Prof. Kazuyuki Aihara, U. Tokyo

Data Analysis on Critical Transitions in Complex Systems and its Application to Early Precision Medicine

October 25, 2021 – 09:00 pm (Japan Standard Time)

Prof. Kazuyuki Aihara will review his group’s recent studies on DNB (Dynamical Network Biomarkers) that provide early warning signals of imminent bifurcation from a healthy state to a disease state through a pre-disease state. He will also explain the possible application of DNB for early precision medicine.

Kazuyuki Aihara received a B.E. degree in electrical engineering and Ph.D. degree in electronic engineering from the University of Tokyo (UTokyo), Tokyo, Japan, in 1977 and 1982, respectively. He led the ERATO (Exploratory Research for Advanced Technology) Aihara Complexity Modelling project by JST (Japan Science and Technology Agency) from 2003 to 2008 and the FIRST Innovative Mathematical Modelling project by JSPS (Japan Society for the Promotion of Science) through the FIRST (Funding Program for World-Leading Innovative R&D Science and Technology) program from 2010 to 2014 designed by CSTP (Council for Science and Technology Policy). Currently, he is University Professor and Professor Emeritus of UTokyo,  Deputy Director at the International Research Center for Neurointelligence (IRCN) at UTokyo, and Project Manager of the Moonshot project by the cabinet office of the Japanese government on “Comprehensive Mathematical Understanding of the Complex Control System between Organs and Challenge for Ultra-Early Precision Medicine.”

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Zoom link for the webinar –

Prof. Edward Ott, U. Maryland

November 29, 2021

Prof. Edward Ott will talk about the prediction of chaotic dynamical systems using machine learning.

Professor Ott’s current research is on the basic theory and applications of nonlinear dynamics. Some of his current research projects are in wave chaos, dynamics on large interconnected networks, chaotic dynamics of fluids, and weather prediction. Professor Ott is a fellow of the American Physical Society, the Institute of Electrical and Electronics Engineers, and the Society for Industrial and Applied Mathematics (SIAM). He is the recipient of the APS Julius Edgar Lilienfield Prize for 2014.

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Prof. Steven Brunton, U. Washington

January 31, 2022

Prof. Steven L. Brunton is an Associate Professor of Mechanical Engineering at the University of Washington. He is also an Adjunct Associate Professor of Applied Mathematics and a Data-Science Fellow at the eScience Institute. His research applies data science and machine learning for dynamical systems and control to fluid dynamics, bio-locomotion, optics, energy systems, and manufacturing. He has co-authored three textbooks, received the Army and Air Force Young Investigator Program (YIP) awards and the Army Early Career in Science and Engineering (ECASE), and was awarded the University of Washington College of Engineering teaching award.

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Dr. Louis Pecora, NRL Washington

February 28, 2022

Dr. Lou Pecora is a research scientist at the Naval Research Laboratory, where he has been since 1977. There he heads a group in nonlinear dynamics of solid-state systems.

In 1990, together with Thomas Carroll, he demonstrated for the first time experimentally the control of chaotic systems via synchronization of chaos, a phenomenon that they discovered. In 2020, he and Thomas Carroll among the Clarivate Citation Laureates for research in nonlinear dynamics including synchronization of chaotic systems. His 1990 work with Thomas Carroll on the subject has been cited over 13,000 times.

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Prof. M. Lakshmanan, Bharathidasan U.

April 25, 2022

Prof. Muthusamy Lakshmanan is a theoretical physicist a Ramanna fellow of the Department of Science and Technology at the Centre for Nonlinear Dynamics of Bharathidasan University.

Known for his research on nonlinear dynamics and for the development of Murali-Lakshmanan-Chua (MLC) Circuit, Lakshmanan is an elected fellow of all three major Indian science academies – Indian Academy of Sciences, Indian National Science Academy and National Academy of Sciences, India – as well as of The World Academy of Sciences and the Royal Swedish Academy of Sciences.

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Dr. Viola Priesemann, MPIDS Göttingen

May 30, 2022

Dr. Viola Priesemann will speak on the topic – Spreading dynamics, self-organization, and stability in complex networks: From neural networks to COVID-19

Dr. Priesemann studied physics and neuroscience at the TU Darmstadt and did research at the École normal supérieure in Paris, at Caltech in California, and the Max Planck Institute for Brain Research in Frankfurt. After receiving PhD in 2013, she became a Fellow at the Bernstein Center for Computational Neuroscience Göttingen in 2014. Today she heads a research group on the theory of neural systems at the Max Planck Institute for Dynamics and Self-Organization in Göttingen.

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Prof. Katharina Krischer, TU Munich

March 28, 2022

Prof. Katharina Krischer covers self-organization in physical and physicochemical systems as well as electrocatalysis. She focuses in particular on efficient photoelectrochemical and electrocatalytic energy conversion and universal mechanisms of structure formation processes in nonequilibrium systems.

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Prof. Jürgen Kurths, PIK Germany

Exploring Predictability of Extreme Climate Events via a Complex Network Approach

September 27, 2021 – 04:00 pm (Central European Time)

Earth is a complex system whose dynamics involve innumerous interactions and multiple feedbacks. This makes predictions and risk analysis of very strong, and sometime extreme events such as floods, landslides, heatwaves, earthquakes etc., a challenging task. Here, I will introduce a recently developed approach via complex networks to analyze strong climate events. This leads to an inverse problem: Is there a backbone-like structure underlying the climate system? Towards this, we propose a method to reconstruct and analyze a complex network from observational and reanalysis data. This approach enables us to uncover relations to global and regional circulation patterns in oceans and atmosphere, which leads to substantially better predictions of high-impact phenomena, in particular of the Indian Summer Monsoon, El Nino events, droughts in the central Amazon, extreme rainfall in the Eastern Central Andes, and the pacific decadal oscillation. I argue that network-based approaches can significantly complement numerical modelling for better predictions of extreme weather event and lead to better understanding of meteorological data.

Prof. Jürgen Kurths is Senior Advisor at Research Department for “Complexity Science” at Potsdam Institute for Climate Impact Research (PIK) as well as Professor and Senior Advisor at Humboldt University Berlin.

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