Distinguished Lecture Series

Abstract: As machine learning and AI systems have become both more complex and used pervasively in high-stakes domains, building theoretical foundations to understand and analyze their behavior has become more elusive, yet more pressing than ever before. Analyzing learnability in such systems, with potentially many learners learning potentially complex objects, requires a multi-faceted approach that brings together and extends ideas from many fields, including learning theory, game theory, and algorithmic foundations.

In this talk, I will present work from my group in these directions. I will discuss learning in the presence of other (possibly learning) agents, both in cooperative scenarios, such as prover-verifier interactions for LLM reasoning, and in competitive strategic situations used in security applications. I will also discuss the learnability of another class of complex objects, namely algorithms for solving problems that remain intractable within classic frameworks.

Bio: Maria Florina Balcan is the Cadence Design Systems Professor of Computer Science in the School of Computer Science at Carnegie Mellon University. Her main research interests are machine learning, artificial intelligence, theory of computing, algorithmic game theory, and connections between learning theory and other scientific fields. She is a Simons Investigator, an ACM Fellow, an AAAI Fellow, a Sloan Fellow, a Microsoft Research New Faculty Fellow, and the recipient of the ACM Grace Murray Hopper Award, NSF CAREER award, paper awards in UAI, COLT, and ACM-EC, and several other industry awards. She has given distinguished lectures and invited keynote talks across different research fields (including machine learning, information theory, mathematics, algorithmic game theory, and operations research). She has co-chaired major conferences in the field: the Conference on Learning Theory (COLT) 2014, the International Conference on Machine Learning (ICML) 2016, and Neural Information Processing Systems (NeurIPS) 2020. She was also the general chair for the International Conference on Machine Learning (ICML) 2021, a board member of the International Machine Learning Society, and a member of the scientific advisory board for the Simons Institute for Theory of Computing.

Abstract: My team’s work on AI for Social Impact (AI4SI) has spanned two decades, focusing on optimizing limited resources in critical areas like public health, conservation, and public safety. I will present field results from India, where the deployment of restless and collaborative multi-armed bandit (RMAB) algorithms achieved significant improvements in the world’s two largest mobile maternal health programs. I will also highlight ongoing work on network-based HIV prevention in South Africa, modeled as a branching bandit problem. These projects, along with other initiatives across Africa and Asia, expose a critical “deployment bottleneck” that spans the entire machine learning pipeline. This bottleneck consists of three key hurdles: the observational scarcity gap (data), the policy synthesis gap (learning and modeling), and the human-AI alignment gap (deployment).

This talk investigates how Generative AI can accelerate the AI4SI deployment cycle through the strategic use of LLM Agents and diffusion models. I will demonstrate how LLM Agents address the alignment gap by integrating expert guidance into algorithmic planning, ensuring resource optimization strategies reflect real-world priorities. Furthermore, I will show how diffusion models mitigate the scarcity and synthesis gaps by generating synthetic social networks and facilitating Transfer RL to utilize data across domains. I will conclude by discussing this path toward a more scalable, human-aligned future for AI for Social Impact.

Bio: Milind Tambe is the Gordon McKay Professor of Computer Science at Harvard University; concurrently, he is Principal Scientist and Director for “AI for Social Good” at Google Research. Prof. Tambe and his team have developed innovative AI and multi-agent reasoning systems that have been successfully deployed to deliver real-world impact in public health (e.g., maternal and child health), public safety, and wildlife conservation.

He is the recipient of the AAAI Award for Artificial Intelligence for the Benefit of Humanity, the AAAI Feigenbaum Prize, the IJCAI John McCarthy Award, the AAAI Robert S. Engelmore Memorial Lecture Award, and the AAMAS ACM/SIGAI Autonomous Agents Research Award. He is a fellow of AAAI and ACM. His contributions in Operations Research and public safety have also been recognized with the INFORMS Wagner Prize for excellence in Operations Research practice, Military Operations Research Society Rist Prize, the Columbus Fellowship Foundation Homeland security award, and commendations and certificates of appreciation from the US Coast Guard, the Federal Air Marshals Service, and airport police at the city of Los Angeles.