Yannis Kevrekidis studied Chemical Engineering at the National Technical University in Athens. He then followed the steps of many alumni of that department to the University of Minnesota, where he studied under Rutherford Aris and Lanny Schmidt (also Dick McGehee and Don Aronson in Mathematics) on computational studies of dynamical systems, which still remains the main theme of his research. He was a Director's Fellow at Los Alamos in 1985-86. He has been at Princeton since 1986, where he teaches Chemical Engineering and also Applied and Computational Mathematics. His research interests have centered around the dynamics of physical and chemical processes, types of instabilities, pattern formation, and their computational study. More recently he has developed an interest in multiscale computations and the modeling of complex systems.
Along with several students and collaborators he has developed the Equation-Free approach to complex systems modeling, explored its capabilities in several areas, and is now working on linking it with moder data mining/machine learning techniques in what could be called an "Equation-Free and Variable Free" approach.
He has been a Packard Fellow, an NSF PYI and the Ulam Scholar at LANL. He holds the Colburn and Wilhelm Awards of the AIChE, the Crawford prize of SIAM/DS and a Humboldt prize. He has been the Gutzwiller Fellow at the Max Planck Institute for the Physics of Complex Systems in Dresden, is currently a senior Hans Fischer Fellow at IAS-TUM in Munich, and next year he will also be an Einstein Visiting Fellow at FU/Zuse Institut Berlin.
Selected Awards
2016-2018, Einstein Foundation and Zuse Institute, Distinguished Visiting Fellowship
2016, Isaac Newton Institute, Cambridge, Rothschild Visiting Distinguished Fellowship, 2016
2015-2017, Institute for Advanced Study, Technical University of Munich, and EEC: Hans Fischer Senior Fellowship
2013, Microsoft Visitor, Newton Institute, Cambridge University
2010-2011, Martin Gutzwiller Fellowship (Max Planck Institute for Complex Systems, Dresden)
2010, AIChE Richard H. Wilhelm Award
2008, Computing in Chemical Engineering Award, Computing and Systems Technology (CAST) Division of the American Institute of Chemical Engineers (AIChE)
2005, Guggenheim Fellowship
2005, Moore Distinguished Visitor, Caltech
2003, J.D. Crawford Prize, Society for Industrial and Applied Mathematics
1998, Humboldt Research Award, Alexander von Humboldt Foundation
1997, Bodossaki Academic Award in Applied Science
1994, Allan P. Colburn Award, American Institute of Chemical Engineers
1989, Presidential Young Investigator, National Science Foundation
1988, Packard Fellowship, David and Lucile Packard Foundation
Research Interests
I am both a computational engineer and an applied/computational mathematician: a modeler. I use the computer to do experiments, to motivate experiments, to design experiments; to detect places where new mathematics could be done, to implement recent mathematics; to explore complex systems by making things happen in simulations that do not happen in nature, maybe do not happen in nature yet.
I was trained as a chemical engineer in Greece, came to the US for my PhD in Minnesota at the time that nonlinear dynamics were blossoming; was a postdoc at the Center for Nonlinear Studies at LANL; and have been in Princeton since 1986 (in Engineering, in Applied and Computational Mathematics, and as associate faculty in Mathematics).
Most of my research career up to 2000 was in dynamics, instabilities and pattern formation; mainly, in developing algorithms that went beyond simple simulation, to compute and quantify instabilities and bifurcations. There were also beautiful interactions with experimentalists (like the development of micro-designed and micro-addressable catalysts with G. Ertl at the Fritz Haber Institute in Berlin).
In the last 15 years, with my students and many international collaborators, we have pioneered a radically new approach to complex systems modeling and computation (the equation-free approach). The novelty and broad scope of this approach has led to extensive research in theory and many applications across disciplines ranging from engineering to physical chemistry, and from evolutionary biology to social modeling and mathematics itself. In more recent years this line of work has blossomed by forging new links (via harmonic analysis) to modern machine learning tools and techniques. The vision emerging from these developments meshes very well conceptually and functionally with our vision and research activities in at the IAS-TUM (in Mathematics and Physics as well as in Engineering).
Building on our shared vision, this Hans Fischer Fellowship will catalyze the integration of our complementary strengths towards the ambitious goal of forging novel approaches to mathematical modeling (linked with data mining) across the sciences and engineering.
Selected Publications
Nadler, B.; Lafon, S.; Coifman, R.R.; Kevrekidis I.G.: Diffusion maps, spectral clustering and reaction coordinates of dynamical systems. Applied and Computational Harmonic Analysis 21, 2006, 113-127.
Kevrekidis, Ioannis G.; Gear, C. William; Hummer, Gerhard: Equation-free: The computer-aided analysis of complex multiscale systems. AIChE Journal 50 (7), 2004, 1346-1355.
Hummer, Gerhard; Kevrekidis, Ioannis G.: Coarse molecular dynamics of a peptide fragment: Free energy, kinetics, and long-time dynamics computations. The Journal of Chemical Physics 118 (23), 2003, 10762.
Gear, C. W.; Kevrekidis, Ioannis G.: Projective Methods for Stiff Differential Equations: Problems with Gaps in Their Eigenvalue Spectrum. SIAM J. Sci. Comput. 24 (4), 2003, 1091-1106.
Gear, C.W.; Kevrekidis, Ioannis G.; Theodoropoulos, Constantinos: ‘Coarse’ integration/bifurcation analysis via microscopic simulators: micro-Galerkin methods. Computers & Chemical Engineering 26 (7-8), 2002, 941-963.
Theodoropoulos, C.; Qian, Y.H.; Kevrekidis, I.G.: “Coarse” stability and bifurcation analysis using time-steppers: A reaction-diffusion example. Proceedings of the National Academy of Sciences 97, 2000, 9840-9843.
Shvartsman, Stanislav Y.; Kevrekidis, Ioannis G.: Nonlinear model reduction for control of distributed systems: A computer-assisted study. AIChE Journal 44 (7), 1998, 1579-1595.
Deane, A. E.; Kevrekidis, I. G.; Karniadakis, G. E.; Orszag, S. A.: Low-dimensional models for complex geometry flows: Application to grooved channels and circular cylinders. Physics of Fluids A: Fluid Dynamics 3 (10), 1991, 2337.
Jolly, M.S.; Kevrekidis, I.G.; Titi, E.S.: Approximate inertial manifolds for the Kuramoto-Sivashinsky equation: Analysis and computations. Physica D: Nonlinear Phenomena 44 (1-2), 1990, 38-60.
Kevrekidis, Ioannis G.; Nicolaenko, Basil; Scovel, James C.: Back in the Saddle Again: A Computer Assisted Study of the Kuramoto–Sivashinsky Equation. SIAM J. Appl. Math. 50 (3), 1990, 760-790.
Foias, C.; Jolly, M.S.; Kevrekidis, I.G.; Sell, G.R.; Titi, E.S.: On the computation of inertial manifolds. Physics Letters A 131 (7-8), 1988, 433-436.
Kemeth, Felix P.; Haugland, Sindre W.; Dietrich, Felix; Bertalan, Tom; Hohlein, Kevin; Li, Qianxiao; Bollt, Erik M.; Talmon, Ronen; Krischer, Katharina; Kevrekidis, Ioannis G.: An Emergent Space for Distributed Data With Hidden Internal Order Through Manifold Learning. IEEE Access 6, 2018, 77402-77413 mehr…BibTeX
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Mitsos, Alexander; Najman, Jaromił; Kevrekidis, Ioannis G.: Correction to: Optimal deterministic algorithm generation. Journal of Global Optimization 73 (2), 2018, 465-465 mehr…BibTeX
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2017
Chiavazzo, Eliodoro; Covino, Roberto; Coifman, Ronald R.; Gear, C. William; Georgiou, Anastasia S.; Hummer, Gerhard; Kevrekidis, Ioannis G.: Intrinsic map dynamics exploration for uncharted effective free-energy landscapes. Proceedings of the National Academy of Sciences 114 (28), 2017, E5494-E5503 mehr…BibTeX
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Junge, Oliver; Kevrekidis, Ioannis G.: On the sighting of unicorns: A variational approach to computing invariant sets in dynamical systems. Chaos: An Interdisciplinary Journal of Nonlinear Science 27 (6), 2017, 063102 mehr…BibTeX
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Yair, Or; Talmon, Ronen; Coifman, Ronald R.; Kevrekidis, Ioannis G.: Reconstruction of normal forms by learning informed observation geometries from data. Proceedings of the National Academy of Sciences 114 (38), 2017, E7865-E7874 mehr…BibTeX
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2016
Ben-Tal, A.; Kevrekidis, I. G.: Coarse-Graining and Simplification of the Dynamics Seen in Bursting Neurons. SIAM Journal on Applied Dynamical Systems 15 (2), 2016, 1193-1226 mehr…BibTeX
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Choi, M.; Bertalan, T.; Laing, C.R.; Kevrekidis, I.G.: Dimension reduction in heterogeneous neural networks: Generalized Polynomial Chaos (gPC) and ANalysis-Of-VAriance (ANOVA). The European Physical Journal Special Topics 225 (6-7), 2016, 1165-1180 mehr…BibTeX
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Holiday, A.; Kevrekidis, I.G.: Equation-free analysis of a dynamically evolving multigraph. The European Physical Journal Special Topics 225 (6-7), 2016, 1281-1292 mehr…BibTeX
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Kattis, Assimakis A; Holiday, Alexander; Stoica, Ana-Andreea; Kevrekidis, Ioannis G: Modeling epidemics on adaptively evolving networks: A data-mining perspective. Virulence 7 (2), 2016, 153-162 mehr…BibTeX
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Kemeth, Felix P.; Haugland, Sindre W.; Schmidt, Lennart; Kevrekidis, Ioannis G.; Krischer, Katharina: A classification scheme for chimera states. Chaos: An Interdisciplinary Journal of Nonlinear Science 26 (9), 2016, 094815 mehr…BibTeX
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Rajendran, Karthikeyan; Tsoumanis, Andreas C.; Siettos, Constantinos I.; Laing, Carlo R.; Kevrekidis, Ioannis G.: MODELING HETEROGENEITY IN NETWORKS USING POLYNOMIAL CHAOS. International Journal for Multiscale Computational Engineering 14 (3), 2016, 291-302 mehr…BibTeX
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