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SUMMARY:MICDE Seminar: Ramanathan Vishnampet\, Senior Research Engineer\, ExxonMobil Upstream Integrated Solutions
DESCRIPTION:Bio: Ramanathan Vishnampet is a Computational Data Scientist at the Global Business Lines Analytics & Optimization group at ExxonMobil Upstream Integrated Solutions. He graduated with a Ph.D. in Theoretical and Applied Mechanics from the University of Illinois at Urbana-Champaign\, where his dissertation focused on an exact and consistent adjoint method for high-fidelity discretization of the compressible flow equations. Ramanathan started as a Senior Research Engineer at ExxonMobil in 2015\, where he worked in the Process Stratigraphy team\, an integrated team including Computational Scientists\, Geoscientists\, Seismic Interpreters\, and Stratigraphers. He helped develop a physics-based stratigraphic model for studying deepwater stratigraphy and showed the emergence of chaotic dynamics and self-organization that limit the ability of traditional model inversion techniques to be applied to the forward model. In his current team\, Ramanathan is working on a scheduling problem for ExxonMobil’s Unconventionals asset base using heuristics and discrete optimization. He is also leading his section’s efforts in adopting lean and agile software development practices\, cloud-based deployment using a service architecture\, and DevOps processes. Ramanathan’s hobbies include cooking\, traveling\, and spending time with his daughter. \nPrediction under chaos using a depth-averaged model of turbidity currents\nIn this talk\, I will demonstrate a forward stratigraphic model based on depth-averaged governing equations for the flow of submarine turbidity currents over an erodible bed. This model is being used with some success by the Process Stratigraphy team at ExxonMobil to generate stratigraphic models for deepwater environments of deposition. The mathematical model consists of a system of nonlinear hyperbolic PDEs\, with an additional so-called Exner equation for modeling the flow-bed sediment exchange and their bedload transport. The Exner equation plays a key role since a (slow time scale) change in the gradient of the bed influences the (fast time scale) momentum of the flow. The transport equations\, along with closure models for sediment transport\, TKE balance\, and water entrainment\, are solved using a first-order finite-volume method with a HLLC approximate Riemann solver and integrated using an explicit Euler scheme. The model shows the emergence of self-organized patterns in the deposits\, including the creation of bedforms\, channel formation\, and avulsions\, consistent with observations of modern systems and lab experiments. These occur even with uniform boundary conditions and symmetric initial conditions. The initial disturbances that trigger these mechanisms are ostensibly sourced by floating-point roundoff errors. An ensemble of simulations with slightly different initial conditions are used to analyze statistics on shapes of geomorphic elements and grain size distributions. The objective is to assess whether and under what conditions such a numerical model can be predictive and quantify the uncertainty in the results arising due to the irreducible chaos in the dynamical system. \nDr. Vishnampet is being hosted by Prof. Capecelatro (ME). If you would like to meet with him during his visit\, please send an email to micde-events@umich.edu.  
URL:https://micde.umich.edu/event/fall2019-vishnampet-exxonmobil/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
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DTSTART;TZID=America/Detroit:20190925T150000
DTEND;TZID=America/Detroit:20190925T160000
DTSTAMP:20260608T094424
CREATED:20230905T171404Z
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SUMMARY:MICDE Seminar: H. Metin Aktulga\, Assistant Professor\, Computer Science and Engineering\, Michigan State University
DESCRIPTION:Bio: H. Metin Aktulga received his B.S. degree from Bilkent University in 2004\, M.S. and Ph.D. degrees from Purdue University in 2009 and 2010\, respectively; all in Computer Science. Before joining the Michigan State University (MSU) in 2014\, he was a postdoctoral researcher in the Computational Research Division at the Lawrence Berkeley Lab. He directs the Scalable Parallel Technologies and Algorithms (SParTA) Lab at MSU. Research in the SParTA Lab focuses on HPC and applications of HPC\, specifically on the design and development of algorithms\, numerical methods and software systems that can harness the full potential of state-of-the-art computing platforms to address challenging problems in large scale scientific computations and big-data analytics problems. Dr. Aktulga’s research is supported by NSF\, DOE\, AFRL\, NIH and the MSU Foundation. He is the recipient of the NSF CAREER award in 2019. \nTowards Fast\, Scalable and High Fidelity Reactive Molecular Dynamics Simulations\nReactive molecular dynamics (RMD) models bridge quantum-scale and classical MD approaches by explicitly modeling bond activity and redistribution of charges. As such they enable the study of important phenomena which otherwise is impractical using classical or quantum techniques. However\, RMD models have a significantly complex formulation\, making fast\, scalable and high fidelity RMD simulations extremely challenging to achieve. In this talk\, I will present our work towards addressing both the scalability and fidelity challenges. I will start by describing the parallel algorithms and numerical techniques that we developed for a fast implementation of the Reax Force Field (ReaxFF)\, which is used by hundreds of researchers worldwide. Particular emphasis will be on novel solvers we recently developed for the dynamic charge distribution problem that constitutes the most important scalability bottleneck in large RMD simulations. I will conclude the talk by outlining our efforts towards addressing the fidelity challenge\, i) through an automated force field framework for RMD models\, ii) by developing a novel hybrid ReaxFF/AMBER simulation software in the spirit of QM/MM techniques. \nProf. Aktulga is being hosted by the Glotzer Lab (Chemical Engineering). If you would like to meet with him during his visit\, please send an email to micde-events@umich.edu. If you are an MICDE students\, or a Chemical Engineering graduate student\, and would like to join Prof. Aktulga for lunch\, please RSVP here by September 23rd.  \n 
URL:https://micde.umich.edu/event/fall2019-aktulga-msu/
LOCATION:1303 EECS\, 1301 Beal Ave\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
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DTSTART;TZID=America/Detroit:20190930T110000
DTEND;TZID=America/Detroit:20190930T120000
DTSTAMP:20260608T094424
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SUMMARY:MICDE Seminar: Jason MacLean\, Associate Professor\, Neurobiology\, University of Chicago
DESCRIPTION:Bio: Jason MacLean is an Associate Professor in the Department of Neurobiology and director of undergraduate studies in neuroscience at the University of Chicago.  His research aims to define how information is encoded in the brain by large groups of synaptically interconnected neurons using a range of analytical approaches. He complements this work by simulating and training spiking neuronal networks. Jason completed his Ph.D. at the University of Manitoba in Canada\, and worked at Cornell University and Columbia University before establishing his own group at the University of Chicago in 2008. He and his wife have two children and he no longer has time for hobbies. \nRecurrent interactions can explain the variance in single trial responses\nTo develop a complete description of sensory encoding\, it is necessary to account for trial-to-trial variability in cortical neurons. Using a generalized linear model with terms corresponding to the visual stimulus\, mouse running speed\, and experimentally measured neuronal correlations\, we modeled short term dynamics of L2/3 murine visual cortical neurons to evaluate the relative importance of each factor to neuronal variability within single trials. We find single trial predictions improve most when conditioning on the experimentally measured local correlations in comparison to predictions based on the stimulus or running speed. Specifically\, accurate predictions are driven by positively co-varying and synchronously active functional groups of neurons. Including functional groups in the model enhances decoding accuracy of sensory information compared to a model that assumes neuronal independence. Functional groups\, in encoding and decoding frameworks\, provide an operational definition of Hebbian assemblies in which local correlations largely explain neuronal responses on individual trials. \nProf. MacLean is being hosted by Prof. Watson (Psychiatry). If you would like to meet with him during his visit\, please send an email to micde-events@umich.edu. If you are an MICDE student and would like to join Prof. MacLean for lunch please RSVP by Saturday\, September 28th. 
URL:https://micde.umich.edu/event/fall2019-maclean-uchicago/
LOCATION:Weiser Hall\, Room 555\, 500 Church St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
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