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SUNMONTUEWEDTHUFRISAT
Events for the 4th week of January
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CS Colloquium: David Pynadath (USC ICT) - Data-Driven Modeling of Human Social Behavior with Recursive Decision-Theoretic Agents
Tue, Jan 21, 2020 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: David Pynadath, USC / ICT
Talk Title: Data-Driven Modeling of Human Social Behavior with Recursive Decision-Theoretic Agents
Abstract: Social scientists, policy makers, and other analysts have increasingly turned to multiagent social simulation as a generative methodology for representing, analyzing, and simulating human behavior. Typical agent-based social simulation methods are attractive, because they use simple, reactive rules that are directly expressible by the people seeking to use them. In contrast, AI provides algorithms for generating autonomous decisions that can match a human level of complexity, but that same complexity is a currently insurmountable obstacle to their use by AI non-experts.
At ICT, we have developed a social simulation framework, PsychSim, using decision-theoretic agents with a theory of mind (ToM) to form mental models about others and use those models to inform their own decision-making. While PsychSim's recursive Partially Observable Markov Decision Processes (POMDPs) offer a generative and transparent approach to social simulation, they share the disadvantage of similarly complex AI languages in that much effort and, often, much error is incurred when building models in them. Fortunately, the growing availability of data about people, their perceptions, and their behaviors offers a novel opportunity for automated support to both reduce the burden and increase the accuracy of the modeling process.
In this talk, I will present algorithms we have developed and applied to two different scenarios: (1) response of an urban population to a disaster, and (2) perceptions of inequality among different national, ethnic, and religious populations. In particular, we analyze the results of applying different automated methods for identifying dynamic influence diagrams whose output matches the beliefs and behaviors that people exhibit in these two scenarios. Because no single model correctly predicted everyone's perceptions and behaviors, we had our algorithm select additional models to capture atypical cases as well. Even with a very restricted space of candidate graphs, our algorithms found multiple models consistent with many of the people in the data sets. We quantify the ambiguity in the models selected by analyzing these cases, and, because of the graphical representation, we can compare models against each other to characterize potential differences in perceptions and behaviors. The result is an automated process that not only generates models for use within multiagent social simulation, but also quantifies the degree of confidence one can place in those models.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dr. David Pynadath is the Director for Social Simulation Research at USC ICT. He received his Ph.D. from the University of Michigan, Ann Arbor in 1999. He has published papers on multiagent systems, teamwork, social simulation, human-robot interaction, explainable AI, and plan recognition. He is the co-creator and maintainer of PsychSim, the multiagent social simulation framework that was the foundation of the work to be presented. Dr. Pynadath has collaborated with partners in academia and government to apply PsychSim to drive virtual characters in interactive simulations for teaching urban stabilization operations, cross-cultural negotiation, disaster response, and avoiding risky behavior.
Host: Jon May
Location: Ronald Tutor Hall of Engineering (RTH) - 105
Audiences: Everyone Is Invited
Contact: Cherie Carter
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Computer Science General Faculty Meeting
Wed, Jan 22, 2020 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
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CAIS Seminar: Nikos Trichakis (MIT) - Data-driven Methods to Improve Organ Allocation for Transplantation
Wed, Jan 22, 2020 @ 04:15 PM - 05:15 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Nikos Trichakis, Massachusetts Institute of Technology
Talk Title: Data-driven Methods to Improve Organ Allocation for Transplantation
Series: USC Center for Artificial Intelligence in Society (CAIS) Seminar Series
Abstract: Current organ distribution and allocation policies have resulted in persistent disparities in access to donated organs for transplantation across different waitlisted candidates based on their geographic location, sex, and/or disease. We discuss a novel optimization scheme that leverages machine learning and simulation techniques to devise allocation policies that could alleviate these disparities and allow for a more efficient use of donated organs in the United States. We find that our proposed allocation policies could provide substantial waitlist mortality reduction (of the order of 20% for end-stage liver disease patients), while providing a more equitable organ access in comparison with other proposals.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Nikos Trichakis is an Associate Professor of Operations Management at the MIT Sloan School of Management. His research interests include optimization under uncertainty, data-driven optimization and analytics, with application in healthcare, supply chain management, and finance. Trichakis is also interested in the interplay of fairness and efficiency in resource allocation problems and operations, and the inherent tradeoffs that arise in balancing these objectives.
Host: USC Center for Artificial Intelligence in Society (CAIS)
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CS Colloquium: Nanyun Peng (USC / ISI) - From Language Understanding to Creative Generation
Thu, Jan 23, 2020 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Nanyun Peng, USC / ISI
Talk Title: From Language Understanding to Creative Generation
Series: CS Colloquium
Abstract: Recent advances in data-driven approaches have demonstrated appealing results in generating natural languages in applications like machine translation and summarization. However, when the generation tasks are open-ended and the content is under-specified, existing techniques struggle to generate coherent and creative sentences. This happens because the generation models are trained to capture the surface form (i.e. sequences of words), rather than the underlying semantics and discourse structures. Moreover, composing creative pieces such as puns, poems, and stories require deviating from the norm, whereas existing generation approaches seek to mimic the norm and thus are unlikely to lead to truly novel, creative composition. In this talk, I will present several of our recent works related to creative story and pun generation, emphasizing the importance of understanding and control for creative generation.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Nanyun Peng is a Research Assistant Professor of Computer Science at the University of Southern California, and a Research Lead at the Information Sciences Institute. She received a Ph.D. in Computer Science from Johns Hopkins University. Her research focuses on creative language generation, and the robustness and generalizability of natural language understanding, with works being featured in major tech media such as Wired and The Register. Nanyun received a Google Anita Borg Scholarship, a Fred Jelinek Fellowship, and multiple DARPA, IARPA, and NIH grants. She has backgrounds in Linguistics and Economics and held BAs in both.
Host: Xiang Ren
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Distinguished Lecture: Manuela Veloso (JP Morgan) - AI for Intelligent Financial Services: Examples and Discussion
Thu, Jan 23, 2020 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Manuela Veloso, JPMorgan AI Research, on leave: Herbert A. Simon University Professor School of Computer Science, Carnegie Mellon University
Talk Title: AI for Intelligent Financial Services: Examples and Discussion
Series: Computer Science Distinguished Lecture Series
Abstract: After more than 30 years in academia researching in the area of AI, as a student and as a faculty, I joined JPMorgan to create and head an AI research group. In this talk, I will present several concrete examples of the projects we are pursuing in engagement with the lines of business. I will focus on areas related to data, learning from experience, explainability, and ethics. I will conclude with a discussion of my current understanding of the transformational impact that AI can have in the future of financial services.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Manuela M. Veloso is the Head of J.P. Morgan AI Research, which pursues fundamental research in areas of core relevance to financial services, including data mining and cryptography, machine learning, explainability, and human-AI interaction. J.P. Morgan AI Research partners with applied data analytics teams across the firm as well as with leading academic institutions globally.
Professor Veloso is on leave from Carnegie Mellon University as the Herbert A. Simon University Professor in the School of Computer Science, and the past Head of the Machine Learning Department. With her students, she had led research in AI, with a focus on robotics and machine learning, having concretely researched and developed a variety of autonomous robots, including teams of soccer robots, and mobile service robots. Her robot soccer teams have been RoboCup world champions several times, and the CoBot mobile robots have autonomously navigated for more than 1,000km in university buildings.
Professor Veloso is the Past President of AAAI, (the Association for the Advancement of Artificial Intelligence), and the co-founder, Trustee, and Past President of RoboCup. Professor Veloso has been recognized with a multiple honors, including being a Fellow of the ACM, IEEE, AAAS, and AAAI. She is the recipient of several best paper awards, the Einstein Chair of the Chinese Academy of Science, the ACM/SIGART Autonomous Agents Research Award, an NSF Career Award, and the Allen Newell Medal for Excellence in Research.
Professor Veloso earned a Bachelor and Master of Science degrees in Electrical and Computer Engineering from Instituto Superior Tecnico in Lisbon, Portugal, a Master of Arts in Computer Science from Boston University, and Master of Science and PhD in Computer Science from Carnegie Mellon University. See www.cs.cmu.edu/~mmv/Veloso.html for her scientific publications.
Host: Maja Mataric and Heather Culbertson
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
