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David Darmon

David Darmon, Ph.D.

Assistant Professor

Howard Hall 241
Office Hours
Mondays 1:30-2:30pm
Tuesdays 3:00-4:00pm
Thursdays 10:00-11:00am and 1:30-2:30pm
(in-person or via Zoom)
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David Darmon, Ph.D.


Ph.D. in Applied Mathematics, Statistics, and Scientific Computation, University of Maryland, College Park

B.S. in Mathematics, Ursinus College

Mathematical Interests

My research focuses on understanding how dynamical systems communicate information from their past through their present to their future. I draw on tools from machine learning, statistics, dynamical systems, network science, and information theory. The research is both theoretical, deriving relevant measures of communication and methods for their estimation from data, and computational, implementing those methods via efficient algorithms. Previous research projects include predicting user behavior on Twitter, characterizing the time-varying exchange of information in the brain, quantifying the change in market dynamics before and after the introduction of automated trading, and modeling the information dynamics of nanoscale electronics.


Selected Publications

David Darmon. Discrete Information Dynamics with Confidence via the Computational Mechanics Bootstrap: Confidence Sets and Significance Tests for Information-Dynamic Measures. Entropy, 22(7), 782 (2020).

Martin Hilbert, David Darmon. Large-Scale Communication is More Complex and Unpredictable with Automated Bots. Journal of Communication, (2020).

Martin Hilbert, David Darmon. How Complexity and Uncertainty Grew with Algorithmic Trading. Entropy, 22(5) (2020).

David Darmon, Christopher J. Cellucci, Paul E. Rapp. Information Dynamics with Confidence: Using Reservoir Computing to Construct Confidence Intervals for Information-dynamic Measures. Chaos: An Interdisciplinary Journal of Nonlinear Science 29.8 (2019).