Professor Saigal has held faculty positions at the Haas School of Business, Berkeley and the department of Industrial Engineering and Management Sciences at Northwestern University, has been a researcher at the Bell Telephone Laboratories and numerous short term visiting positions. He currently teaches courses in Financial Engineering. In the recent past he taught courses in optimization, and Management Science. His current research involves data based studies of operational problems in the areas of Finance, Transportation, Renewable Energy and Healthcare, with an emphasis on the management and pricing of risks. This involves the use of data analytics, optimization, stochastic processes and financial engineering tools. His earlier research involved theoretical investigation into interior point methods, large scale optimization and software development for mathematical programming. He is an author of two books on optimization and large set of publications in top refereed journals. He has been an associate editor of Management Science and is a member of SIAM, AMS and AAAS. He has served as the Director of the interdisciplinary Financial Engineering Program and as the Director of Interdisciplinary Professional Programs (now Integrative Design + Systems) at the College of Engineering.
Professor Seiford’s research interests are primarily in the areas of quality engineering, productivity analysis, process improvement, multiple-criteria decision making, and performance measurement. In addition, he is recognized as one of the world’s experts in the methodology of Data Envelopment Analysis. His current research involves the development of benchmarking models for identifying best-practice in manufacturing and service systems. He has written and co-authored four books and over one hundred articles in the areas of quality, productivity, operations management, process improvement, decision analysis, and decision support systems.
My research is on the design of efficient (approximation) algorithms for combinatorial optimization problems. I work on deterministic optimization models, as well as richer models that handle uncertainty in data: stochastic, robust and online optimization. I am also interested in algorithms for scheduling and energy-efficiency in data centers.
Professor Pollock has taught courses in decision analysis, mathematical modeling, dynamic programming, and stochastic processes. He has applied operations research and decision analysis methods to problems in defense, criminal justice, manufacturing, epidemiology and medicine. He has authored over 60 technical papers, co-edited two books, and has served as a consultant to over 30 organizations, and on the editorial boards of three major journals.
He was chair of the IOE Department, chaired the University’s Research Policies Committee and Tenure Committee, served as Director of the Engineering College’s Financial Engineering Program and Engineering Global Leadership Program, was a member the College of Engineering’s Executive Committee, and a recipient of the College’s Attwood Award.
He has served on and chaired various NSF and NRC advisory boards and panels, and on the Army Science Board. He was President of the Operations Research Society of America, was awarded the 2001 INFORMS Kimball Medal, is a fellow of INFORMS and AAAS and is a member of the National Academy of Engineering
The research of Shen’s group covers the following areas that are closely related to data science and large-scale optimization problems.
– We develop new decomposition paradigms for stochastic integer programming models. We focus on two-stage stochastic integer programs, and advance decomposition paradigms based on special structures of specific risk-averse programs, and also based on special integer-programming structures.The new decomposition paradigms can be widely applied to large-scale complex system design and operations management, including optimizing critical interdependent infrastructures such as power grids, transportation systems, and cyber-clouds.
– We optimize carsharing system design/operations and real-time ridesharing problems including supply-demand matching for ride-pooling as well as service pricing.
– We apply risk-averse models and approaches to optimize integrated system design and service operations with multiple resources, multiple stages of service, and multiple stakeholders with diverse decision preferences. We in particular focus on related problems in healthcare operations management.
– We develop data-driven optimization methods that are suited to dispatching power systems with both fluctuating renewable energy sources and flexible loads contributing to balancing reserves via load control. We also study multi-stage stochastic programs over various risk and robustness measures for transmission planning with complex spatio-temporal data correlations.
– We study network interdiction models and design algorithms for specially structured networks (e.g., trees, small-world networks) in defense-related problems.
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.
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.
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.