Per Executive Order 14434, federal agencies are now using "Super Intelligence" (SI) in place of "Artificial Intelligence" (AI). You may see both terms across NIH content during this transition.
Entropy Estimation in the Extreme Sparse Regime: From Simple Models to Complex Biology
Presented By
Ernesto Suárez
Event Details
Presenter: Ernesto Suárez, Ph.D.
Accurately estimating entropy in high-dimensional, heavily correlated systems remains a fundamental challenge in statistical mechanics and information theory. In the extreme sparse regime, where the system’s state space K vastly exceeds the number of observations (K >> N), traditional frequency-based estimators fail catastrophically. To address this, we introduce a methodology that leverages a mutual information-based metric space and the sum of conditional entropies to infer the unobserved state space. We demonstrate that this approach successfully recovers the entropy of complex, high-dimensional systems even when empirical samples are unique (no repeats), and we showcase its broad utility across applications ranging from predicting the free-energy difference of biomolecules to studying high-dimensional single-cell transcriptomic states. Attendees should have a basic knowledge of statistics.
This will be a hybrid event. Please register at this link.
This session will be recorded, and materials will be shared with attendees a few days after the event.
For additional details and questions, please contact Natasha Pacheco (natasha.pacheco@nih.gov), Advanced Biomedical Computational Science group, Frederick National Laboratory for Cancer Research.
Event Details
Tue Jun 30, 2026
12:00 PM - 1:00 PM
Building 549, Executive Board Room
Series