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X-ORIGINAL-URL:https://aimi.cornell.edu
X-WR-CALDESC:Events for The Artificial Intelligence Materials Institute
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DTSTART;TZID=America/New_York:20260409T150000
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UID:1629-1775746800-1775750400@aimi.cornell.edu
SUMMARY:AI-MI Seminar Series: From Entropy to Epiplexity - Rethinking Information for Computationally Bounded Intelligence
DESCRIPTION:Join us for the next AI-MI Seminar Series talk as we explore a bold rethinking of how information is defined\, measured\, and created for learning systems. \nThis talk challenges foundational assumptions in information theory — asking whether computationally bounded observers can extract more from data than classical frameworks like Shannon entropy or Kolmogorov complexity suggest. The speaker introduces epiplexity\, a new formalization of information that captures what real-world learners can actually use\, with practical implications for how we select\, generate\, and transform data to build better AI systems. \nWatch live at youtube.com/@AIMaterialsInstitute \nTopic: Can we learn more from data than existed in the generating process itself? Can new and useful information be constructed from merely applying deterministic transformations to existing data? Can the learnable content in data be evaluated without considering a downstream task? On these questions\, Shannon information and Kolmogorov complexity come up nearly empty-handed\, in part because they assume observers with unlimited computational capacity and fail to target the useful information content. In this talk we identify and exemplify three seeming paradoxes in information theory: (1) information cannot be increased by deterministic transformations; (2) information is independent of the order of data; (3) likelihood modeling is merely distribution matching. To shed light on the tension between these results and modern practice\, and to quantify the value of data\, we introduce epiplexity\, a formalization of information capturing what computationally bounded observers can learn from data. Epiplexity captures the structural content in data while excluding time-bounded entropy\, the random unpredictable content exemplified by pseudorandom number generators and chaotic dynamical systems. With these concepts\, we demonstrate how information can be created with computation\, how it depends on the ordering of the data\, and how likelihood modeling can produce more complex programs than present in the data generating process itself. We also present practical procedures to estimate epiplexity which we show capture differences across data sources\, track with downstream performance\, and highlight dataset interventions that improve out-of-distribution generalization. In contrast to principles of model selection\, epiplexity provides a theoretical foundation for data selection\, guiding how to select\, generate\, or transform data for learning systems. \nSpeaker: Andrew Gordon Wilson is a Professor at the Courant Institute of Mathematical Sciences and Center for Data Science at New York University\, and an Amazon Scholar. He aims to develop a prescriptive foundation for intelligent systems. His work includes generalization theory\, Bayesian inference\, equivariances\, time-series forecasting\, and scientific applications\, particularly in computational biology\, physics\, and materials. He has received the NSF Career Award\, the Heilbronn Distinguished Fellowship\, the Amazon Research Award\, and several best paper\, dissertation\, reviewer\, and area chair awards.
URL:https://aimi.cornell.edu/event/ai-mi-seminar-series-from-entropy-to-epiplexity-rethinking-information-for-computationally-bounded-intelligence/
CATEGORIES:Seminar
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DTSTART;TZID=America/New_York:20260423T150000
DTEND;TZID=America/New_York:20260423T160000
DTSTAMP:20260404T133910
CREATED:20260324T163654Z
LAST-MODIFIED:20260401T204149Z
UID:1631-1776956400-1776960000@aimi.cornell.edu
SUMMARY:AI-MI Seminar Series: Learning\, Understanding\, and Predicting Quantum Phases in Two-Dimensional Materials
DESCRIPTION:Join us for the next AI-MI Seminar Series talk as we explore how neural networks and Monte Carlo methods are cracking open one of the hardest unsolved problems in physical science. \nThis talk takes on the many-electron problem — where the sheer scale of quantum interactions has long outpaced our computational tools. The speaker presents a physics-inspired approach combining neural networks with Monte Carlo simulation to achieve breakthrough accuracy in modeling two-dimensional electron systems\, uncovering exotic quantum phases that previous methods couldn’t reach. \nWatch live at youtube.com/@AIMaterialsInstitute \nTopic: The many-electron problem remains one of the great challenges in physical sciences. An incredible variety of phases arise from the competition between interaction\, quantum zero-point motion\, and the external environment provided by nuclei\, including topology. Understanding and harnessing quantum phases can lead to profound advances\, as has been evident through many of the technological breakthroughs in the past decades. Our tools for doing this are still very limited\, however\, because of the exponentially large dimensions in the many-electron problem\, compounded by the fact that the quantum phases are often the outcome of a delicate balancing act between multiple competing or co-existing tendencies. The use of neural networks in combination with Monte Carlo methods has opened new opportunities. I will discuss our effort to apply neural networks in a physics-inspired manner for accurate and predictive computations for a class of Hamiltonians based on the two-dimensional electron gas\, which are integral to the fast-growing field of two-dimensional materials. In a short period\, these approaches have surpassed state-of-the-art computations in this area\, and have made discoveries of new exotic quantum phases. \nSpeaker: Shiwei Zhang is a Senior Research Scientist/Group Leader at the Center for Computational Quantum Physics (CCQ)\, Flatiron Institute\, Simons Foundation. He is recognized as a world leader in computational quantum physics\, known for broad contributions in computational algorithm innovations and developments\, especially in Monte Carlo methods\, and their applications. Methods he pioneered have been applied in diverse areas including in condensed matter physics\, quantum chemistry\, ultra-cold atoms\, and nuclear physics. He has led a number of international collaborative teams on major research projects in computational quantum physics. Zhang received his Ph.D in Physics from Cornell University. After three years of postdoctoral appointments\, first at Los Alamos National Lab and then at Ohio State under an NSF postdoctoral fellowship\, he joined the faculty of William & Mary\, where he remained for over 20 years\, holding the position of Chancellor Professor of Physics before he moved to Flatiron in 2018. He was recipient of multiple awards including an NSF CAREER award and the Cottrell Scholar Award. He is a Fellow of the American Physical Society.
URL:https://aimi.cornell.edu/event/ai-mi-seminar-series-learning-understanding-and-predicting-quantum-phases-in-two-dimensional-materials/
CATEGORIES:Seminar
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DTSTART;VALUE=DATE:20260519
DTEND;VALUE=DATE:20260521
DTSTAMP:20260404T133910
CREATED:20260105T032736Z
LAST-MODIFIED:20260121T190141Z
UID:1007-1779148800-1779321599@aimi.cornell.edu
SUMMARY:AI-MI Annual Meeting
DESCRIPTION: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. The meeting will feature research updates across the institute\, student and trainee spotlights\, focused discussions on shared challenges and opportunities\, and dedicated time for networking and collaboration. We welcome faculty\, staff and students to connect\, align on priorities\, and accelerate progress toward AI-enabled materials discovery.
URL:https://aimi.cornell.edu/event/ai-mi-annual-meeting/
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