the quantum device design, quantum architecture, and quantum transpilation flow, it is possible to significantly decrease circuit depth and increase the problem size that can be computed in NISQ machines. In this talk I will present our recent findings for topology and gate co-design using Transmon qubits with SNAIL couplers. I will discuss methods for reducing two-qubit gate pulse lengths to provide greater control over quantum computations while also addressing the overhead of one-qubit gates. Using a concept called parallel drive, I will show how one-qubit gates can sometimes be eliminated by driving two-qubit and one-qubit gates at the same time without creating substantial calibration problems. I will also discuss a new transpilation approach called MIRAGE that takes advantage of mirror gates to improve circuit depth in the case of restricted radix topologies and for better decomposition in high-radix topologies.
About the Speaker: Alex K. Jones is the Klaus Schroder Endowed Chair Professor of Engineering and Computer Science and Department Chair of EECS (Electrical Engineering and Computer Science) at Syracuse University. Previously from 2003—2024 he was a Professor at the University of Pittsburgh. From 2020—2024 he served in a variety of roles at the NSF including Program Director and Cluster Lead in CISE/CNS/CSR (Computer and Information Science and Engineering/Computer and Network Systems/Computer Systems Research) and Deputy Division Director of ENG/ECCS (Engineering/Electrical, Communications and Cyber Systems). Dr. Jones is well known for advancing the field of sustainable computing with full lifecycle carbon modeling and optimization and actively developing nanoscale magnetic memory systems including spin-transfer-torque and Racetrack memories with an emphasis on processing in memory. Recently he has been investigating quantum system codesign including design of basis gates, topologies, and transpilation from resonator devices to systems. His other interests include compilation for configurable systems and architectures, reliability and fault-tolerance, and computing and memories in harsh environments such as space, among others. Dr. Jones' research interests include compilation for configurable systems and architectures, scaled and emerging memory, reliability, fault tolerance, quantum computing, and sustainable computing. He is the author of more than 200 publications in these areas. His research is funded by the NSF, DARPA, NSA, ARO, LPS, foundation grants, and industry. He is the steering committee chair for the IEEE International Green and Sustainable Computing Conference, a topical editor for the IEEE Transactions on Computers, and an associate editor for the IEEE Transactions on Sustainable Computing. Dr. Jones is a Fellow of the IEEE.
Dec 9
It's Not (All) About You: Adventures in Multistakeholder Recommendation
Robin Burke | University of Colorado Boulder
This talk will be held on Monday, December 9, at 11:30 a.m. in Boggs 600. Please note the special weekday, time, and venue for this event. Zoom details will be provided via the announcement listserv, or you may email mrougelot@tulane.edu to request the corresponding link.
Abstract: From their origins in the mid-1990s, recommender systems have been presented as a user-centered technology, aimed at improving information efficiency by helping users find items of interest in large and diverse information spaces. Recommender systems are now ubiquitous in our online environments, and it is has become clear that the reality of these systems is different than their origin story suggests. End users receiving recommendations are only one of multiple groups who are impacted by recommender systems and there are others whose viewpoints are valuable in understanding their effects. The term "multistakeholder recommendation" has emerged to describe research that takes this reality as a starting point and explores the consequences that follow from expanding the set of perspectives to be considered in designing and evaluating recommender systems. In this talk, I will describe recent work from That Recommender Systems Lab in multistakeholder recommendation and an emerging agenda of open questions and problems.
About the Speaker: Robin Burke is a Professor in the Department of Information Science at the University of Colorado, Boulder. He conducts research in personalized recommender systems, a field he helped found and develop. His most recent projects explore fairness, accountability and transparency in recommendation through the integration of objectives from diverse stakeholders. Dr Burke obtained his PhD in Computer Science from Northwestern University in 1993 and a BS in Computer Science from Harvey Mudd College in 1986. Professor Burke is the author of more than 150 peer-reviewed articles in various areas of artificial intelligence including recommender systems, machine learning and information retrieval. His work has received support from the National Science Foundation, the National Endowment for the Humanities, the Fulbright Commission and the MacArthur Foundation, among others.
Fall 2023 Distinguished Lecture Series
Oct 5
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.
Oct 12
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
Oct 20
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.
Dec 2
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 dramil1@tulane.edu 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.
