Department Seminars

Please note this seminar will take place in 228 Malone Hall.

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Computer Science Seminar Series

“Foundations of Evidence-Based AI Policy”

Abstract: Governing AI is a grand challenge for society. Rishi Bommasani’s research provides the foundations for the scientific field of AI policy: How do we understand the societal impact of AI and how do we use our understanding to produce evidence-based AI policy? Bommasani’s research introduces new paradigms for measuring frontier models, deployed systems, and AI companies. Alongside his research, he will cover his work in multiple jurisdictions to demonstrate how AI research can impact public policy.

Speaker Biography: Rishi Bommasani is a senior research scholar at the Stanford Institute for Human-Centered AI researching the societal and economic impact of AI. His research has received several recognitions at machine learning conferences and has been covered by The New York Times, Nature, Science, The Washington Post, and The Wall Street Journal. Bommasani’s research shapes public policy: He is the lead author of the California Report on Frontier AI Policy that led to the first U.S. laws on frontier AI; he is an independent expert chair of the European Union AI Act General-Purpose Code of Practice, which clarifies the first comprehensive worldwide laws on frontier AI; and he’s an author of the International Scientific Report on the Safety of Advanced AI. Bommasani recently completed his PhD in computer science at Stanford University, where he was advised by Percy Liang and Dan Jurafsky and was funded by Stanford’s Gerald J. Lieberman Fellowship and the NSF Graduate Research Fellowship.

Please note this seminar will take place in 228 Malone Hall.

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Computer Science Seminar Series

“The Missing Science of AI Evaluation”

Abstract: AI evaluations inform critical decisions, from the valuations of trillion-dollar companies to policies on regulating AI. Yet evaluation methods have failed to keep pace with deployment, creating an evaluation crisis where performance in the lab fails to predict real-world utility. In this talk, Sayash Kapoor will discuss the evaluation crisis in a high-stakes domain: AI-based science. Across dozens of fields, from medicine to political science, Kapoor finds that flawed evaluation practices have led to overoptimistic claims about AI’s accuracy, affecting hundreds of published papers. To address these evaluation failures, he presents a consensus-based checklist that identifies common pitfalls and consolidates best practices for researchers adopting AI, as well as a benchmark to foster the development of AI agents that can verify scientific reproducibility. AI evaluation failures affect many other applications; beyond science, Kapoor examines how AI agent benchmarks miss many failure modes and presents systems to identify these errors. He examines inference scaling, a recent technique to improve AI capabilities, and shows that claims of improvement fail to hold under realistic conditions. Finally, Kapoor discusses how better AI evaluation can inform policymaking, drawing on his work on evaluating the risks of open foundation models and his engagement with state and federal agencies. Why does the evaluation crisis persist? The AI community has poured enormous resources into building evaluations for models, but not into investigating how models impact the world. To address the crisis, we need to build a systematic science of AI evaluation to bridge the gap between benchmark performance and real-world impact.

Speaker Biography: Sayash Kapoor is a computer science PhD candidate and a Porter Ogden Jacobus Fellow at Princeton University, as well as a senior fellow at Mozilla. He is a co-author of AI Snake Oil, one of Nature’s ten best books of 2024. Kapoor’s newsletter is read by over 65,000 AI enthusiasts, researchers, policymakers, and journalists. His work has been published in leading scientific journals such as Science and Nature Human Behaviour, as well as conferences like the Conference on Neural Information Processing Systems and the International Conference on Machine Learning. Kapoor has written for mainstream outlets including The Wall Street Journal and Wired, and his work has been featured by The New York Times, The Atlantic, The Washington Post, Bloomberg News, and many more. He has been recognized with various awards, including a Best Paper Award at the ACM Conference on Fairness, Accountability, and Transparency; an Impact Recognition Award at the ACM Conference on Computer-Supported Cooperative Work and Social Computing; and inclusion in TIME’s inaugural list of the 100 Most Influential People in AI.

Please note this seminar will take place in 228 Malone Hall at 12:30 p.m., with refreshments available at 12:15 p.m.

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Computer Science Seminar Series

“Making Robust AI Safeguards Run Deep”

Abstract: In 2025, frontier AI developers started warning that their AI systems were beginning to cross risk thresholds for dangerous cyber, chemical, and biological capabilities. This is unfortunate given how closed-weight AI systems are persistently vulnerable to prompt-injection attacks and open-weight systems are persistently vulnerable to malicious fine-tuning. Reinforcement learning from human feedback and refusal training aren’t enough. This presentation will focus on adversarial attacks that target model internals and their uses for making frontier AI safeguards “run deep.” In particular, we will focus on what technical tools can help us make open-weight AI systems safer. Along the way, we will discuss what AI safety can learn from the design of lightbulbs and why you should keep a close eye on Arkansas Attorney General Tim Griffin in 2026.

Speaker Biography: Stephen “Cas” Casper is a final-year PhD student at the Massachusetts Institute of Technology in the Algorithmic Alignment Group, where is he advised by Dylan Hadfield-Menell. Casper leads a research stream for the MATS Program and mentors for ERA and GovAI. He is also a writer for the International AI Safety Report and the Singapore Consensus on Global AI Safety Research Priorities. Casper’s research focuses on AI safeguards and governance, with features in the Conference on Neural Information Processing Systems; the Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence; Nature; the ACM Conference on Fairness, Accountability, and Transparency; the Conference on Empirical Methods in Natural Language Processing; the Institute of Electrical and Electronics Engineers Conference on Secure and Trustworthy Machine Learning; Transactions on Machine Learning Research; and the Iranian Scholars Chapter of the Association for Information Systems Annual Conference on Information Systems—as well as in a number of workshops and over 20 press articles and newsletters. Learn more on his Google Scholar page or personal website.

Please note this seminar will take place in 228 Malone Hall.

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Computer Science Seminar Series

“Context-Aware Systems to Empower People with Disabilities”

Abstract: People with disabilities are marginalized by inaccessible social infrastructure and technology, facing various challenges in all aspects of their life. Conventional assistive technologies commonly provide generic solutions to a certain disability population and do not consider users’ individual and context differences, leading to high abandonment rate. Yuhang Zhao’s research seeks to thoroughly understand the experiences and needs of people with disabilities and create intelligent assistive technologies adaptive to user contexts, including their abilities, environments, and intents, providing effective, unobtrusive support tailored to user needs. In this talk, Zhao will discuss how she leverages state-of-the-art artificial intelligence, augmented reality, and eye-tracking technologies to design and develop context-aware assistive technologies. She will divide user context into external factors (e.g., surrounding environments) and internal factors (e.g., intents, abilities) and present her work on scene-aware, intent-aware, and ability-aware systems, respectively. Specifically, she will discuss: (1) CookAR, a wearable scene-aware AR system that distinguishes and augments the affordance of kitchen tools (e.g., knife blade vs. knife handle) for low-vision users to facilitate safe and efficient interactions; (2) GazePrompt, an eye-tracking-based, intent-aware system that supports low-vision users in reading; and (3) FocusView, a customizable video interface that allows users with ADHD to tailor video presentations to their sensory abilities. Zhao will conclude her talk by highlighting future research directions toward AI-powered context-aware systems for people with disabilities.

Speaker Biography: Yuhang Zhao is an assistant professor in the Department of Computer Sciences at the University of Wisconsin–Madison. Her research interests lie in human-computer interaction (HCI), accessibility, augmented/virtual reality, and AI-powered systems. Zhao leads the madAbility Lab at UW–Madison to design and build intelligent interactive systems to enhance human abilities. She has frequently published at top-tier conferences and journals in the field of HCI and accessibility (e.g., the ACM Conference on Human Factors in Computing Systems, the ACM Symposium on User Interface Software and Technology, the International ACM Special Interest Group on Accessible Computing Conference on Computers and Accessibility) and has received several U.S. and international patents. Her research has been funded by various agencies, including the NSF, the National Institutes of Health, the National Institute of Standards and Technology, and corporate sponsors such as Meta and Apple. Her work has received multiple Best Paper honorable mention awards and recognitions for contribution to diversity and inclusion and has been covered by various media outlets (e.g., TNW, New Scientist). Beyond paper publications, she disseminates her research outcomes via open-source toolkits and guidelines for broader impact. Zhao received her PhD in information science from Cornell University and her BA and MS in computer science from Tsinghua University.

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Computer Science Seminar Series

November 11, 2025

Abstract: Algorithms are increasingly used to aid with high-stakes decision making. Yet, their predictive ability frequently exhibits systematic variation across population subgroups. To assess the trade-off between fairness and accuracy using finite data, we propose a debiased machine learning estimator for the fairness-accuracy frontier introduced by Liang, Lu, Mu, and Okumura (2024). We derive its asymptotic distribution and propose inference methods to test key hypotheses in the fairness literature, such as (i) whether excluding group identity from use in training the algorithm is optimal and (ii) whether there are less discriminatory alternatives to a given algorithm. In addition, we construct an estimator for the distance between a given algorithm and the fairest point on the frontier, and characterize its asymptotic distribution. Using Monte Carlo simulations, we evaluate the finite-sample performance of our inference methods. We apply our framework to reevaluate algorithms used in hospital care management and show that our approach yields alternative algorithms that lie on the fairness-accuracy frontier, offering improvements along both dimensions.

Speaker Biography: Francesca Molinari is the H. T. Warshow and Robert Irving Warshow Professor of Economics and a professor of statistics and data science at Cornell University. She received her PhD from the Department of Economics at Northwestern University after obtaining a BA and master’s in economics at the Università degli Studi di Torino, Italy. Her research interests are in econometrics, both theoretical and applied. Her theoretical work is concerned with the study of identification problems and with proposing new methods for statistical inference in partially identified models. In her applied work, she has focused primarily on the analysis of decision making under risk and uncertainty. She has worked on estimation of risk preferences using market-level data and on the analysis of individuals’ probabilistic expectations using survey data. Molinari is a Fellow of both the Econometric Society and the International Association for Applied Econometrics. She is currently serving as joint managing editor at the Review of Economic Studies and as a member of the board of editors at the Journal of Economic Literature.

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Computer Science Seminar Series

November 6, 2025

Abstract: As the driving force behind the Fifth Industrial Revolution, AI has become a global focal point. This talk will explore the evolution of the five industrial revolutions, explain why large language models represent the pinnacle of cognitive recognition, and analyze how algorithmic advancements are shaping the future of AI products. In addition, we will discuss how AI is reshaping our daily lives and work, as well as the emerging directions for AI-driven entrepreneurship today.

Speaker Biography: Lingyun Gu is currently the founder and CEO of IceKredit, an AI company specializing in the application of natural language processing and deep learning to combat fraud in the financial sector. Focusing on the field of AI, he has authored dozens of papers in international journals and holds 51 invention patents in the U.S. and China. Gu is also a recipient of the Harvard Business Review‘s Ram Charan Management Practice Award. He earned a PhD in computer science from Carnegie Mellon University.

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Computer Science Seminar Series

September 18, 2025

Abstract: Cancer, a leading cause of death, can be effectively treated if detected in its early stages. However, early detection is difficult for both humans and computers. AI can identify details beyond human perception, delineate anatomical structures, and localize abnormalities in medical images, but achieving this level of reliability requires “AI-ready” datasets—big datasets with carefully prepared annotations—and resources that are often limited and expensive in medical imaging. Several disciplines—particularly the success of GPTs—have shown the transformative power of scaling laws for AI advancement, but this concept remains relatively under-explored in medical imaging. This talk will discuss how scaling AI-ready datasets can positively impact new methodologies and applications in medical imaging, with a special focus on enabling earlier cancer detection.

Speaker Biography: Zongwei Zhou is an incoming assistant research professor in the Department of Computer Science at the Johns Hopkins University and a member of the Malone Center for Engineering in Healthcare. His research focuses on medical computer vision, language, and graphics for cancer detection and diagnosis. He is best known for developing UNet++, a widely adopted segmentation architecture cited nearly 15,000 times since its publication in 2019. He currently serves as a principal investigator on a $2.8 million R01 grant from the National Institutes of Health and the National Institute of Biomedical Imaging and Bioengineering. Zhou’s work has earned multiple honors, including a Doctoral Dissertation Award from the American Medical Informatics Association, an Elsevier Medical Image Analysis Best Paper Award, and a Young Scientist Award from the Medical Image Computing and Computer Assisted Intervention Society. He also received the Arizona State University President’s Award for Innovation, the highest honor for graduate students at ASU, and has been recognized among Stanford University’s World’s Top 2% Scientists every year since 2022.