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Dr. Martin Webster
Senior Research Associate, Lubricants Technology
ExxonMobil Research and Engineering
Martin Webster currently holds the position of senior research associate and program leader at ExxonMobil’s Corporate Strategic Research laboratory in Annandale, New Jersey. He is responsible for a number of the longer range lubrication and tribology research programs. Webster gained his Ph.D. in tribology from Imperial College London, where his work on rough surface contact mechanics resulted in being awarded the Institute of Mechanical Engineers Tribology Bronze Medal in 1986. Following a postdoctoral assignment with Shell Research in the UK, he spent 2 years working on the design and analysis of wind turbine systems with the UKbased Wind Energy Group (WEG). He has since accumulated more than 20 years of experience working with ExxonMobil in both research and product development positions. His research focus has been on the fundamentals of lubricated contacts, including the measurement and characterization of elastohydrodynamic lubrication (EHL) performance, modeling EHL contacts, rolling contact fatigue phenomenon, and the interactions of lubricant components with engineering surfaces. He has published numerous papers and patents in each of these areas. He has been active in various societies and technical committees, including the Gear Research Institute, the ASME Rolling Element Bearing Committee, and the STLE Gears and Gear Lubrication technical committee. In 2006 he was elected to join the STLE Board of Directors which culminated in serving as the STLE President 2015-16.
Dr. Marius Stan
Senior Scientist and Program Lead, Intelligent Materials Design
Argonne National Laboratory
Dr. Marius Stan is the Intelligent Materials Design Lead in the Argonne National Laboratory’s Applied Materials division. Stan is a computational physicist and chemist interested in complexity, non-equilibrium thermodynamics, heterogeneity, and materials design for energy and electronics applications. He uses artificial intelligence, machine learning, and multi-scale computer simulations to understand and predict properties and evolution of complex physical systems.
Stan came to Argonne and the University of Chicago in 2010, from Los Alamos National Laboratory. He is a Senior Fellow at the University of Chicago’s Computation Institute (CI) and a senior Fellow of the Northwestern-Argonne Institute for Science and Engineering (NAISE).
The goal of Stan’s research is to discover or design materials, structures, and device architectures for energy applications, such as nuclear energy and energy storage, and for the new generation computers. To that end, he develops theory-based (as opposite to empirical) mathematical models of thermodynamic and chemical properties of imperfect materials. The imperfection comes from defects or deviations from stoichiometry (e.g., in battery electrodes), from irradiation (e.g. in nuclear fuels), or doping (e.g. computer memory devices). Then Stan uses the models in computer simulations of coupled heat and chemical transport, micro(nano)-structure evolution, phase-stability, and phase transformations. To analyze large and complex experimental and computational data sets, Stan uses Bayesian analysis and machine learning methods based on regression and evolutionary (genetic) algorithms that can produce robust data screening and sampling. In parallel, Stan designs experiments to validate the models and simulations.
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Preliminary Program Guide