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Speaker: , assistant professor of engineering sciences and applied mathematics, Northwestern University

Title: Sparse-Optimization to Discover Dynamical Systems Models from Data

Abstract: Building models for biological, chemical, and physical systems has traditionally relied on domain-specific intuition about which interactions and features most strongly influence a system.  Alternatively, machine-learning methods are adept at finding novel patterns in large data sets and building predictive models but can be challenging to interpret in terms of or integrate with existing knowledge. Our group balances traditional modeling with data-driven methods and optimization to get the best of both worlds.  Recently developed for and applied to dynamical systems, sparse optimization strategies can select a subset of terms from a library that best describes data, automatically interfering potential model structures from a broad but well-defined class. I will discuss my group's application and development of data-driven methods for model selection to 1) recover chaotic systems models from data with hidden variables,  2) discover models for metabolic and temperature regulation in hibernating mammals, and 3) model selection for differential-algebraic-equations. I'll briefly discuss current preliminary work and roadblocks in developing new methods for model selection of biological metabolic and regulatory networks.

Bio: Niall M. Mangan received the Dual BS degrees in mathematics and physics, with a minor in chemistry, from Clarkson University, Potsdam, NY, USA, in 2008, and the PhD degree in systems biology from Harvard University, Cambridge, MA, USA, in 2013. Dr. Mangan worked as a postdoctoral associate in the Photovoltaics Lab at MIT from 2013-2015 and as an Acting Assistant Professor at the University of Washington, Seattle from 2016-2017. She is currently an Assistant Professor of engineering sciences and applied mathematics with Northwestern University, where she works at the interface of mechanistic modeling, machine learning, and statistical inference. Her group applies these methods to many applications including metabolic and regulatory networks to accelerate engineering.

Applied Mathematics Colloquium