MASCOTS 2023
October 16-18, 2023
Stony Brook, NY, USA
16 Oct (Monday) 13:15-14:45
Keynote: Dr. Tamar Eilam, IBM Research
Title: The elephant in the room: Is AI Sustainable?
Abstract: TBA
Bio: Dr. Tamar Eilam is an IBM Fellow and Chief Scientist for Sustainable Computing in the IBM T. J. Watson Research Center, New York. Tamar is leading research strategy aiming at drastically reducing the carbon footprint associated with computing across infrastructure, systems, software, data and AI. Tamar completed a Ph.D. degree in Computer Science in the Technion, Israel in 2000. She joined the IBM T.J. Watson Research Center in New York as a Research Staff Member that same year. She was recognized as an IBM Fellow in 2014.
17 Oct (Tuesday) 13:30-15:00
Keynote: Dr. Siddhartha Sen, Microsoft Research New York City
Title: The case for modeling humans in AI System design
Abstract: I will make the case for modeling and understanding human behavior in the design of AI systems, using examples from request scheduling, video streaming, and gaming. Beyond allowing us to give humans a better quality of experience, it also allows us to help them advance their own skills. This results in a much more productive relationship between humans and AI.
Bio: Siddhartha Sen is a Principal Researcher in the Microsoft Research New York City lab. His research trajectory started with distributed systems and data structures, evolved to incorporate machine learning, and is currently most inspired by humans. His current mission is to use AI to design human-oriented and human-inspired systems that advance human skill and achievement. Siddhartha received his BS/MEng degrees in computer science and mathematics from MIT, then worked for three years as a developer in Microsoft’s Windows Server before returning to academia to complete his PhD from Princeton University. Siddhartha’s work on data structures and human/AI systems has been featured in several textbooks and podcasts.
18 Oct (Wednesday) 10:30-12:00
Keynote: Prof. Vishal Misra, Columbia University
Title: From Self-Similarity to LLMs: A journey with Markov Models
