Contributing Authors
John R. Birge (“Optimization Modeling and Techniques for Systemic Risk Assessment and Control in Financial Networks”) is the Jerry W. and Carol Lee Levin Distinguished Service Professor of Operations Management at the University of Chicago Booth School of Business. His work focuses on application, theory, and computation for decision making under uncertainty with applications in the management of operations in energy, finance, healthcare, manufacturing, public policy, and transportation. He is an INFORMS Fellow, MSOM Society Distinguished Fellow, member of the U.S. National Academy of Engineering, and Editor-in-Chief of Operations Research.
Satish Bukkapatnam (“Change Detection and Prognostics for Transient Real-World Processes Using Streaming Data”) received his PhD in Industrial and Manufacturing Engineering from the Pennsylvania State University. He currently serves as Rockwell International Professor in the Department of Industrial and Systems Engineering at Texas A&M University, and is the Director of the Texas A&M Engineering Experimentation Station (TEES) Institute for Manufacturing Systems. His research addresses the harnessing of high-resolution nonlinear dynamic information, especially from wireless MEMS sensors, to improve the monitoring and prognostics, mainly of precision manufacturing, and cardiorespiratory processes. He is a Fellow of the Institute for Industrial and Systems Engineers (IISE) and the Society of Manufacturing Engineers (SME).
Jeffrey D. Camm (“How to Influence and Improve Decisions Through Optimization Models”) is Associate Dean of Business Analytics and the Inmar Presidential Chair in Analytics at the Wake Forest University School of Business. His scholarship is on the application of optimization modeling to difficult decision problems. He previously served as the Editor-in-Chief of Interfaces, was the 2016 recipient of the Kimball Medal for service to the profession, and was named an INFORMS Fellow in 2017. He has consulted for numerous corporations including among others, Procter and Gamble, Owens Corning, GE, Tyco, Ace Hardware, Boar’s Head, Brooks Running Shoes, and Kroger.
Yunxiao Deng (“Coalescing Data and Decision Sciences for Analytics”) is a PhD candidate in operations research at the Daniel J. Epstein Department of Industrial and Systems Engineering at the University of Southern California. Her research interests are in stochastic optimization, particularly numerical methods for solving two-stage stochastic programs with applications in data-driven decisions.
Brian T. Denton (“Optimization of Sequential Decision Making for Chronic Diseases: From Data to Decisions”) is a Professor and Chair of the Department of Industrial and Operations Engineering at University of Michigan, in Ann Arbor, MI. He is also a Professor in the Department of Urology and a member of the Cancer Center at University of Michigan. He is past president of the INFORMS Health Applications Section and Past President of INFORMS. His research interests are in optimization under uncertainty with applications to chronic diseases including cancer and cardiovascular disease.
Peter I. Frazier (“Bayesian Optimization”) is an Associate Professor in the School of Operations Research and Information Engineering at Cornell University, and a Staff Data Scientist at Uber. He received his PhD in Operations Research and Financial Engineering from Princeton University in 2009. His research is in optimal learning, including Bayesian optimization and incentive design for social learning, with applications in e-commerce, the sharing economy, and materials design. He is an Associate Editor for Operations Research, ACM TOMACS, and IISE Transactions, and is the recipient of an AFOSR Young Investigator Award and an NSF CAREER Award.
Dorit S. Hochbaum (“Machine Learning and Data Mining with Combinatorial Optimization Algorithms”) is Professor of Engineering at University of California, Berkeley. She is known for her work on approximation algorithms, facility location, covering and packing problems, and scheduling, as well as work on flow and cut algorithms, Markov Random Fields, isotonic regression, image segmentation, and clustering. Professor Hochbaum was awarded an honorary doctorate from the University of Copenhagen, recognizing her ground-breaking achievements and leadership in optimization and in the field of approximation algorithms. Among other honors, she is an INFORMS Fellow, a SIAM Fellow, and winner of the 2011 INFORMS Computing Society prize for the best paper dealing with the Operations Research/Computer Science interface.
Ashif Sikandar Iquebal (“Change Detection and Prognostics for Transient Real-World Processes Using Streaming Data”) is a PhD candidate in the department of Industrial and Systems Engineering, Texas A&M University, College Station. Prior to joining Texas A&M University, he received his BE in Industrial Engineering from Indian Institute of Technology, Kharagpur, India. His research interests include monitoring and prognostics based on advanced sensor and image data, and graph-theoretic approaches with applications mainly in manufacturing and healthcare systems. He is a student member of IEEE, the Institute of Industrial Engineers, and INFORMS.
Aein Khabazian (“Optimization Modeling and Techniques for Systemic Risk Assessment and Control in Financial Networks”) is a PhD student in the Department of Industrial Engineering, University of Houston. Her research focuses on systemic risk assessment and control in financial networks. She has completed several technical reports in this area and has published one paper in Management Science.
Manuel Laguna (“Tabu and Scatter Search: Principles and Practice”) is the MediaOne Professor of Management Science and Faculty Director of Global Initiatives at the Leeds School of Business of the University of Colorado, Boulder. He has done extensive research in the interface between computer science, artificial intelligence and operations research, resulting in over one hundred publications, including four books. He has received research funding from private industry and government agencies such as the National Science Foundation, the Office of Naval Research, and the Environmental Protection Agency. He is cofounder of OptTek Systems, a Boulder-based software and consulting company that provides optimization solutions. He is the Editor-in-Chief of the Journal of Heuristics and has been Division Chair, Senior Associate Dean, and Interim Dean at the Leeds School of Business.
Junyi Liu (“Coalescing Data and Decision Sciences for Analytics”) is a PhD student in the Daniel J. Epstein Department of Industrial and Systems Engineering at the University of Southern California. Prior to that, she graduated from the School of Gifted Young at the University of Science and Technology of China with a major in statistics. Junyi’s research interests include models, algorithms, and applications of stochastic programming. She is also devoted to building the bridge connecting the gap between data science and decision science with theoretical fundamentals and computational efficiency. She was the recipient of a Viterbi Graduate School Graduate Fellowship in 2015.
Gilberto Montibeller (“Behavioral Challenges in Policy Analysis with Conflicting Objectives”) is Full Professor of Management Science and the Head of the Management Science and Operations Group at Loughborough University (UK). He is an expert on multicriteria decision analysis and his research is focused on behavioral aspects of decision analysis. Dr. Montibeller has published widely in the field, serving as Area Editor of the Journal of Multi-Criteria Decision Analysis (2009–2018), and is currently on the editorial boards of the INFORMS journal Decision Analysis and the EURO Journal on Decision Processes. The quality of his research has been recognized by best publication awards by the INFORMS Decision Analysis Society, the Society for Risk Analysis, and the International Society on Multi-Criteria Decision Making. He has extensive experience in decision-analytic applications, particularly in global health and strategic prioritizations.
David Newton (“Stochastic Gradient Descent: Recent Trends”) is a PhD student in the Department of Statistics at Purdue University. He works with Raghu Pasupathy in the area of stochastic optimization. His current work focuses on the asymptotic properties of stochastic gradient descent with adaptive sampling.
Nilay Noyan (“Risk-Averse Stochastic Modeling and Optimization”) is an Associate Professor in the Industrial Engineering Program at Sabancı University, Turkey. She received her PhD degree in Operations Research from Rutgers University (RUTCOR) in 2006. Her research interests include decision making under uncertainty, stochastic programming, risk modeling, large-scale optimization, and stochastic optimization applications with an emphasis on humanitarian logistics. She is a recipient of the Young Scientist (BAGEP) Research Award of the Science Academy, Turkey, and the Research Encouragement Award by METU Prof. Dr. Mustafa N. Parlar Education and Research Foundation.
Raghu Pasupathy (“Stochastic Gradient Descent: Recent Trends”) is an Associate Professor in the Department of Statistics at Purdue University. His research focuses on Monte Carlo methods with a recent emphasis on stochastic optimization and rare-event estimation. More information on Raghu Pasupathy, including recent publications, software, and editorial positions held, can be obtained through his website http://web.ics.purdue.edu/∼pasupath/.
Jiming Peng (“Optimization Modeling and Techniques for Systemic Risk Assessment and Control in Financial Networks”) is an associate professor in the Department of Industrial Engineering, University of Houston. He received his PhD degree in Operations Research from Delft University of Technology, Holland in 2001. Previously he worked as an assistant professor in McMaster University in Canada and the University of Illinois at Urbana-Champaign before relocating to Houston in 2013. His research interests lie mainly in optimization modeling, theory, and techniques with applications to healthcare, financial engineering, and big data. His research has been recognized by several awards and highlighted by the National Science Foundation.
Linus Schrage (“A Guide to Optimization Based Multiperiod Planning”) is emeritus professor at the University of Chicago Booth School of Business. He previously taught at Stanford University, has held several visiting professor positions, and has also worked at NASA. Together with Kevin Cunningham, he developed the LINDO, LINGO, and What’sBest! optimization software. Schrage and Cunningham received the INFORMS Impact Prize for their work on developing algebraic modeling languages, as well as the PC Magazine annual Award for Technical Excellence for the What'sBest! spreadsheet optimizer. Schrage’s research has ranged over supply chain management, inventory management, cutting stock, optimization, auctions, logistics, production scheduling, simulation, queueing theory, planning under uncertainty, and computer science. He is an INFORMS Fellow, as well as an MSOM Distinguished Fellow.
Suvrajeet Sen (“Coalescing Data and Decision Sciences for Analytics”) is professor at the Daniel J. Epstein Department of Industrial and Systems Engineering at the University of Southern California. Prior to joining USC, he was professor at Ohio State University and the University of Arizona. He has also served as a Program Director at NSF. Professor Sen’s research is devoted to many categories of optimization models, and he has published over a hundred papers, with the vast majority devoted to models, algorithms, and applications of Stochastic Programming (SP). His research portfolio has garnered over $10 million as PI for basic research grants in SP. He has served on several editorial boards, including Operations Research as Area Editor for Optimization. Professor Sen was instrumental in founding the INFORMS Optimization Society and its new journal INFORMS Journal on Optimization. He has made several plenary presentations at national and international conferences, including the INFORMS Annual Meeting (2016), International Conference on Stochastic Programming (2001, 2007), and INFORMS Computing Society (2015). He led a group of researchers who were awarded the INFORMS Computing Society prize for “seminal work in Stochastic Mixed-Integer Programming” in 2015. Professor Sen is a Fellow of INFORMS.
Craig A. Tovey (“Nature-Inspired Heuristics: Overview and Critique”) is professor and H. Milton Stewart Fellow in the School of Industrial and Systems Engineering at Georgia Tech. He holds an MS in computer science and PhD in operations research from Stanford University. He is cofounder and codirector of the Georgia Tech Center for Biologically Inspired Design. His research in biology and nature-inspired heuristics has been recognized by a Jacob Wolfowitz prize and a Golden Goose award. Tovey is also the recipient of a Presidential Young Investigator award, a National Research Council Senior Associateship, and the ACM SigEcon Test of Time Award. His Erdös-Bacon number is 4.
Farzad Yousefian (“Stochastic Gradient Descent: Recent Trends”) is assistant professor in the School of Industrial Engineering and Management at Oklahoma State University. Before joining OSU, he was a postdoctoral researcher in the Department of Industrial and Manufacturing Engineering at Penn State. He received his PhD in industrial engineering from the University of Illinois at Urbana-Champaign in 2013. His research interests lie in the development of computational methods for solving stochastic optimization and equilibrium problems arising from machine learning and multiagent systems. He is the recipient of the best theoretical paper award at the 2013 Winter Simulation Conference.
