Energy landscapes and criticality of multivariate time series data
Visiting Speaker
Past Talk
Naoki Masuda
Friday
Dec 3, 2021
Watch video
3:30 pm
177 Huntington Ave
11th floor
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I will present the "energy landscape analysis" of multivariate time series data. In this analysis, one identifies the state of the system at each time point as the position of a "ball" constrained on an energy landscape inferred from data. The energy landscape is constructed using the inverse Ising model (also called Boltzmann machine) and can be regarded as a representation of a correlational network inferred from the data. A ball tends to go downhill on the energy landscape whereas it sometimes goes uphill to transit from one local minimum of the energy to another, possibly corresponding to major dynamical transitions of the system. The application of the method to neuroimaging data is illustrated (while the method is not domain specific). I will also present a further development of the method with which to estimate how close the time series to the criticality and its application to the "brain criticality hypothesis".

About the speaker
About the speaker
Naoki Masuda received his PhD in 2002 from the University of Tokyo. He worked as Lecturer and then Associate Professor at the University of Tokyo, Japan, between 2006 and 2014. Then, he worked as Senior Lecturer and Associate Professor at the University of Bristol, UK, between 2014 and 2019. He moved to Department of Mathematics at University at Buffalo in 2019 as Associate Professor and promoted to full Professor in September 2021. His research interests include network science and mathematical biology. In network science, he is particularly known for his work on temporal networks, models of epidemic processes, and random walks on networks.
Naoki Masuda received his PhD in 2002 from the University of Tokyo. He worked as Lecturer and then Associate Professor at the University of Tokyo, Japan, between 2006 and 2014. Then, he worked as Senior Lecturer and Associate Professor at the University of Bristol, UK, between 2014 and 2019. He moved to Department of Mathematics at University at Buffalo in 2019 as Associate Professor and promoted to full Professor in September 2021. His research interests include network science and mathematical biology. In network science, he is particularly known for his work on temporal networks, models of epidemic processes, and random walks on networks.