April 10, 2019
4th Floor Rackham Building (915 E. Washington St., Ann Arbor)
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Light breakfast items and coffee
Artificial Intelligence (AI) is a rapidly advancing field. Novel machine learning methods combined with reasoning and search techniques have led us to reach new milestones: from computer vision, machine translation, and Go and Chess world-champion level play using pure self-training strategies, to self-driving cars. These ever-expanding AI capabilities open up new exciting avenues for advances in new domains. I will discuss our AI research for advancing scientific discovery for a sustainable future. In particular, I will talk about our research in a new interdisciplinary field, Computational Sustainability, which has the overarching goal of developing computational models and methods to help manage the balance between environmental, economic, and societal needs for a sustainable future. I will also discuss our work on using AI for accelerating materials discovery. Our research involves a novel integration of AI learning and reasoning techniques.
Bio: Carla Gomes is a Professor of Computer Science and the director of the Institute for Computational Sustainability at Cornell University. Gomes received a Ph.D. in computer science in the area of artificial intelligence from the University of Edinburgh. Her research area is Artificial Intelligence with a focus on large-scale constraint reasoning, optimization, and machine learning. Recently, Gomes has become deeply immersed in research on scientific discovery for a sustainable future and more generally in research in the new field of Computational Sustainability. Computational Sustainability aims to develop computational methods to help solve some of the key challenges concerning environmental, economic, and societal issues in order to help put us on a path towards a sustainable future. Gomes has (co-)authored over 150 publications, including five best paper awards, which have appeared in venues spanning Nature, Science, and a variety of conferences and journals in AI and Computer Science. Her research group has been supported by over $50M in basic research funds. Gomes is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Fellow of the Association for Computing Machinery (ACM), and a Fellow of American Association for the Advancement of Science (AAAS).
Connecting dynamic models with data to yield insights and predictive results requires a variety of parameter estimation, identifiability, and uncertainty quantification techniques. These approaches can help to determine what inferences and predictions are possible from a given model and data set, and help guide control strategies and new data collection. In this talk, we will discuss some of the ideas and methods from identifiability, and examine some of the potential difficulties in estimating the effectiveness of interventions in infectious diseases. We will illustrate how reparameterization and alternative data collection may help resolve various types of unidentifiability and allow for successful intervention predictions.
Bio: Marisa Eisenberg received her Ph.D. and M.S. in Biomedical Engineering from the University of California, Los Angeles in 2009. She then spent three years as a postdoctoral fellow studying mathematical biology at the Mathematical Biosciences Institute at Ohio State University, before joining the faculty at University of Michigan as an assistant professor in the Department of Epidemiology. She is currently an associate professor of Epidemiology, Mathematics and Complex Systems.
Her research is in mathematical biology, and is centered around using and developing parameter estimation and identifiability techniques to connect math models and disease data. Her recent research has been primarily in modeling infectious diseases, particularly examining cholera and waterborne disease. She has also developed models of cancer and endocrine disorders. Some current areas of interest include: parameter identifiability and estimation, infectious diseases, cholera and waterborne diseases, cancer modeling, global health, networks and complexity.
The development of reduced order models for complex applications, offering the promise for rapid and accurate evaluation of the output of complex models under parameterized variation, remains a very active research area. Applications are found in problems which require many evaluations, sampled over a potentially large parameter space, such as in optimization, control, uncertainty quantification and applications where near real-time response is needed.
However, many challenges remain to secure the flexibility, robustness, and efficiency needed for general large-scale applications, in particular for nonlinear and/or time-dependent problems.
After giving a brief general introduction to reduced order models, we discuss the use of Gaussian process regression to allow the development of fast and accurate models for problems, including techniques for greedy regression/active learning and error estimation. We illustrate the performance by examples taken from nonlinear mechanics and fluid dynamics. To enable the modeling of more complex problems, we discuss the development of hybrid element based reduced order model with nonlinear elements, allowing for the rapid and fast evaluation of large and complex structures for design and uncertainty quantification applications. Time permitting we extend the discussion to the multi-fidelity case where different models are combined through coKriging.
This work has been done with in collaboration with M. Guo (EPFL, CH), Z. Zhang (EPFL, CH), and M. Kast (EPFL, CH).
Bio: Prof. Hesthaven received an M.Sc. in computational physics from the Technical University of Denmark (DTU) in August 1991. During the studies, the last 6 months of 1989 was spend at JET, the european fusion laboratory in Culham, UK. Following graduation, he was awarded a 3 year fellowship to begin work towards a Ph.D. at Risø National Laboratory in the Department of Optics and Fluid Dynamics.
During the 3 years of study, the academic year of 1993-1994 was spend in the Division of Applied Mathematics at Brown University and three 3 months during the summer of 1994 in Department of Mathematics and Statistics at University of New Mexico. In August 1995, he recieved a Ph.D. in Numerical Analysis from the Institute of Mathematical Modelling (DTU).
Prof. Hesthaven has been at EPFL since July 2013, having spent most of his previous academic career at Brown University. Since 2017, he has served as the Dean of the School of Basic Sciences overseeing the Institutes of Mathematics, Physics and Chemistry. FULL BIO
An outstanding challenge in the design of large scale, distributed sensing, actuation and control systems (aka cyber-physical systems) is the complexity arising from interactions between different system components as well as interactions with the dynamic environment the system operates in. This talk will present recent progress addressing this challenge through the use of formal methods based algorithmic techniques to rigorously reason about system dynamics and specifications and to automatically synthesize feedback controllers that can guarantee the correct operation of the closed-loop system. I will discuss several potential applications of this approach and illustrate it on problems related to autonomous-driving and coordination of large collections of robots or IoT devices.
Bio: Prof. Ozay received her B.S. degree in Electrical and Electronics Engineering from Bogazici University, Istanbul in 2004, her M.S. degree in Electrical Engineering from the Pennsylvania State University, University Park, PA in 2006 and her Ph.D degree again in Electrical Engineering from Northeastern University, Boston, MA in 2010. Between 2010 and 2013, she was a Control and Dynamical Systems postdoctoral scholar at the Department of Computing and Mathematical Sciences at California Institute of Technology, Pasadena, CA. She is part of the Michigan Controls Group, a core member of Michigan Robotics and also affiliated with the Michigan Institute for Data Science (MIDAS).
Her research interests include dynamical systems, control, optimization and formal methods with applications in cyber-physical systems, system identification, verification and validation, and autonomy. Her papers received several awards including an IEEE Control Systems Society Conference on Decision and Control Best Student Paper Award in 2008 and a best paper award from the Journal of Nonlinear Analysis: Hybrid Systems for the years 2014-2016. Prof. Ozay received a DARPA Young Faculty Award in 2014, an NSF CAREER Award, a NASA Early Career Faculty Award and a DARPA Director’s Fellowship in 2016, an ONR Young Investigator Award and the 1938E Award from the University of Michigan College of Engineering in 2018, and a 2019 Henry Russel Award from the University of Michigan, Rackham Graduate School. She was selected as an Outstanding Reviewer of the IEEE Transactions on Automatic Control in 2011. She is currently an associate editor for Journal of Discrete Event Dynamic Systems, a member of the IEEE, and a member of the IEEE Control Systems Society Technical Committees on Computational Aspects of Control System Design and on Hybrid Systems.
Please RSVP if you are planning on attending lunch.
Students and post-docs will be available to talk to you about their posters from 12:30 – 2:25 p.m.
We gratefully acknowledge KLA’s sponsorship of this poster competition
For every field X there either is now, or soon will be, a computational X—and it’ll be the future of the field. This talk will discuss both the theory and the practice of computation as the key paradigm for future of science. Expect to challenge the speaker with what computational X might be for your favorite value of X.
Bio:Stephen Wolfram is the creator of Mathematica, Wolfram|Alpha and the Wolfram Language; the author of A New Kind of Science; and the founder and CEO of Wolfram Research. Over the course of nearly four decades, he has been a pioneer in the development and application of computational thinking—and has been responsible for many discoveries, inventions and innovations in science, technology and business. FULL BIO
Four years ago, an asteroid with a 20 meter diameter exploded in the atmosphere over Chelyabinsk, causing injury and damage 20 kilometers away but no deaths. We are studying the question of what would occur if such an airburst happened over the ocean. Would the blast wave generate a tsunami that could threaten coastal cities far away?
We show simulations of tsunami propagation from asteroid-generated airbursts under a range of conditions. Our simulations use the open-source software package GeoClaw, which has been successful in modeling earthquake-generated tsunamis using the shallow water equations (SWE). We then present a simplified one dimensional model problem with an explicit solution in closed form to understand some of the unexpected results.
The SWE model however may not be accurate enough for airburst-generated tsunamis, which have shorter length and time scales than earthquake-generated waves. We extend our model problem to the linearized Euler equations to explore the effects of wave dispersion and water compressibility. We end with a discussion of appropriate tools to study the more serious case of an asteroid that impacts the water.
Bio: Prof. Berger’s major areas of research are in computational fluid dynamics, adaptive methods for the numerical solution of pdes in complex geometries, and large-scale parallel computing. SEE MORE