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Conferences, Lectures, & Seminars
Events for April
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Sanmi (Oluwasanmi) Koyejo (University of Illinois at Urbana-Champaign) - The Measurement and Mismeasurement of Trustworthy ML
Mon, Apr 05, 2021 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Sanmi (Oluwasanmi) Koyejo, University of Illinois at Urbana-Champaign
Talk Title: The Measurement and Mismeasurement of Trustworthy ML
Abstract: Across healthcare, science, and engineering, we increasingly employ machine learning (ML) to automate decision-making that, in turn, affects our lives in profound ways. However, ML can fail, with significant and long-lasting consequences. Reliably measuring such failures is the first step towards building robust and trustworthy learning machines. Consider algorithmic fairness, where widely-deployed fairness metrics can exacerbate group disparities and result in discriminatory outcomes. Moreover, existing metrics are often incompatible. Hence, selecting fairness metrics is an open problem. Measurement is also crucial for robustness, particularly in federated learning with error-prone devices. Here, once again, models constructed using well-accepted robustness metrics can fail. Across ML applications, the dire consequences of mismeasurement are a recurring theme. This talk will outline emerging strategies for addressing the measurement gap in ML and how this impacts trustworthiness.
Biography: Sanmi (Oluwasanmi) Koyejo is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Koyejo's research interests are in developing the principles and practice of trustworthy machine learning. Additionally, Koyejo focuses on applications to neuroscience and healthcare. Koyejo completed his Ph.D. in Electrical Engineering at the University of Texas at Austin, advised by Joydeep Ghosh, and completed postdoctoral research at Stanford University. His postdoctoral research was primarily with Russell A. Poldrack and Pradeep Ravikumar. Koyejo has been the recipient of several awards, including a best paper award from the conference on uncertainty in artificial intelligence (UAI), a Sloan Fellowship, a Kavli Fellowship, an IJCAI early career spotlight, and a trainee award from the Organization for Human Brain Mapping (OHBM). Koyejo serves on the board of the Black in AI organization.
Host: Fei Sha
Audiences: By invitation only.
Contact: Assistant to CS chair
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CS Colloquium: Heni Ben Amor (Arizona State University) - Human-Robot Interactive Collaboration and Communication
Thu, Apr 08, 2021 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Heni Ben Amor, Arizona State University
Talk Title: Human-Robot Interactive Collaboration and Communication
Series: Computer Science Colloquium
Abstract: Autonomous and anthropomorphic robots are poised to play a critical role in manufacturing, healthcare and the services industry in the near future. However, for this vision to become a reality, robots need to efficiently communicate and physically interact with their human partners. Rather than traditional remote controls and programming languages, adaptive and transparent techniques for human-robot collaboration are needed. In particular, robots may need to interpret implicit behavioral cues or explicit instructions and, in turn, generate appropriate responses. In this talk, I will present ongoing work which leverages machine learning (ML), natural language processing and virtual reality to create different modalities for humans and machines to engage in effortless and natural interactions. To this end, I will describe Bayesian Interaction Primitives - an approach for motor skill learning and spatio-temporal modelling in physical human-robot collaboration tasks. Further, I will discuss our recent work on language-conditioned imitation learning and self-supervised learning in interactive tasks. The talk will also cover techniques that enable robots to communicate information back to the human partner via mixed reality projections. To demonstrate these techniques, I will present applications in prosthetics, social robotics, and collaborative assembly.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_0-U51YE7Rx-z99qO4Kj33w
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Heni Ben Amor is an Assistant Professor for robotics at Arizona State University. He is the director of the ASU Interactive Robotics Laboratory. Ben Amor received the NSF CAREER Award, the Fulton Outstanding Assistant Professor Award in 2018, as well as the Daimler-and-Benz Fellowship in 2012. Prior to joining ASU, he was a research scientist at Georgia Tech, a postdoctoral researcher at the Technical University Darmstadt (Germany), and a visiting research scientist in the Intelligent Robotics Lab at the University of Osaka (Japan). His primary research interests lie in the fields of artificial intelligence, machine learning, robotics, and human-robot interaction. Ben Amor received a Ph.D. in computer science from the Technical University Freiberg, focusing on artificial intelligence and machine learning. More information can be found at: http://henibenamor.weebly.com/
Host: Heather Culbertson
Webcast: https://usc.zoom.us/webinar/register/WN_0-U51YE7Rx-z99qO4Kj33wLocation: Online Zoom Webinar
WebCast Link: https://usc.zoom.us/webinar/register/WN_0-U51YE7Rx-z99qO4Kj33w
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CS Distinguished Lecture: Tuomas Sandholm (Carnegie Mellon University, Strategy Robot, Inc., Optimized Markets, Inc., Strategic Machine, Inc.) - What Can and Should Humans Contribute to Superhuman AIs?
Tue, Apr 13, 2021 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Tuomas Sandholm, Carnegie Mellon University, Strategy Robot, Inc., Optimized Markets, Inc., Strategic Machine, Inc.
Talk Title: What Can and Should Humans Contribute to Superhuman AIs?
Series: Computer Science Distinguished Lecture Series
Abstract: I will discuss what humans can and should contribute to superhuman AIs-”not general ones intended to be like humans, but ones for specific applications that make the world a better place. I will discuss how the application should drive research. I will present extensive experiences from having fielded superhuman AIs for combinatorial markets, organ exchanges, and imperfect-information game settings. I will discuss inventing and scoping novel AI applications. I will discuss how humans should supply the value framework while leaving policy optimization and combinatorics for AI. I will cover a framework that separates those ends and means, and conducts future-aware optimization in very-large-scale dynamic problems in a scalable way. I will wonder about the future of science when theorems (not just proofs) and empirical theories need to be so long that they are beyond human comprehension. I will discuss human overconfidence in humans over AI. I will discuss what explainability could be and why in many AI applications it should not be required. Finally, I will suggest flipping ethics around from an ex post discussion activity to a system-design discipline that I coin pre-design ethics.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_xhADAX7FRt-eiAg2fCI5bQ
After registering, attendees will receive a confirmation email containing information about joining the webinar.
Biography: Tuomas Sandholm is Angel Jordan University Professor of Computer Science at Carnegie Mellon University and a serial entrepreneur. His research focuses on the convergence of artificial intelligence, economics, and operations research. He is Co-Director of CMU AI. He is Founder and Director of the Electronic Marketplaces Laboratory.
In parallel with his academic career, he was Founder, Chairman, first CEO, and CTO/Chief Scientist of CombineNet, Inc. from 1997 until its acquisition in 2010. During this period the company commercialized over 800 of the world's largest-scale generalized combinatorial multi-attribute auctions, with over $60 billion in total spend and over $6 billion in generated savings. He is Founder and CEO of Optimized Markets, Inc., which is bringing a new optimization-powered paradigm to advertising campaign sales, scheduling, and pricing in linear and nonlinear TV, display, streaming, and cross-media advertising.
Since 2010, his algorithms have been running the national kidney exchange for UNOS, where they make the kidney exchange transplant plan for 80% of U.S. transplant centers together each week. He also co-invented never-ending altruist-donor-initiated chains, which have become the main modality of kidney exchange worldwide and have led to around 10,000 life-saving transplants. He invented liver lobe and multi-organ exchanges, and the first liver-kidney swap took place in 2019.
He has developed the leading algorithms and pipelines for several general game classes. The team he leads is the multi-time world champion in AI-vs-AI heads-up no-limit Texas hold'em, the main benchmark and decades-open challenge problem for application-independent algorithms for imperfect-information games. Their AI Libratus became the first and only AI to beat top humans at that game. Then their AI Pluribus became the first and only AI to beat top humans at the multi-player game. That is the first superhuman milestone in any game beyond two-player zero-sum games. He is Founder and CEO of Strategic Machine, Inc., which provides solutions for strategic reasoning in business and gaming applications. He is Founder and CEO of Strategy Robot, Inc., which focuses on defense, intelligence, and other government applications.
Among his honors are the Minsky Medal, Engelmore Award, Computers and Thought Award, inaugural ACM Autonomous Agents Research Award, CMU's Allen Newell Award for Research Excellence, Sloan Fellowship, NSF Career Award, Carnegie Science Center Award for Excellence, Edelman Laureateship, and Goldman Sachs 100 Most Intriguing Entrepreneurs. He is Fellow of the ACM, AAAI, and INFORMS. He holds an honorary doctorate from the University of Zurich.
Host: Bistra Dilkina, USC Center for Artificial Intelligence in Society (CAIS)
Webcast: https://usc.zoom.us/webinar/register/WN_xhADAX7FRt-eiAg2fCI5bQLocation: Online Zoom Webinar
WebCast Link: https://usc.zoom.us/webinar/register/WN_xhADAX7FRt-eiAg2fCI5bQ
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CAIS Seminar: Karen Smilowitz (Northwestern University) - Achieving More Equitable Access to Learning
Wed, Apr 21, 2021 @ 01:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Karen Smilowitz, Northwestern University
Talk Title: Achieving More Equitable Access to Learning
Series: USC Center for Artificial Intelligence in Society (CAIS) Seminar Series
Abstract: Dr. Smilowitz will discuss connections between evolving issues in public education and advances in optimization, computing and geographic information systems, beginning with early work motivated by Supreme Court decisions to desegregate schools. The talk will end with a reflection on current issues facing public school districts, including school busing and return-to-school plans amid the COVID-19 pandemic, and the ways in which operations research can be part of these discussions.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_ApjNTpveTnOIHvwqbduuAw
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Dr. Karen Smilowitz is the James N. and Margie M. Krebs Professor in Industrial Engineering and Management Science at Northwestern University, with a joint appointment in the Operations group at the Kellogg School of Management. Dr. Smilowitz is an expert in modeling and solution approaches for logistics and transportation systems in both commercial and non-profit applications, working with transportation providers, logistics specialists and a range of non-profit organizations. Dr. Smilowitz is the founder of the Northwestern Initiative on Humanitarian and Non-Profit Logistics. She has been instrumental in promoting the use of operations research within the humanitarian and nonproï¬t sectors through the Woodrow Wilson International Center for Scholars, the American Association for the Advancement of Science, and the National Academy of Engineering, as well as various media outlets. Dr. Smilowitz is Editor-in-Chief of Transportation Science. Dr. Smilowitz received the Award for the Advancement of Women in OR/MS from INFORMS and led the winning team in the INFORMS Innovative Applications of Analytics Award.
Host: USC Center for Artificial Intelligence in Society (CAIS)
Webcast: https://usc.zoom.us/webinar/register/WN_ApjNTpveTnOIHvwqbduuAwLocation: Online Zoom Webinar
WebCast Link: https://usc.zoom.us/webinar/register/WN_ApjNTpveTnOIHvwqbduuAw
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CS Colloquium: Alice Xiang (Sony AI) - Algorithmic Fairness and the Law
Fri, Apr 23, 2021 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Alice Xiang, Sony AI
Talk Title: Algorithmic Fairness and the Law
Series: Computer Science Colloquium
Abstract: What does it mean for an algorithm or decision-making process to be fair? When making decisions using data, how do we account for historical biases or systemic inequalities? While the algorithmic fairness literature is comparatively nascent, these are questions the law has long grappled with. This talk will provide an overview of the algorithmic fairness literature and connections with anti-discrimination jurisprudence. In particular, this talk will discuss the tension between the use of protected class attributes to mitigate algorithmic bias and the law's preference for efforts to address discrimination that are blind or neutral to protected class attributes. Divergences like this between legal compatibility and technical feasibility are challenges that will need to be addressed in order to deploy algorithmic fairness methods in practice.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Alice Xiang is a Senior Research Scientist at Sony AI, where she leads research on responsible AI. Alice previously worked as the Head of Fairness, Transparency, and Accountability Research at the Partnership on AI, where she led a team of interdisciplinary researchers and a portfolio of multi-stakeholder research initiatives. She also served as a Visiting Scholar at Tsinghua University's Yau Mathematical Sciences Center. She was recognized as one of the 100 Brilliant Women in AI Ethics, and has been quoted in the Wall Street Journal, MIT Tech Review, Fortune, and others, for her work on algorithmic bias and transparency, criminal justice risk assessment tools, and AI ethics. Alice holds a Juris Doctor from Yale Law School, a Master's in Development Economics from Oxford, a Master's in Statistics from Harvard, and a Bachelor's in Economics from Harvard.
Host: Sirisha Rambhatla (sirishar@usc.edu)
Webcast: https://viterbi.webex.com/viterbi/j.php?MTID=m5f18c96c4db845b65c22fa7c17d9516cLocation: Online Webex
WebCast Link: https://viterbi.webex.com/viterbi/j.php?MTID=m5f18c96c4db845b65c22fa7c17d9516c
Audiences: Everyone Is Invited
Contact: Computer Science Department
