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Edward Ott

Affiliations: 
University of Maryland, College Park, College Park, MD 
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"Edward Ott"
Mean distance: 16.47 (cluster 29)
 
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Parents

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Jerry Shmoys grad student Polytechnic Institute of Brooklyn
 (I think this is true...)

Children

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Jay Y. Vaishnav research assistant 1998-2000 University of Maryland (Physics Tree)
Thomas M. Antonsen grad student (E-Tree)
Mingzhou Ding grad student University of Maryland
Mitrajit Dutta grad student 2000 University of Maryland
Matthew R. Hendrey grad student 2000 University of Maryland
Douglas N. Armstead grad student 2002 University of Maryland
Jong-Won Kim grad student 2002 University of Maryland
Romulus Breban grad student 2003 University of Maryland
Michael Oczkowski grad student 2003 University of Maryland
Aleksey V. Zimin grad student 2003 University of Maryland
Yue-Kin Tsang grad student 2004 University of Maryland
Vasily Dronov grad student 2005 University of Maryland
Jonathan Ozik grad student 2005 University of Maryland
Juan G. Restrepo grad student 2005 University of Maryland
Xing Zheng grad student 2005 University of Maryland
Seung-Jong Baek grad student 2007 University of Maryland
Matthew T. Cornick grad student 2007 University of Maryland
James A. Hart grad student 2009 University of Maryland
Viktor Nagy grad student 2009 University of Maryland
Nicholas A. Mecholsky grad student 2010 University of Maryland
Young-noh Yoon grad student 2011 University of Maryland
Sanjeev K. Chauhan grad student 2012 University of Maryland
Gilad Barlev grad student 2013 University of Maryland
Mark R. Herrera grad student 2013 University of Maryland
Wai S. Lee grad student 2013 University of Maryland
Ming-Jer Lee grad student 2013 University of Maryland
Shane Squires grad student 2014 University of Maryland

Collaborators

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Steven Henry Strogatz collaborator (MathTree)
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Publications

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Wikner A, Harvey J, Girvan M, et al. (2023) Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing. Neural Networks : the Official Journal of the International Neural Network Society. 170: 94-110
Srinivasan K, Coble N, Hamlin J, et al. (2022) Parallel Machine Learning for Forecasting the Dynamics of Complex Networks. Physical Review Letters. 128: 164101
Chandra S, Ott E, Girvan M. (2020) Critical network cascades with re-excitable nodes: Why treelike approximations usually work, when they break down, and how to correct them. Physical Review. E. 101: 062304
Ma S, Xiao B, Drikas Z, et al. (2020) Wave scattering properties of multiple weakly coupled complex systems. Physical Review. E. 101: 022201
Virkar YS, Restrepo JG, Shew WL, et al. (2020) Dynamic regulation of resource transport induces criticality in interdependent networks of excitable units. Physical Review. E. 101: 022303
Ma S, Phang S, Drikas Z, et al. (2020) Efficient Statistical Model for Predicting Electromagnetic Wave Distribution in Coupled Enclosures Physical Review Applied. 14
Banerjee A, Pathak J, Roy R, et al. (2019) Using machine learning to assess short term causal dependence and infer network links. Chaos (Woodbury, N.Y.). 29: 121104
Chandra S, Girvan M, Ott E. (2019) Complexity reduction ansatz for systems of interacting orientable agents: Beyond the Kuramoto model. Chaos (Woodbury, N.Y.). 29: 053107
Zhou M, Ott E, Antonsen TM, et al. (2019) Scattering statistics in nonlinear wave chaotic systems. Chaos (Woodbury, N.Y.). 29: 033113
Ma S, Xiao B, Hong R, et al. (2019) Classification and Prediction of Wave Chaotic Systems with Machine Learning Techniques Acta Physica Polonica A. 136: 757-764
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