Department Seminars

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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.