Dr. Harrison Kim is an Associate Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign (UIUC) with appointment at the Beckman Institute and the Computational Science and Engineering. Dr. Kim’s research focuses on a variety of areas of complex systems design and large-scale computation and optimization. Dr. Kim’s current research topics are energy systems engineering; renewable, hybrid energy conversion and distribution; user-centered sustainable product design; product design analytics; multidisciplinary, multilevel optimization; green design. Application areas are automotive, consumer electronics, heavy-duty equipment, national security, commercial/military system of systems, and information technology. Dr. Kim has received numerous recognitions including the National Science Foundation’s CAREER Award, Dean’s Award in Excellence in Research (Xerox Award), Best Paper Award in ASME Design for Manufacturing and Life Cycle Conference, and news media coverage in the USA Today and the Chicago Tribune. Harrison Kim earned his Ph.D. degree at the University of Michigan in 2001 in the area of Engineering System Design and Optimization in Mechanical Engineering under the supervision of Prof. Panos Papalambros. He joined the University of Illinois in 2005 after Business-IT consulting experience and postdoctoral training under Prof. Wei Chen at Northwestern University and has been leading the Enterprise Systems Optimization Lab.
Dr. Eunshin Byon is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan, Ann Arbor, USA. She received her Ph.D. degree in Industrial and Systems Engineering from the Texas A&M University, College Station, USA, and joined the University of Michigan in 2011.
Importance sampling has been used to improve the efficiency of simulations where the simulation output is uniquely determined, given a fixed input. We extend the theory of importance sampling to estimate a system’s reliability with stochastic simulations. Thanks to the advance of computing power, stochastic computer models are employed in many applications to represent a complex system behavior. More
To quantify and minimize the uncertainties in the design and operational stage, we model and analyze the dependency of wind turbine responses (e.g., power generation, loads and condition monitoring sensor measurement) on operating conditions and the interactions among turbines. Our research entails several areas… More