Salar Fattahi

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Today’s real-world problems are complex and large, often with overwhelmingly large number of unknown variables which render them doomed to the so-called “curse of dimensionality”. For instance, in energy systems, the system operators should solve optimal power flow, unit commitment, and transmission switching problems with tens of thousands of continuous and discrete variables in real time. In control systems, a long standing question is how to efficiently design structured and distributed controllers for large-scale and unknown dynamical systems. Finally, in machine learning, it is important to obtain simple, interpretable, and parsimonious models for high-dimensional and noisy datasets. Our research is motivated by two main goals: (1) to model these problems as tractable optimization problems; and (2) to develop structure-aware and scalable computational methods for these optimization problems that come equipped with certifiable optimality guarantees. We aim to show that exploiting hidden structures in these problems—such as graph-induced or spectral sparsity—is a key game-changer in the pursuit of massively scalable and guaranteed computational methods.

9.9.2020 MIDAS Faculty Research Pitch Video.

My research lies at the intersection of optimization, data analytics, and control.

Albert S. Berahas

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Albert S. Berahas is an Assistant Professor in the department of Industrial & Operations Engineering. His research broadly focuses on designing, developing and analyzing algorithms for solving large scale nonlinear optimization problems. Such problems are ubiquitous, and arise in a plethora of areas such as engineering design, economics, transportation, robotics, machine learning and statistics. Specifically, he is interested in and has explored several sub-fields of nonlinear optimization such as: (i) general nonlinear optimization algorithms, (ii) optimization algorithms for machine learning, (iii) constrained optimization, (iv) stochastic optimization, (v) derivative-free optimization, and (vi) distributed optimization.

9.9.2020 MIDAS Faculty Research Pitch Video.

Robert Hampshire

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He develops and applies operations research, data science, and systems approaches to public and private service industries. His research focuses on the management and policy analysis of emerging networked industries and innovative mobility services such as smart parking, connected vehicles, autonomous vehicles, ride-hailing, bike sharing, and car sharing. He has worked extensively with both public and private sector partners worldwide. He is a queueing theorist that uses statistics, stochastic modeling, simulation and dynamic optimization.

Raed Al Kontar

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My research broadly focuses on developing data analytics and decision-making methodologies specifically tailored for Internet of Things (IoT) enabled smart and connected products/systems. I envision that most (if not all) engineering systems will eventually become connected systems in the future. Therefore, my key focus is on developing next-generation data analytics, machine learning, individualized informatics and graphical and network modeling tools to truly realize the competitive advantages that are promised by smart and connected products/systems.

 

Romesh Saigal

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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.

Lawrence Seiford

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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.

Viswanath Nagarajan

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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.

Siqian Shen

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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 ScienceOperations ResearchMathematical ProgrammingManufacturing and Service Operations ManagementINFORMS Journal on ComputingTransportation Research Part BIEEE 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.

Stephen M. Pollock

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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

Judy Jin

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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:

  1. Advanced quality control methodologies for system monitoring, diagnosis and control with temporally and spatially dense operational/sensing data.
  2. Multi-scale data transform and high order tensor data analysis for modeling, analysis, classification, and making inferences of multistream sensing signals.
  3. 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.
  4. 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.

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