Principal Investigators

A diverse, multidisciplinary team that includes faculty from ASU’s Ira A. Fulton Schools of Engineering and collaborators from Vanderbilt University, Southwest Research Institute and Optimal Synthesis Inc.

Dr. Yongming Liu is a Professor in the School for Engineering of Matter, Transport and Energy at the Arizona State University. His research interests include fatigue and fracture analysis of metals and composite materials, probabilistic methods, computational mechanics, and risk management. He completed his PhD at Vanderbilt University, and obtained his Bachelors’ and Masters’ degrees from Tongji University in China. Dr. Liu is a member of AIAA,ASME, and ASCE and serves on several technical committees on probabilistic methods and advanced materials. His group is current working on several projects for damage analysis and prognosis sponsored by DOE, AFOSR, NASA, and DOT.

Dr. Aditi Chattopadhyay is a Regents’ Professor and Ira A. Fulton Professor of Mechanical and Aerospace Engineering in the School for Engineering of Matter, Transport and Energy and Director of the Adaptive, Intelligent Materials and Systems Center (AIMS) at Arizona State University (ASU).  She has held a resident Research Scientist position at the NASA Langley Research Center and summer faculty positions at the NASA Ames Research Center. She obtained her undergraduate degree in Aerospace Engineering from the Indian Institute of Technology, Kharagpur, India, and M.S. and Ph.D. degrees from the School of Aerospace Engineering at Georgia Institute of Technology, Atlanta, Georgia. Her research focuses on smart materials and adaptive structures, structural health monitoring (SHM) and damage prognosis, multiscale analysis, mechanics of composites and multidisciplinary design optimization. 

Dr. Jingrui He is an assistant professor in the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University. She received her Ph.D. from Carnegie Mellon University. She joined ASU in 2014 and directs the Statistical Learning Lab (STAR Lab). Her research focuses on rare category analysis, heterogeneous machine learning, active learning and semi-supervised learning, with applications in social media analysis, healthcare, manufacturing process, etc. She is the recipient of the NSF CAREER Award in 2016, the IBM Faculty Award in 2015 and 2014 respectively and the IBM Fellowship in 2009 and 2008 respectively. She has published more than 60 peer reviewed articles, and is the author of the book on Analysis of Rare Categories by Springer.

Dr. Lei Ying received his bachelor’s degree from Tsinghua University in Beijing, China, and his master’s and doctoral degrees in electrical and computer engineering from the University of Illinois at Urbana-Champaign. He is currently an associate professor in the School of Electrical, Computer and Energy Engineering at Arizona State University, and an Associate Editor of the IEEE/ACM Transactions on Networking. His research interest is broadly in the area of stochastic networks, including cloud computing, communication networks and social networks. He is coauthor with R. Srikant of the book Communication Networks: An Optimization, Control and Stochastic Networks Perspective, Cambridge University Press, 2014.

Dr. Mary Niemczyk (Passing) is an Associate Professor and Chair of Technological Entrepreneurship & Innovation Management (TEIM), program Chair of Aviation Programs and Air Transportation Management  and program Lead of the Department of Technology Management, College of Tech and Innovation. Dr. Niemczyk’s experience and expertise consists of education, aviation and entrepreneurship. She taught in the K-12 public school system prior to coming to ASU. She has also worked as a financial manager at a major US airline, and co-founded Research Integrations, an aviation human performance research company that focuses on issues concerning pilot training and human factors. In addition, Dr. Niemczyk has also started three other companies, including Mastering Learning, an instructional and learning strategies consulting firm. 

Dr. Nancy J. Cooke is a professor of Cognitive Science and Engineering in the Polytechnic School, one of the Ira A. Fulton Schools of Engineering at Arizona State University and is Science Director of the Cognitive Engineering Research Institute in Mesa, AZ. She is also Cognitive Track Editor of Human Factors, a member-at-large of the Human Factors and Ergonomics Society’s Executive Council, a member and incoming chair of the National Research Council’s Board on Human Systems Integration, a member of the National Research Council’s Soldier System Panel, and a member of the U.S. Air Force Scientific Advisory Board. Her research interests include the study of individual and team cognition and its application to the development of cognitive and knowledge engineering methodologies, healthcare, homeland security systems, remotely-piloted aircraft, and emergency response systems. In particular, Cooke specializes in the development, application, and evaluation of methodologies to elicit and assess individual and team cognition (i.e., team situation awareness, coordination) and performance.

Dr. Pingbo Tang is an assistant professor in the School of Sustainable Engineering and the Built Environment at Arizona State University. He obtained his Bachelor Degree of Civil Engineering in 2002, and his Master Degree of Bridge Engineering in 2005, both from Tongji University, Shanghai, China. His Ph.D. degree in Civil Engineering is from Carnegie Mellon University (2009). Dr. Tang completed his postdoctoral training in the Mapping and GIS lab at The Ohio State University from 2009 to 2010, and then worked as an assistant professor of Construction Engineering at Western Michigan University from 2010 to 2012. Dr. Tang’s research explores imaging (e.g., LiDAR) and information modeling techniques (e.g., Building Information Modeling) to support visual data-driven management of construction sites, constructed facilities, and civil infrastructure systems

Dr. Sankaran Mahadevan is Professor of Civil and Environmental Engineering at Vanderbilt University, Nashville, Tennessee, where he has served since 1988. He also has a joint appointment as Professor of Mechanical Engineering. His research interests are in reliability and uncertainty analysis methods, material degradation, structural health monitoring, design optimization, and model uncertainty. The methods have been applied to civil, mechanical and aerospace systems. This research has been funded by NSF, NASA (Glen, Marshall, Langley, Ames), FAA, U. S. DOE, U. S. DOT, Nuclear Regulatory Commission, U. S. Army Research Office, U.S. Air Force, U. S. Army Corps of Engineers, General Motors, Chrysler, Union Pacific, Transportation Technology Center, and the Sandia, Los Alamos, Idaho and Oak Ridge National Laboratories.

Dr. P.K. Menon founded Optimal Synthesis Inc. in 1992, and is serving as its Chairman & CEO and Chief Scientist. Previously, he has served as an Associate Professor of Aerospace Engineering at the Georgia Institute of Technology and as a Visiting Scientist at the NASA Ames Research Center. He worked as a Research Scientist at Integrated Systems Inc during 1983-1986, and with the Indian Space Research Organization as a Mission Analyst during 1976-1981. As of 2008, he has published 39 research papers in peer-reviewed journals and has presented more than 100 papers at international conferences. Dr. Menon received his Ph. D degree from Virginia Polytechnic Institute in 1983, an M. E. degree in Aeronautical Engineering from the Indian Institute Science in 1975 and a B. E. degree in Mechanical Engineering from the Osmania University in 1973.

Dr. Barron Bichon leads a team that assists clients in making confident decisions using computational models. His technical interests also include surrogate modeling, integrated computational materials engineering (ICME), reliability-based design, Bayesian statistics, composite materials, additive manufacturing, and global optimization techniques. In his recent work, Dr. Bichon leads a team applying data analysis techniques coupled with a computational framework to create a continuously updated predictive capability for manufacturing process control of bonded composite structures as part of a DARPA program. He also serves as the Validation & Certification technology lead for Lightweight Innovations For Tomorrow (LIFT), which is part of Manufacturing USA. Dr. Bichon received his B.S. in Civil Engineering from the University of Memphis, his M.S. in Civil Engineering from the University of Illinois at Urbana-Champaign and his Ph. D in Civil Engineering from Vanderbilt University. 

Dr. Hao Yan received the B.S. degree in Physics from the Peking University, Beijing, China, in 2011. He also received the M.S. degree in Statistics, the M.S. degree in Computational Science and Engineering, and the Ph.D. degree in Industrial Engineering from Georgia Institute of Technology, Atlanta, in 2015, 2016, 2017, respectively. Currently, he is an assistant professor in the School of Computing, Informatics, and Decision Systems Engineering in Arizona State University. His research interests focus on developing scalable statistical learning algorithms for large-scale high-dimensional data with complex heterogeneous structures to extract useful information for the purpose of system performance assessment, anomaly detection, intelligent sampling and decision making. Dr. Yan was also recipients of multiple awards including best paper award in IEEE TASE and ASQ Brumbaugh Award. Dr. Yan is a member of INFORMS and IIE. His group is currently working on several projects supported by NSF, NASA, and private sectors such as the Procter & Gamble company.