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Michelle Girvan, Department of Physics, University of MarylandTailored Forecasts from Short Time Series Using Meta-Learning and Reservoir ComputingMachine learning (ML) models can be effective for forecasting the dynamics of unknown systems from

Vladimir Rokhlin, Arthur K Watson Professor of Computer Science & Mathematics, Yale UniversityFinding scattering resonances via generalized colleague matricesLocating scattering resonances is a standard task in certain areas of physics and

Deanna Needell, Department of Mathematics, University of California - Los Angeles (UCLA)Fairness and Foundations in Machine LearningIn this talk, we will address areas of recent work centered around the themes of fairness and foundations in machine

Applied Math for the Heart; Take a few PDEs and call me in the morning.The heart is an electro-mechanical system in which, under normal conditions, electrical waves propagate in a coordinated manner to initiate an efficient contraction. In

Nick Trefethen, Professor of Applied Mathematics in Residence, Harvard UniversityThe AAA Algorithm for Rational ApproximationWith the introduction of the AAA algorithm in 2018 (Nakatsukasa-Sete-T., SISC), the computation of rational approximations

Ioannis Kevrekidis, Bloomberg Distinguished Professor, Department of Applied Mathematics and Statistics, Johns Hopkins Whiting School of EngineeringNo Equations, No Variables, No Space and No Time: Data and the Modeling of Complex SystemsI will give

Dane Taylor, Department of Mathematics and Statistics, University of WyomingConsensus processes over networks: Past, present, and futureModels for consensus---that is, the reaching of agreement---have been developed, e.g., to study how group

Cecilia Diniz Behn, Department of Applied Mathematics and Statistics, Colorado School of MinesDynamics of sleep homeostasisThe “homeostatic sleep drive” describes sleep need that increases during wake and decreases during sleep. Using a

Mark Ward, Department of Mathematics, Purdue UniversityThe Data Mine Model for PartnershipsThe Data Mine at Purdue University is a pioneering experiential learning community for undergraduate and graduate students of any background to learn data

Leonid Berlyand, Department of Mathematics, Penn State UniversityEnhancing Accuracy in Deep Learning Using Random Matrix TheoryWe discuss applications of random matrix theory (RMT) to the training of deep neural networks (DNNs). Our focus is on the