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Abstract

Some brain disorders are hypothesized to have a dynamical origin; in particular, it has been hypothesized that some symptoms of Parkinson's disease are due to pathologically synchronized neural activity in the basal ganglia region of the brain. We construct artificial neural networks to first demonstrate the capacity to make meaningful predictions of bursting events in patient Parkinsonian data, then use the same framework to generate an adaptive control strategy applied to computational modeling. Our model shows meaningful success in predicting the future brain state in Parkinsonian patients compared to alternative approaches. With this baseline established, we demonstrate that the network can quickly learn precise control and maintain high fidelity to the control objective in a simulated environment, even if the underlying model's parameters vary.

Bio

Jeff Moehlis received a Ph.D. in Physics from UC Berkeley in 2000, and was a Postdoctoral Researcher in Applied and Computational Mathematics at Princeton University from 2000-2003. He joined the Department of Mechanical Engineering at UC Santa Barbara in 2003, and is currently department Chair. He was also recently the Chair of the Program in Dynamical Neuroscience at UC Santa Barbara. He has been a recipient of a Sloan Research Fellowship in Mathematics and a National Science Foundation CAREER Award, and was Program Director of the SIAM Activity Group in Dynamical Systems from 2008-2009.听 He has supervised 11 students to completion of a PhD degree, and 7 additional students who received an MS degree. Jeff's current research includes applications of dynamical systems, control, and computational techniques to neuroscience and system identification. He has published approximately 120 journal/conference proceedings articles on these and other topics including cardiac dynamics, collective behavior, shear flow turbulence, microelectromechanical systems, energy harvesting, and dynamical systems with symmetry.

Abstract

The parameterization of interatomic potentials for molecular dynamics (MD) simulations has long been a highly specialized endeavor requiring strong domain expertise and, in most cases, deep chemical intuition. Progress in computational power and machine learning algorithms offer the opportunity to speed up the development of accurate interatomic potentials for the study of rapidly emerging materials, e.g., two-dimensional (2D) materials.听 In this presentation, we advance a robust approach incorporating multi-objective genetic algorithms and machine-learning-inspired protocols.听 Using monolayer MoSe2 as a testbed, we demonstrate the effectiveness of the proposed approach in parametrizing interatomic potentials with different levels of complexities for structural and mechanical properties in both the equilibrium and non-equilibrium regimes. The methodology achieves better accuracy than existing methods for non-equilibrium properties. Moreover, we reveal for the first-time correlation relationships between material properties of interest.

Bio

Horacio D. Espinosa is the James and Nancy Farley Professor of Manufacturing and Entrepreneurship, Professor of Mechanical Engineering, and the Director of the Theoretical and Applied Mechanics Program at the McCormick School of Engineering, Northwestern University. He received his Ph.D. in Solid Mechanics from Brown University in 1992. Espinosa has made contributions in the areas of deformation and failure of materials, design of micro- and nano-systems, in situ microscopy characterization of nanomaterials, and microfluidics for single cell manipulation and analysis. He has published over 300 technical papers in these topics. Espinosa received several awards including the PRAGER Medal from the Society of Engineering Science, the Society for Experimental Mechanics MURRAY and SIA NEMAT NASSER Medals, and the ASME THURSTON award. He is a member of the National Academy of Engineering (NAE), foreign member of Academia Europaea, the European Academy of Arts and Sciences, the Russian Academy of Engineering, and Fellow of AAAS, ASME, SEM, and AAM. He was the President of the Society of Engineering Science in 2012 and is a member of the IUTAM General Assembly.

Abstract

Optofluidics is the study of the synergies between optics and fluid mechanics and how these synergies they can be used to drive technological innovation.听 In this talk, I will describe several of the ways we have been able to use optofluidics to address practical problems in global health, carbon conversion, and nanomanipulation.听 I will describe our recent efforts on the development of TINY - a solar-thermal powered diagnostic system for the diagnosis of Kaposi鈥檚 Sarcoma 鈥� and its current state of deployment in east Africa.听 Our efforts to show how optimizing the interactions between light, catalyst and flow can be used to enhance the efficiency of photothermal CO2 conversion reactors will also be discussed, along with how the reactor technology was upscaled through the Carbon X-Prize competition.听 I will also discuss some of our efforts on manipulating fluidic systems and nanoparticles using both free space optics and chip-based photonics.听 In addition to covering the basic engineering science advancements that led to the development of these technologies, I will also discuss our strategies for deployment and commercialization.

Bio

David Erickson is the SC Thomas Sze Director and Sibley College Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University.听 He is also a joint Professor within the Division of Nutritional Sciences.听 His research focuses on: mobile and global health technology, medical diagnostics, microfluidics, photonics, and nanotechnology.听 Prior to joining the faculty, he was a postdoctoral scholar at the California Institute of Technology and he received his Ph.D. degree from the University of Toronto. Research in the Erickson lab is or has been primarily funded through grants from the NIH, NSF, ARPA-E, ONR, DOE, DARPA, USAID, USDA, Nutrition International, and Global Alliance for Improved Nutrition (GAIN).听 Prof. Erickson has helped to found numerous start-up companies commercializing: high-throughput pharmaceutical instrumentation, biomedical diagnostics, and energy technologies including Halo Labs (), VitaScan () and Dimensional Energy ().听 Dr. Erickson has received the DARPA-MTO Young Faculty Award, the NSF CAREER Award, the Department of Energy Early Career Award, among others.听 In 2011 he was awarded the Presidential Early Career Award for Scientist and Engineers (PECASE) by President Obama.听 Erickson has been named a fellow of the Optical Society of America, the American Society of Mechanical Engineers, and the Canadian Academy of Engineering.

Abstract

Interfaces separate phases, and they exist everywhere: we can see and perceive them in nature but also in man-made materials, tools, and devices that we use daily. Between two phases separated by an interface there may be transport and chemical reactions among the various components, and a lot of other physical/chemical events that depend on the specific system. In particular, interfacial phenomena dominates the actual performance of electrochemical devices such as batteries and belong to the second category of evolving interfaces. This is the topic of my talk. The tools we use to 鈥渋nterrogate鈥� interfaces are based on theory and computation, and they usually start at the atomistic level involving electrons, nuclei, and molecules, or assembled surfaces and interphases of nanoscopic dimensions.听 However, we expect the knowledge developed using the 鈥渢heoretical microscope鈥� to be useful to understand, predict, and hopefully control the battery macroscopic behavior. Regarding this point, I will show how coarse-grained modeling may help, and how much we gain when integrating our modeling to the experiments. I will refer to a couple of chemistries that are used in current advanced battery technologies. Interfaces Li metal anode/electrolyte, and cathode/electrolyte, for liquid and solid electrolytes.听 I will describe the distinct behavior of interfaces involving electrolytes interacting with metal-oxide cathodes, from others interacting with sulfur-based cathodes. I will end with a critical outlook of the most critical issues and needs both in modeling and experimentation for further development of advanced, longer-life, stable battery components and technologies.

Bio

Dr. Perla B. Balbuena is full Professor of the Department of Chemical Engineering at Texas A&M University since 2004; she currently holds the Mike O鈥機onnor Chair I. Dr. Balbuena also has joint appointments as Professor of Materials Science and Engineering (since 2006) and Professor of Chemistry at Texas A&M (since 2016). Dr. Balbuena obtained her PhD from the University of Texas at Austin, MSc from the University of Pennsylvania, and BSc from Universidad Tecnologica Nacional, Argentina, all in Chemical Engineering.听 From 1984 to 1990 she was Associate Professor at Universidad Nacional del Litoral (INTEC) and Associate Researcher of CONICET (Argentina National Research Council). From 1997 to 2004 she was Assistant and then Associate Professor at the University of South Carolina.听 Her research focuses on first-principles computational materials design, with main areas in interfacial phenomena for batteries, catalysis, and electrocatalysis.听 She has done pioneering work in computational analysis of lithium ion batteries and fuel cell materials, and has also investigated materials for CO2 capture, electrocatalysis, and photocatalysis. Dr. Balbuena is author of 324 scientific articles in peer-reviewed journals and has co-edited five books in her areas of specialization. She was elected AAAS Fellow in 2013, and AIChE Fellow in 2020.

Abstract

This talk will highlight the advances in biomechanics drawing inspiration from diverse organisms ranging from geckos to squirrels and discuss the challenges and opportunities for bioinspired design and robotics.

Bio

Robert J. Full is a Howard Hughes Medical Institute Professor of Integrative Biology at the University of California at Berkeley. Professor Full received his undergraduate, masters, and doctoral degrees at SUNY Buffalo and then held a postdoctoral position at The University of Chicago. He holds a joint appointment in Electrical Engineering and Computer Science, is a member of the Graduate Groups in Biophysics and Science and Mathematics Education in the Graduate School of Education. He is the founder and director of the Center of Interdisciplinary Bio-inspiration in Education and Research (CiBER), currently the Editor-in-Chief of the journal Bioinspiration & Biomimetics, on the science advisory board of the journal Science Robotics, and serves on the National Academies of Sciences, Engineering, & Medicine鈥檚 Board of Life Sciences. He is a National Academy of Sciences Mentor in the Life Sciences, an elected Fellow of the American Association for the Advancement of Science and the American Academy of Arts and Sciences. Professor Full directs the Poly-PEDAL Laboratory, which studies the Performance, Energetics and Dynamics of Animal Locomotion (PEDAL) in many-footed creatures (Poly). He has authored over two hundred research contributions in animal motion science using diverse biological designs as natural experiments to probe for basic themes concerning the relationship between morphology, body size, energetics, dynamics, control, stability, maneuverability, maximum speed, and endurance. An understanding of diverse biological solutions to the problems of locomotion has contributed to the development of a general theory of energetics, neuro-mechanics, and behavior. These principles have resulted in the design of insect inspired search-and-rescue robots, artificial muscles, novel control algorithms, and gecko-inspired, self-cleaning, dry adhesives. Professor Full leads a new HHMI sponsored education program whose goal is to expand the STEM workforce with an early, inspirational and interdisciplinary experience that fosters inclusive excellence showing diverse minds are required to invent the future.