Yannis Kevrekidis

Ph.D. Chemical Engineering, University of Minnesota, (’86)

Other Appointments:
Professor, Department of Applied Mathematics and Statistics, Johns Hopkins University Whiting School of Engineering
Professor, Department of Urology, Johns Hopkins University School of Medicine

Research Interests

Algorithms, data, and the computer-assisted modeling of complex dynamical systems

Yannis Kevrekidis, Bloomberg Distinguished Professor in the departments of Chemical and Biomolecular Engineering and Applied Mathematics and Statistics and in the School of Medicine’s Department of Urology, pioneered the approach known as “equation-free computation.”

Kevrekidis’ research interests have always centered around the dynamic behavior of physical, chemical, and biological processes; the types of instabilities they exhibit; the patterns they form; 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 developed what he calls the “equation-free” approach to complex systems modeling, explored its capabilities in several areas, and is now working on linking it with modern data mining/machine learning techniques in what could be called an “equation-free and variable-free” approach.

While Kevrekidis collaborates extensively with experimentalists, the thrust of his group is modeling and algorithm development toward the study of complex dynamics. The work is interdisciplinary, with applications ranging from protein folding to electrochemistry and from reaction engineering to network theory. It also features components of high performance computing, and—in recent years—an increased data science and machine learning component.

Kevrekidis’ work has transformed the way scientists and engineers perform computer-assisted modeling of complex systems – both through new algorithmic techniques, and through targeted applications such as accelerated molecular dynamics, or nonlinear system identification.

Recently, the group used machine learning techniques to intelligently bias molecular dynamics simulations that accelerate folding computations for proteins, elucidating the mechanism that controls saturated vs. unsaturated lipid synthesis in yeast. In collaboration with researchers from Germany, Israel, and Yale University, the group also demonstrated the extraction of useful “quantities of interest” and dynamic equations connecting them – that is, the apparent discovery of physical laws from information-rich data – even when it was not known how the measurements correspond to physically important variables.

Kevrekidis is a member of the National Academy of Arts and Sciences and has been a Packard Fellow, an NSF Presidential Young Investigator, and a Guggenheim Fellow. He holds the Colburn, the Wilhelm, and the Computing in Chemical Engineering awards of the AIChE; the Crawford Prize and the W.T. and Idalia Reid Prize of SIAM; and a Senior Humboldt prize. He has been the Gutzwiller Fellow at the Max Planck Institute for the Physics of Complex Systems in Dresden and a Rothschild Distinguished Visitor at the Newton Institute at Cambridge University, and is currently a senior Hans Fischer Fellow at IAS-TUM in Munich and an Einstein Visiting Fellow at FU/Zuse Institut Berlin. In 2015, he was elected a corresponding member of the Academy of Athens. He also holds a career Teaching Award from the School of Engineering at Princeton.

Kevrekidis earned a bachelor’s degree in chemical engineering at the National Technical University in Athens and a PhD at the University of Minnesota’s Department of Chemical Engineering and Materials Science. He arrived at Johns Hopkins in 2017 after serving as the Pomeroy and Betty Perry Smith Professor in Engineering at Princeton University, where he was professor of Chemical and Biological Engineering, senior faculty in Applied and Computational Mathematics, and associate faculty member in Mathematics.


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