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Picture of Kevin Lin

Kevin Lin

Member of the Graduate Faculty | Professor, Neuroscience - GIDP | Professor, Cognitive Science - GIDP | Professor | Professor, Applied Mathematics - GIDP | Professor, Statistics-GIDP

Research Area

neural networks, neuroscience, monte carlo methods, chaotic systems, data modeling, model reduction, data-driven modeling, reliability, hippocampus, symmetrization

Data Driven Modeling and Analysis of Chaotic and Stochastic Dynamics
Active·2023·$42K·
External
Principal Investigator (PI)
chaotic dynamics, stochastic dynamics, data modeling, analysis, dynamics
RTG: Applied Mathematics and Statistics for Data-Driven Discovery
Active·2020·$800K / $2M·
External
Principal Investigator (PI)
applied mathematics, statistics, data-driven discovery, research methods, machine learning
CDS&E-MSS: Predictive Modeling and Data-Driven Closure of Chaotic and Noisy Dynamics in Discrete Time
2018·$200K·
External
Principal Investigator (PI)
predictive modeling, data-driven closure, chaotic dynamics, noisy dynamics, discrete time
Computational Nonlinear Dynamics: Variance Reduction Methods and Numerical Studies of Large, Chaotic, and Noisy Systems
2014·$220K·
External
Principal Investigator (PI)
nonlinear dynamics, variance reduction, numerical studies, chaotic systems, noisy systems
Computational Analysis of Large Dynamical Systems
2009·$249.3K·
External
Principal Investigator (PI)
computational analysis, large systems, dynamical systems, computational methods, mathematical modeling

Recent

Supporting Information Efficient models of cortical activity via local dynamic equilibria and coarse-grained interactions

2024

Detection of latent brain states from baseline neural activity in the amygdala

2024

neural activity, brain states, amygdala, detection, latent states

Effects of sleeon breakfast behaviors in recently unemployed adults

2024

sleep, unemployment, nutrition, behavior, health

A comparison of probe geometries for neuronal localization

2022

neuronal localization, probe geometries, comparison, neuronal probes, neuroscience

Data-driven model reduction, Wiener projections, and the Koopman-Mori-Zwanzig formalism

2021

data-driven modeling, projection methods, koopman theory, model reduction, formalism

Conjunctive reward–place coding properties of dorsal distal CA1 hippocampus cells

2020

hippocampus, spatial coding, reward processing, neural circuits, learning and memory

Multilevel Monte Carlo for Cortical Circuit Models

2020

monte carlo methods, cortical circuits, multilevel methods, computational neuroscience, propositional logic

Data-driven model reduction, Wiener projections, and the Mori-Zwanzig formalism

2019

model reduction, wiener projections, mori-zwanzig formalism, data-driven modeling, dynamic systems

Data assimilation with stochastic reduced models quantifying model error

2018

data assimilation, stochastic models, model error quantification, data assimilation models, model error analysis

Data-based stochastic model reduction for the Kuramoto–Sivashinsky equation

2017

Hao Zhang
Nirav Merchant
Michael Chertkov
Shankar Venkataramani
David Glickenstein

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Playing the Odds to Your Favor
2012

Calculus II
Mathematical Analysis for Engineers
Linear Algebra for Data Science
Applied Stochastic Processes
Introduction to Ordinary Differential Equations

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10+ Collaborators

$1.5M / $2.7M Funding

50+ Publications