Loading [MathJax]/extensions/Safe.js

Kempner Seminar Series

The Kempner Seminar Series brings leading intelligence researchers to Harvard to discuss their latest research and findings.

About

Join us for our research-level seminar series exploring recent advances in the field of intelligence. Seminars are held on Fridays in-person at Harvard’s Science and Engineering Complex in Allston, MA. The talks in this series are free and open to the public.

To explore upcoming talks, visit our event calendar.

See upcoming talks

Explore the series

Most of our Kempner Seminar Series talks are recorded and available on our Youtube channel.

Recorded talks

Past speakers

Not-Your-Mother’s-Connectionism: LLMs as Cognitive Models

Ellie Pavlick, Assistant Professor of Computer Science at Brown University

Neural Dynamics of Memory, Navigation, and Threat Detection: Implications for AI & Brain-Inspired Learning Systems

Nanthia Suthana, Associate Professor at UCLA

CICERO: Human-Level Performance in the Game of Diplomacy by Combining Language Models with Strategic Reasoning

Noam Brown, Research Scientist at Open AI

Can AI Save Democracy?

Matthew Botvinick, Senior Director of Research, Google DeepMind and Honorary Professor, Gatsby Computational Neuroscience Unit, University College London

The Geometry of Abstraction in Multiple Brain Areas in Multiple Species

Stefano Fusi, Professor of Neuroscience at MIT and Principal Investigator at Columbia’s Zuckerman Institute

Rethinking Brain Mechanisms in the Light of Evolution

Paul Cisek, Professor in the Department of Neuroscience at Université de Montréal 

(Some) Open Frontiers with LLMs

Yoav Goldberg, Professor at Bar Ilan University’s Computer Science Department, and Research Director of the Israeli branch of the Allen Institute for Artificial Intelligence

The Evolution of Agency

Michael Tomasello, Distinguished Professor of Psychology and Neuroscience at Duke University and emeritus Director of the Max Planck Institute for Evolutionary Anthropology

Multimodal AI Agents

Ruslan Salakhutdinov,  UPMC professor of Computer Science in the Machine Learning Department, School of Computer Science at Carnegie Mellon University

High-Capacity Flexible Hippocampal Associative and Episodic Memory Enabled by Prestructured “Spatial” Representations

Ila Fiete, Associate Investigator at the McGovern Institute and Professor of Brain and Cognitive Sciences at MIT 

Towards a Foundation Model of Cognitive Science

Eric Schulz, Assistant Professor at Max Planck Institute for Biological Cybernetics 

Just Asking Questions

Jacob Andreas, Associate Professor at MIT in the Department of Electrical Engineering and Computer Science, and the Computer Science and Artificial Intelligence Laboratory 

State Space Models for Natural and Artificial Intelligence

Scott Linderman, Assistant Professor of Statistics and Institute Scholar in the Wu Tsai Neurosciences Institute at Stanford University

From Spikes to Factors: Understanding Large-Scale Neural Computations

Mark Churchland, Associate Professor, Department of Neuroscience at Columbia University and Co-Director of the Grossman Center for the Statistics of Mind

Reasoning with Language Models (and People)

Noah Goodman, Kempner Institute Visiting Scientist and Associate Professor of Psychology and Computer Science, and Linguistics (by courtesy) at Stanford University

  • Allison Gopnik: Transmission Versus Truth: AI Models as Cultural Technologies and Epistemic Agents.
  • Brian DePasquale: Constructing Spiking Networks as a Foundation for the Theory of Manifolds as Computational Substrate
  • Kim Stachenfeld: Structured Predictive Models for Neuroscience + AI
  • Stefano Ermon: Score Entropy Discrete Diffusion Models
  • Andrea Montanari: Solving overparametrized systems of nonlinear equations
  • Tom Griffiths: Using the Tools of Cognitive Science to Understand the Behavior of Large Language Models
  • Larry Abbott: Modeling the Navigational Circuitry of the Fly
  • Rajesh Rao: Active Predictive Coding: A Sensory-Motor Theory of the Neocortex and a Unifying Framework for AI
  • Noam Brown: CICERO: Human-Level Performance in the Game of Diplomacy by Combining Language Models with Strategic Reasoning
  • Carsen Stringer: Unsupervised Pretraining in Biological Neural Networks
  • Emmanuel Abbe: Logic Reasoning and Generalization on the Unseen
  • Denny Zhou: Teach Language Models to Reason
  • Tom Goldstein: Dataset Security Issues in Generative AI
  • Yann LeCunn: Towards Machines That Can Learn, Reason and Plan
  • Timothy Lillicrap: Model-Based Reinforcement Learning and the Future of Language Models
  • Yejin Choi: Common Sense: The Dark Matter of Language Intelligence
  • Jamie Morgenstern: Shifts in Distributions and Preferences in Learning
  • Uri Hasson: Deep Language Models as a Cognitive Model for NLP in the Human Brain
  • Sebastien Bubeck: First Contact
  • Ludwig Schmidt: A Data-Centric View on Reliable Generalization: From ImageNet to LAION-5B
  • Chelsea Finn: Neural Networks Make Stuff Up. What Should We Do About It?
  • Roger Grosse: Studying Neural Net Generalization through Influence Functions
  • Matthieu Wyart: Data Structure and Curse of Dimensionality in Deep Learning