1/28: Bianca Dumitrascu (Columbia)

Interpretable Representation Learning for Wound Healing Dynamics

2/21: Shirley Ho (Flatiron Institute)

Building Pan-Scientific Foundation Models

3/4: Tina Eliassi-Rad (Northeastern)

Stability in Complex Systems: Formal, Social, and Epistemic

9/23: Bin Yu (Berkeley)

Veridical Data Science and PCS Uncertainty Quantification

12/4: Chris Wiggins (Columbia)

Machine Learning at the New York Times

1/25: Tamara Broderick (MIT)

An Automatic Finite-Sample Robustness Check: Can Dropping a

Little Data Change Conclusions?

3/20: Lerrel Pinto (NYU)

On Building General-Purpose Home Robotsns?

4/10: Michael Kearns (NYU)

Poison and Cure: Non-Convex Optimization Techniques for Private Synthetic Data and Reconstruction Attacks

9/20: Daniel Lee (Cornell Tech)
Perceptrons Revisited

10/18: Richard Zemel (Columbia)
Quantile Risk Control: A Framework for Responsible Deployment of AI Models

11/13: Tom Griffiths (Princeton)
Bayes in the Age of Intelligent Machines

2/23: He He (NYU)
Evaluating and Understanding the In-context Learning Ability of Language Models 

3/9 Christos Papadimitriou (Columbia),
Towards a Less Artificial Intelligence

4/24 Léon Bottou (FAIR)

Pseudo-Euclidean Attract-Repel Embeddings for Undirected Graphs

9/15: Kyunghyun Cho (NYU and Prescient Design),
Generative Multitask Learning Mitigates Target-causing Confounding

10/6: Emma Pierson (Cornell Tech),
Using Machine Learning to Increase Equity in Healthcare and Public Health

11/15: Samory Kpotufe (Columbia),
Adaptivity in Domain Adaptation and Friends

12/15: Lawrence Saul (Flatiron),
A Geometrical Connection Between Sparse and Low-rank Matrices and Its Uses for Machine Learning