Siqian Shen is an Associate Professor of Industrial and Operations Engineering at the University of Michigan and also serves as an Associate Director in the Michigan Institute for Computational Discovery & Engineering (MICDE). Her theoretical research interests are in integer programming, stochastic/robust optimization, and network optimization. Applications include optimization and risk analysis of energy, healthcare, cloud-computing, and transportation systems. Her work has been supported by the National Science Foundation, Army Research Office, Department of Energy, and industrial funds. Her work has appeared in journals such as Management Science, Operations Research, Mathematical Programming, Manufacturing and Service Operations Management, INFORMS Journal on Computing, Transportation Research Part B, IEEE Transactions on Power Systems, and others. She is the recipient of the INFORMS Computing Society Best Student Paper award (runner-up), IIE Pritsker Doctoral Dissertation Award (1st Place), IBM Smarter Planet Innovation Faculty Award, and Department of Energy (DoE) Early Career Award.
Judy Jin, PhD, is Professor of Industrial and Operations Engineering in the College of Engineering at the University of Michigan, Ann Arbor.
Prof. Jin’s research focuses on the development of new data fusion methodologies for improving system operation and quality with the emphasis on fusion of data and engineering knowledge collected from disparate sources by integrating multidisciplinary methods. Her research has been widely applied in both manufacturing and service industry by providing techniques for knowledge discovery and risk-informed decision making. Key research issues are being pursued:
- Advanced quality control methodologies for system monitoring, diagnosis and control with temporally and spatially dense operational/sensing data.
- Multi-scale data transform and high order tensor data analysis for modeling, analysis, classification, and making inferences of multistream sensing signals.
- Optimal sensor distribution and hierarchical variable selection methods for system abnormal detection and sensor fusion decisions, which integrates the causal probability network model, statistical change detection, set-covering algorithm, and hierarchical lasso regression.
- A unified approach for variation reduction in multistage manufacturing processes (MMPs) using a state space model, which blend the control theory with advanced statistics for MMPs sensing, monitoring, diagnosis and control, integrative design of process tolerance and maintenance policy considering the interaction between product quality and tool reliability.
Data science applications: (a) Smart manufacturing with sensor fusion, process monitoring, diagnosis and control (e.g., metal forming including stamping, forging, casting and rolling), assembly, ultrasonic welding, photovoltaic thin film deposition. (b) Travel time estimation and traffic prediction for intelligent transportation systems. (c) Multi-stream data analysis of human motion/vehicle crash testing data for improving vehicle design and safety. (d) Risk informed decision support for healthcare and clinical decisions. (e) Customer behavior modeling for fraud detection in healthcare and telecommunication. (f) Human decision-making behavior modeling in a dynamic/emergency environment.
Dr. Kalbfleisch is a Professor of Biostatistics and Statistics at the University of Michigan, Ann Arbor. He served as chair of the Department of Biostatistics, School of Public Health, from 2002 to 2007 and as Director of the Kidney Epidemiology and Cost Center from 2008 to 2011. He received his Ph.D. in statistics in 1969 from the University of Waterloo. He was an assistant professor of statistics at the State University of New York at Buffalo (1970-73) and on faculty at the University of Waterloo (1973-2002). At Waterloo, he served as chair of the Department of Statistics and Actuarial Science (1984-1990) and as dean of the faculty of Mathematics (1990-1998). He has held visiting appointments as Professor at the University of Washington, the University of California at San Francisco, the University of Auckland, Fred Hutchinson Cancer Research Center and the National University of Singapore. He has interests in and has publised in various areas of statistics and biostatistics including life history and survival analysis, likelihood methods of inference, bootstrapping and estimating equations, mixture and mixed effects models and medical applications, particularly in the area of renal disease and organ transplantation. Dr. Kalbfleisch is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics. He is also an elected member of the International Statistical Institute, a Fellow of the Royal Society of Canada and a Gold Medalist of the Statistical Society of Canada. He also received the Distinguished Research Award from the UM School of Public Health in 2011.
A primary research interest is in the development of models and methods for analyzing failure time or event history data. Applications of this work arise in many areas including epidemiology, medicine, demography and engineering. In event history data, interest centers on the timing and occurrence of various kinds of events such as, for example, repeated infections or recurrences of disease, or other sequences of events that may occur during a study period. I have been particularly interested in situations in which only partial data or data subject to sampling bias are available.
In recent years, I have been working on statistical aspects of problems associated with End Stage Renal Disease and solid organ transplantation. The Kidney Epidemiology and Cost Center has many projects associated with these including various projects funded through the Centers for Medicare and Medicaid Services. This provides a rich area of application where statistical methods and developments play a substantial role in defining public policy. I am particularly interested in the development of appropriate methods for the use of such data in profiling and/or ranking medical providers.
I have recently worked on the optimization and simulation of kidney paired donation programs. In these, candidates in need of a kidney transplant who have a willing but incompatible living donor are entered into a pool and we seek exchanges of donors to overcome incompatibilities. Added to this is the potential for non-directed donors who can give a kidney to one member of the pool and hence create a chain of transplants. Our methods use integer programming methods to create flexible allocation schemes that have the potential to provide substantial increases in the number of transplants achieved.
Jon’s research focus is on nonlinear discrete optimization (NDO). Many practical engineering problems have physical aspects which are naturally modeled through smooth nonlinear functions, as well as design aspects which are often modeled with discrete variables. Research in NDO seeks to marry diverse techniques from classical areas of optimization, for example methods for smooth nonlinear optimization and methods for integer linear programming, with the idea of successfully attacking natural NDO models for practical engineering problems. On particular area of applied interest is environmental monitoring and the framework of maximum-entropy sampling.
Dr. Byon’s research interests include reliability evaluation, fault diagnosis/condition monitoring, predictive modeling and data analytics, and operations and maintenance decision-making for stochastic systems. Her recent research focuses on uncertainty quantification of stochastic systems using stochastic simulations, reliability analysis and improvement of large-scale, interconnected systems with applications to renewable power power systems and manufacturing processes. She is a member of IIE, INFORMS, and IEEE.
Amy Cohn, PhD, is a Thurnau Professor in the Department of Industrial and Operations Engineering at the University of Michigan College of Engineering and Director of the Center for Healthcare Engineering and Patient Safety. Her primary research interest is in robust and integrated planning for large-scale systems, predominantly in healthcare and aviation applications. Her primary teaching interest is in optimization techniques, at both the graduate and undergraduate level.
Mark S. Daskin, PhD, holds the Clyde W. Johnson Collegiate Professorship in the Department of Industrial and Operations Engineering of the College of Engineering at the University of Michigan, Ann Arbor. He is a past-president of INFORMS, the Institute for Operations Research and the Management Sciences. He is also the former chair of the IE/MS Department as well as a past editor-in-chief of IIE Transactions, the flagship journal of IIE, the Institute of Industrial Engineers. He is a past vice president of publications of INFORMS. Finally, he has served on a number of editorial boards and is a former editor-in-chief of Transportation Science.
Prof. Daskin’s research focuses on supply chain network design in general and facility location models in particular. He is currently studying reliability in supply chain design as well as sustainability issues associated with supply chains. He is also studying problems in health care operations research with a current focus on transplantation problems and the assignment of residents and interns to patients. He has taught courses on: probability, statistics, operations research, supply chain reliability, location modeling, health care operations research, service operations management, and heuristic algorithms. Currently, Prof. Daskin is teaching a course on service operations management for upper level undergraduates and MS students.
My research interests are in data-driven sequential decision making and optimization under uncertainty with applications to medicine. I have a cross-appointment in the School of Medicine and I am a member of the Cancer Center and the Institute for Healthcare Policy and Innovation (IHPI) at University of Michigan. My current research projects are investigating new ways to use longitudinal data to improve decisions related to the using of biomarkers, imaging, and medication for early detection of cancer and prevention of cardiovascular disease.