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<i><span style="font-size: 12pt; font-family: Helvetica; color: rgb(139, 1, 2);">UNIVERSITY OF CHICAGO</span></i><o:p></o:p></p>
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<i><span style="font-size: 12pt; font-family: Helvetica; color: rgb(139, 1, 2);">COMPUTER SCIENCE DEPARTMENT</span></i><o:p></o:p></p>
<div style="font-family: Helvetica;"><i style="font-family: Calibri, sans-serif; font-size: 10pt;"><span style="font-size: 12pt; font-family: Helvetica; color: rgb(139, 1, 2);">PRESENTS</span></i></div>
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<b>Yevgeniy Vorobeychik</b></div>
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<b>Washington University </b></div>
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<b>Monday, March 31, 2025 at 2:00pm</b></div>
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<b>Location: JCL 298</b></div>
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<b><span style="font-size: 11pt; color: rgb(33, 33, 33);">Title:</span></b><span style="font-size: 11pt; color: rgb(33, 33, 33);"> <b>Achieving AI Safety in a Contested World</b><o:p></o:p></span></p>
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<b><span style="font-size: 11pt; color: rgb(33, 33, 33);">Abstract:</span></b><span style="font-size: 11pt; color: rgb(33, 33, 33);"> As the increasing capabilities of AI-enabled systems have led to broad deployment across diverse applications ranging from
conversational agents to self-driving cars, safety considerations have come to be central to the current research agenda. However, the very meaning of safety has come to be broad and in some cases contested. For example, there may be responses to conversational
prompts that some may deem neutral, while others offensive, or autonomous driving behaviors that some may view as efficient while others perceive them as dangerously aggressive. A useful way to conceptualize safety considerations is to divide these into two
categories: objective and subjective. The former (for example, running over a pedestrian) is not reasonable contested, while the latter (for example, how aggressively a self-driving car should merge onto a freeway) can admit a range of legitimate perspectives.<o:p></o:p></span></p>
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<span style="font-size: 11pt; color: rgb(33, 33, 33);">In this talk, I will present our recent work tackling both objective and subjective safety considerations. On the former, I will present learning-based approaches for synthesizing provably stable and safe
neural network controllers in known dynamical systems, combining gradient-based methods for both synthesis and verification with ideas from curriculum learning. Further, I will briefly discuss our recent work that facilitates safety specifications that combine
natural language with formal logic, in which we combine LLMs with conformal prediction to obtain provably correct plans. For the latter, I will discuss an axiomatic framework for preference learning that accounts for disagreement in safety preferences, as
well as a novel approach for reinforcement learning with diverse task (e.g., safety) specifications that achieves provable performance guarantees and state-of-the-art performance in zero-shot and few-shot settings.<o:p></o:p></span></p>
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<p class="MsoNormal" style="margin: 0in; font-family: Aptos, sans-serif;"><span style="font-size: 14.666667px;"><b>Bio. <a href="https://urldefense.com/v3/__https://vorobeychik.com/__;!!BpyFHLRN4TMTrA!_h3T87phpwu-HrW5lv_lOXjH_GSOIhfXNdqmCmWjucq9l1k71DWjx50RdvyeNrQrdFwe3i95WACAm-yIR_y-D0bVVQPbZA9WqXvw0Q$" style="color: rgb(70, 120, 134);">Yevgeniy
Vorobeychik</a></b> is a Professor of Computer Science & Engineering at Washington University in Saint Louis. Previously, he was an Assistant Professor of Computer Science at Vanderbilt University. Between 2008 and 2010 he was a post-doctoral research associate
at the University of Pennsylvania Computer and Information Science department. He received Ph.D. (2008) and M.S.E. (2004) degrees in Computer Science and Engineering from the University of Michigan, and a B.S. degree in Computer Engineering from Northwestern
University. His work focuses on game theoretic modeling of security and privacy, adversarial machine learning, algorithmic and behavioral game theory and incentive design, optimization, agent-based modeling, complex systems, network science, and epidemic control.
Dr. Vorobeychik received an NSF CAREER award in 2017, and was invited to give an IJCAI-16 early career spotlight talk. He also received several Best Paper awards, including one of 2017 Best Papers in Health Informatics. He was nominated for the 2008 ACM Doctoral
Dissertation Award and received honorable mention for the 2008 IFAAMAS Distinguished Dissertation Award.<o:p></o:p></span></p>
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<p class="MsoNormal" style="margin: 0in; font-family: Aptos, sans-serif;"><b style="font-size: 14.666667px;">Host: Haifeng Xu</b></p>
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