Session Description: The session will begin with an overview presented by the moderators, followed by a 40 minute keynote lecture. The keynote lecture will be followed by an open discussion led by the moderators.
Charge to Speakers: What are the issues presented by ordinary differential equations in biology that can benefit from a machine learning---and more broadly---a data-driven approach? In addition to your own perspective, is there an emerging view of this question in the field?
Keynote Speaker Bio: Dr. C. Alberto Figueroa received his PhD in Mechanical Engineering from Stanford University in 2006. In 2011, he was appointed Associate Professor in Biomedical Engineering at King’s College London. He moved to the University of Michigan in 2014, where he is currently the Edward B. Diethrich M.D. Research Professor of Biomedical Engineering and Vascular Surgery. Dr. Figueroa’s main expertise is image-based simulation of hemodynamics. His doctoral work focused on developing techniques for fluid-structure interaction and multi-scale modeling for subject-specific cardiovascular simulations. Dr. Figueroa’s current research interests include: – Methods to predict the short-term response (auto-regulation) of the arterial system in response to changes in pressure and flow. – Pathophysiology and mechano-biology of arterial hypertension. – Methods to predict the growth & remodeling of blood vessels in response to changes in their biomechanical environment. – Computational tools to evaluate and predict the performance of abdominal and thoracic endografts. Dr. Figueroa’s lab is currently developing the open-source software for patient-specific blood flow modeling “CRIMSON” (CardiovasculaR Integrated Modeling and SimulatiON). This project is funded by the European Research Council via the “Starting Grant” mechanism, the most competitive in Europe. The goal of this project is to develop software to perform “virtual surgical planning”, a paradigm that will enable one day to test different approaches for a surgery and choose the optimal prior to the procedure. Dr. Figueroa has published extensively in the fields of Biomedical Engineering, Applied Mechanics, Life Sciences, and Vascular and Endovascular Surgery.
Moderator Bios:
Krishna Garikipati obtained his Bachelors degree from the Indian Institute of Technology, Bombay, in 1991, a Masters and PhD from Stanford University in 1992 and 1996, respectively. After a few years of post-doctoral work, he joined the faculty at University of Michigan in 2000, where since 2012, he has been a professor in the Departments of Mechanical Engineering, and of Mathematics. His research draws on applied mathematics and numerical methods to explain phenomena in biophysics and materials physics. A recent interest is in using data-driven methods to enhance our ability to solve computational physics problems. In 2016 he was appointed the Director of the Michigan Institute for Computational Discovery and Engineering (MICDE), a research institute focused on developing new paradigms of computational science that cut across application areas. He has been awarded the DOE Early Career Award, the Presidential Early Career Award for Scientists and Engineers, and a Humboldt Research Fellowship.
Mark Alber earned his Ph.D. in mathematics at the University of Pennsylvania under the direction of J. E. Marsden (UC Berkeley and Caltech). He held several positions at the University of Notre Dame including most recently Vincent J. Duncan Family Chair in Applied Mathematics. He also served as the Director of the Interdisciplinary Center for the Study of Biocomplexity at the University of Notre Dame. He is currently Distinguished Professor in the Department of Mathematics and Director of the Center for Quantitative Modeling in Biology, University of California, Riverside. Dr. Alber was elected a Fellow of the American Association for the Advancement of Science (AAAS) in 2011. He is currently deputy editor of the PLoS Computational Biology and member of editorial boards of Bulletin of Mathematical Biology and Biophysical Journal. His research interests include mathematical and computational multiscale modeling of blood clot formation, epithelial tissue growth, bacterial swarming and plants development and growth.
Topics for Discussion:
- What are the specific instances where machine learning can be used with ordinary differential equations in biology?
- More broadly, what is the role for data-driven methods?
- Are there issues that arise in reconciling the modeler's and experimentalist's approach to identifying parameters, models and mechanisms?