Speakers and Panelists

Naomi Saphra is a research fellow at the Kempner Institute at Harvard University and incoming faculty at Boston University in 2026. Naomi is interested in empirically understanding training in NLP and language models: how models learn to encode linguistic patterns or other structure and how we can encode useful inductive biases into the training process. Recently, she has begun collaborating with natural and social scientists to use interpretability to understand the world around us. She has become particularly interested in fish. Previously, she earned a PhD from the University of Edinburgh on Training Dynamics of Neural Language Models; worked at NYU, Google, MosaicML, and Facebook; and attended Johns Hopkins and Carnegie Mellon University. Outside of research, she plays roller derby under the name Gaussian Retribution, performs standup comedy, and shepherds disabled programmers into the world of code dictation.

Talk Title: And Nothing Between: Using Categorical Differences to Understand and Predict Model Behavior (10:00-10:40AM)

Shiry Ginosar is an Assistant Professor at the Toyota Technological Institute at Chicago (TTIC), where she works at the intersection of computer vision, machine learning, and artificial intelligence. She earned her B.Sc. from Carnegie Mellon University and her Ph.D. in Computer Science from the University of California, Berkeley. Following her Ph.D., she was a Computing Innovation postdoctoral fellow at UC Berkeley's EECS department and the Simons Institute for the Theory of Computing. Shiry then spent a couple of years at Google DeepMind as a Visiting Faculty Researcher.

Shiry's research explores grounded visual understanding, with particular contributions to social behavior prediction, visual data mining, and video synthesis. Her work is driven by a central question: how can we understand human and artificial intelligence one pixel at a time? While rooted in machine perception, her approach is deeply interdisciplinary, drawing from Psychology, Neuroscience, and the arts.

Shiry's work has received broad recognition beyond academia, with coverage in The New Yorker, The Wall Street Journal, and The Washington Post. It has been featured on PBS NOVA, exhibited at the Israeli Design Museum, and included in the permanent collection of the Deutsches Museum.

Talk Title: What Do Vision and Vision-Language Models Really Know About the World? (10:40-11:20AM)

Jacob Andreas is an associate professor at MIT in the Department of Electrical Engineering and Computer Science as well as the Computer Science and Artificial Intelligence Laboratory. His research aims to understand the computational foundations of language learning, and to build intelligent systems that can learn from human guidance. Jacob earned his Ph.D. from UC Berkeley, his M.Phil. from Cambridge (where he studied as a Churchill scholar) and his B.S. from Columbia. He has received a Sloan fellowship, an NSF CAREER award, MIT's Junior Bose and Kolokotrones teaching awards, and paper awards at ACL, ICML and NAACL.

Talk Title: Language Models as World Models? (11:20AM-12:00PM) / Panelist (2:20-3:20PM)

Shirley Ho is a senior research scientist at CCA and she joined the Foundation in 2018 to lead the Cosmology X Data Science group. Her research interests range from cosmology to developing new machine learning methods for scientific data that leverage shared concepts across scientific domains. Ho has extensive expertise in astrophysical theory, observation, and data science. She focuses on novel statistical and machine learning tools to address cosmic mysteries like the origins and fate of the universe. Ho analyzes data from surveys including ACT, Euclid, LSST, Simons Observatory, SDSS, and Roman Space Telescope to understand our universe’s evolution. She earned her Ph.D. in Astrophysical Sciences from Princeton in 2008 and B.S. degrees in Computer Science and Physics from UC Berkeley in 2004. Ho was previously a Chamberlain and Seaborg Fellow at Lawrence Berkeley National Lab. She joined Carnegie Mellon as an Assistant Professor in 2011, becoming Cooper Siegel Career Development Chair Professor and tenured Associate Professor. In 2016 she moved to Lawrence Berkeley Lab as a Senior Scientist.

Since 2011, Ho has mentored over 50 postdocs, 10 Ph.D. students, and 20 undergraduates in astrophysics, computer science, and statistics. Her awards include the Macronix Prize, Carnegie Science Award, Blavatnik National Finalist, and the EPS Giuseppe and Vanna Cocconi Prize in Cosmology.

Talk Title: Polymathic AI: Building Scientific Foundation Models  (1:00-1:40PM)

Sendhil Mullainathan is the Peter de Florez Professor at MIT, splitting his time between the Economics and the Electrical Engineering and Computer Science departments. His current research is at the intersection of algorithms and people, with a focus on building algorithmic tools that change how we do science, understand people and tackle hard problems in society. He enjoys writing, having recently co-authored Scarcity: Why Having too Little Means so Much as well as having written regularly for the New York Times.  

He is active in application of research insights. He helped co-found a non-profit to apply behavioral science (ideas42), co-founded a center to promote the use of randomized control trials in development (the Abdul Latif Jameel Poverty Action Lab), and most recently co-founded Dandelion Health, a company that provides the healthcare data needed to build breakthrough medical AI.   He also serves on the board of the MacArthur Foundation, and has worked in government in various roles. 

He is a recipient of the MacArthur “genius” Award, is a winner of the Infosys Prize, has been designated a “Young Global Leader” by the World Economic Forum, labeled a “Top 100 Thinker” by Foreign Policy Magazine, and named to the “Smart List: 50 people who will change the world” by Wired Magazine (UK). 

Talk Title: Testing for Understanding Requires First Defining It (1:40-2:20PM)

Jon Kleinberg is a professor at Cornell University. His research focuses on algorithms and networks, the roles they play in large-scale social and information systems, and their broader societal implications. His work has been supported by an NSF Career Award, an ONR Young Investigator Award, a MacArthur Foundation Fellowship, a Packard Foundation Fellowship, a Simons Investigator Award, a Sloan Foundation Fellowship, a Vannevar Bush Faculty Fellowship, and grants from Facebook, Google, Yahoo, the MacArthur and Simons Foundations, and the AFOSR, ARO, and NSF. He is a member of the National Academy of Sciences, the National Academy of Engineering, the American Academy of Arts and Sciences, and the American Philosophical Society.

Panelist (2:20-3:20PM)

Mengye Ren is an assistant professor of computer science and data science at New York University (NYU). He runs the Agentic Learning AI Lab. Before joining NYU, he was a visiting faculty researcher at Google Brain Toronto working with Prof. Geoffrey Hinton. From 2017 to 2021, he was a senior research scientist at Uber Advanced Technologies Group (ATG) and Waabi, working on self-driving vehicles. He received Ph.D. in Computer Science from the University of Toronto, advised by Prof. Richard Zemel and Prof. Raquel Urtasun. His research focuses on making machine learning more natural and human-like, in order for AIs to continually learn, adapt, and reason in naturalistic environments.

Panelist (2:20-3:20PM)

Alane Suhr is an assistant professor in the Division of Computer Science, EECS. Alane's research focuses on building systems that use and learn language to interact with people in collaborative, situated interactions. This work spans natural language processing, machine learning, and computer vision. Alane received a PhD in Computer Science from Cornell University, and a BS in Computer Science and Engineering with a minor in Linguistics from Ohio State University.

Panelist (2:20-3:20PM)