Artificial Intelligence Materials Institute
Artificial Intelligence Materials Institute

Events Calendar

Upcoming workshops, seminars, and convenings from AI-MI, bringing together researchers and partners to explore advances at the intersection of AI and materials science.

Upcoming Events

There are no upcoming events scheduled. Please check back!

Events Archive

AI-MI Annual Meeting

Members of the NSF AI Materials Institute will convene for the AI-MI Annual Meeting, a two-day, in-person meeting of the NSF AI Materials Institute community.

AI-MI Seminar Series: Analyzing the Nonlocality of Sparse Autoencoder Features

Seminar

Sparse autoencoders are opening new pathways for interpreting large language models. This talk introduces an entropy-based measure, inspired by holographic duality, to quantify how features relate to input tokens—offering a new lens into the information dynamics of LLMs.

AI-MI Seminar Series: Learning, Understanding, and Predicting Quantum Phases in Two-Dimensional Materials

Seminar

The many-electron problem has resisted solution for decades — but neural networks are changing that. In this AI-MI Seminar, Flatiron Institute's Shiwei Zhang presents a physics-inspired computational approach that has already surpassed state-of-the-art methods for two-dimensional electron systems, revealing exotic quantum phases and opening new frontiers in materials science.

AI-MI Seminar Series: From Entropy to Epiplexity – Rethinking Information for Computationally Bounded Intelligence

Seminar

What can a computationally bounded learner actually extract from data — and can that amount exceed what the generating process itself contained? In this AI-MI Seminar, NYU Professor Andrew Gordon Wilson introduces epiplexity, a new formalization of information that targets structured, learnable content and offers a principled foundation for data selection in modern AI systems.

AI-MI Seminar Series: Literature-Informed Agents for Drug Discovery

Seminar

Discover how researchers are using AI to extract chemical and biological data from scientific literature and patents. These large-scale datasets are enabling faster molecule design and advancing AI-driven therapeutic discovery.

Notable Past Events

AI-MI Community Well-Represented at ICML 2026 in Seoul

AI-MI was well-represented at the International Conference on Machine Learning (ICML 2026, Seoul, July 11–19). Eun-Ah Kim, AI-MI Director, co-organized the “AI for Physics” workshop and moderated its opening panel, “AI4Physics and the Current AI Revolution.” John...

AI-MI Researchers Featured at AI4X 2026 in Singapore

AI-MI Director Eun-Ah Kim (Cornell) delivered a plenary talk — “Learning Quantum Matter from Data: Data-Centric AI for Scientific Discovery” — at the AI4X 2026 conference in Singapore. The plenary session was chaired by Yong Tao Tan. AI-MI Senior Investigator Keith...

More Notable Events