Appendix C

Biographical Sketches of Committee Members

David M. Higdon, Chair, is a professor in the Social Decision Analytics Laboratory at Virginia Tech. Previously, he spent 10 years as a scientist or group leader of the Statistical Sciences Group at Los Alamos National Laboratory. Dr. Higdon holds a B.A. and an M.A. in mathematics from the University of California, San Diego, and a Ph.D. in statistics from the University of Washington. He is an expert in Bayesian statistical modeling of environmental and physical systems, combining physical observations with computer simulation models for prediction and inference. His research interests include space-time modeling; inverse problems in hydrology and imaging; statistical modeling in ecology, environmental science, and biology; multiscale models; parallel processing in posterior exploration; statistical computing; and Monte Carlo and simulation-based methods. Dr. Higdon has served on several advisory groups concerned with statistical modeling and uncertainty quantification, and co-chaired the National Research Council (NRC) Committee on Mathematical Foundations of Validation, Verification, and Uncertainty Quantification. He is a fellow of the American Statistical Association.

Robert L. Axtell is a professor and chair of the Department of Computational Social Science at George Mason University. Previously he was a senior fellow in the Economic Studies and Governance Studies programs at the Brookings Institution. He holds a B.S. from the University of Detroit and an interdisciplinary Ph.D. from Carnegie Mellon University, where he studied economics, computer science, and public policy. Dr. Axtell’s research involves agent-based computational models of social phenomena, in which autonomous software agents—each agent representing an individual person—interact according to simple rules of behavior, with patterns and structure emerging at the aggregate level. His current focus is on the creation of entire artificial economies consisting of hundreds of millions of agents. His book Growing Artificial Societies: Social Science from the Bottom Up (MIT Press, 1996), co-authored with J.M. Epstein, is widely cited as an early statement of the potential of multiagent systems to more fully represent social processes.

Venkatramani Balaji is head of the Modeling Systems Group at the Geophysical Fluid Dynamics Laboratory and Princeton University. His group provides a software environment where scientific groups can develop new physics and new algorithms concurrently, and also coordinate their efforts. Dr. Balaji received an M.S. from the Indian Institute of Technology, Kanpur, and a Ph.D. from The Ohio State University, both in physics. He is an expert in parallel computing and scientific infrastructure, and has pioneered the use of model frameworks, such as the Flexible Modeling System, and community standards needed to construct climate models from independently

developed components sharing a technical architecture. Dr. Balaji served on the NRC Committee on a National Strategy for Advancing Climate Modeling. He is a sought-after speaker and lecturer and is committed to provide training in the use of climate models in developing nations, leading workshops to advanced students and researchers in South Africa and India.

Lawrence E. Buja directs the Climate Science and Applications Program at the National Center for Atmospheric Research (NCAR). The program examines societal vulnerability, impacts, and adaptation to climate change using climate change scenarios; vulnerability analyses; integrated analyses of changes in climate, land use, conventional pollution, biodiversity, and human systems; and decision support tools. Previously, Dr. Buja was scientific project manager for NCAR’s Community Climate System Model, which simulates Earth’s past, present, and future climates and is one of the models used by the Intergovernmental Panel on Climate Change (IPCC). Dr. Buja was a contributing author to both the third and fourth IPCC assessments. Dr. Buja also works with the World Bank, the Inter-American Development Bank, and other international agencies, applying NCAR’s climate and social science expertise to help guide sustainable development strategies throughout the developing world. He has a B.S. and an M.S. in atmospheric science from Iowa State University and a Ph.D. in meteorology from the University of Utah.

Katherine V. Calvin is a scientist at the Pacific Northwest National Laboratory’s Joint Global Change Research Institute. Prior to joining the institute in 2008, she spent 2 years as an international energy analyst at the U.S. Energy Information Administration. Dr. Calvin earned bachelor’s degrees in mathematics and in computer science from the University of Maryland, and M.S. and Ph.D. degrees in management science and engineering from Stanford University. Her work focuses on model development and scenario analysis using a global change integrated assessment model, with an emphasis on assessing climate change impacts and potential adaptation, and examining the effects of bioenergy and land policy on land use. She also coordinates regional model comparison exercises of Asia and Latin America. Dr. Calvin was a lead author for the National Climate Assessment and a contributing author for an IPCC assessment, and is currently on the scientific steering committee for the Land Use Model Intercomparison Project.

Kathleen M. Carley is a professor of computation, organization, and society at the Institute for Software Research at Carnegie Mellon University. She also directs the university’s Center for Computational Analysis of Social and Organizational Systems, which brings together network analysis, computer science, and organization science. Dr. Carley holds bachelor’s degrees in economics and in political science from the Massachusetts Institute of Technology, and a Ph.D. in sociology from Harvard University. She uses organization theory, dynamic network analysis, social networks, multiagent systems, and computational social science to examine how cognitive, social, technological, and institutional factors affect individual, team, social, and policy outcomes in areas ranging from public health to counterterrorism to cybersecurity. Dr. Carley has participated in several NRC studies on modeling and intelligence needs, including the Committee on Modeling and Simulation for Defense Transformation and the Committee on the Future U.S. Workforce for Geospatial Intelligence. She is a fellow of the Institute of Electrical and Electronics Engineers (IEEE), and received the lifetime achievement award from the Mathematical Sociology section of the American Sociological Association, and the Simmel award for advances in social networks and network science from the International Network for Social Network Analysis.

Rebecca Castaño is division technologist for the Mission Systems and Operations Division and the discipline area program manager for Applied Sciences at the Jet Propulsion Laboratory. She works to ensure that relevant new technologies are developed, matured, validated, and infused into instruments and missions. From 2002 to 2007, she was the Supervisor of the Machine Learning Systems Group, which uses learning algorithms, data mining, knowledge discovery, pattern recognition, and automated classification and clustering to carry out automated analyses of remote sensing data. Dr. Castaño received her B.S. in electrical and computer engineering from the University

of Iowa and her M.S. and Ph.D. in electrical and computer engineering from the University of Illinois, where she focused on computer vision. Dr. Castaño’s research interests include machine learning, computer vision, and pattern recognition. She has spent the past 5 years advancing the state of the art in onboard science analysis methods, and has contributed to software operating on Earth orbiters and Mars rovers. She received NASA’s Exceptional Engineering Achievement Medal in 2008.

Ronald R. Coifman (NAS) is the Phillips Professor of Math and Computer Science at Yale University. His research interests include nonlinear Fourier analysis, wavelet theory, singular integrals, numerical analysis and scattering theory, and real and complex analysis. He is also interested in new mathematical tools for efficient computation and transcriptions of physical data, as well as their applications to numerical analysis, feature extraction recognition, and denoising. He is currently developing analysis tools for spectrometric diagnostics and hyperspectral imaging. Dr. Coifman served on the National Academies of Sciences, Engineering, and Medicine’s Board on Mathematical Sciences and Their Applications and the NRC Committee on the Analysis of Massive Data. He is a recipient of the 1996 Defense Advanced Research Projects Agency Sustained Excellence Award, the 1996 Connecticut Science Medal, the 1999 Pioneer Award of the International Society for Industrial and Applied Science, and the 1999 National Medal of Science. Dr. Coifman is a member of the American Academy of Arts and Sciences, the Connecticut Academy of Science and Engineering, and the National Academy of Sciences. He received his Ph.D. from the University of Geneva.

Omar Ghattas is the John A. and Katherine G. Jackson Chair in Computational Geosciences, a professor of geological sciences and mechanical engineering, and director of the Center for Computational Geosciences at The University of Texas at Austin. Previously, he was a professor at Carnegie Mellon University for 16 years. Dr. Ghattas earned a B.S. in civil engineering and M.S. and Ph.D. degrees in computational mechanics, all from Duke University. He has research interests in simulation and modeling of complex geophysical, mechanical, and biological systems on supercomputers, with specific interest in inverse problems and associated uncertainty quantification for large-scale systems. He served on several advisory groups on these topics, including the NRC Committee on Mathematical Foundations of Validation, Verification, and Uncertainty Quantification and the National Science Foundation Advisory Committee for Cyberinfrastructure Task Force on Grand Challenges. Dr. Ghattas is a recipient of the 2003 IEEE/Association for Computing Machinery Gordon Bell Prize for Special Accomplishment in Supercomputing, and is a fellow of the Society for Industrial and Applied Mathematics.

James A. Hansen is the head of the Meteorological Applications Development Branch in the Marine Meteorology Division of the Naval Research Laboratory. Previously, he was an associate professor in the Earth, Atmospheric, and Planetary Sciences Department at the Massachusetts Institute of Technology. Dr. Hansen has a B.S. and an M.S. in aerospace engineering from the University of Colorado. He received a Rhodes Scholarship and obtained his Ph.D. in atmospheric, oceanic, and planetary physics from Oxford University. His research interests focus on the estimation of environmental forecast uncertainty and the use of that uncertainty in decision making. One of his recent projects was the development of the Pirate Attack Risk Surface product, which uses intelligence, meteorological, oceanographic, and adversarial behavioral information to estimate and communicate the risk of pirate attack. Dr. Hansen served on the NRC Committee on Estimating and Communicating Uncertainty in Weather and Climate Forecasts.

Anna M. Michalak is a faculty member in the Department of Global Ecology at the Carnegie Institution for Science. Previously, she was an associate professor at the University of Michigan. Dr. Michalak holds a B.S. in environmental engineering from the University of Guelph, Ontario, and an M.S. and a Ph.D. in civil and environmental engineering from Stanford University. Her research interests focus on characterizing complexity and quantifying uncertainty in environmental systems with the goal of improving our understanding of these systems

and our ability to forecast their variability. Application areas include estimating greenhouse gas emissions and sequestration, understanding linkages between climate variability and water quality, and characterizing the Earth system. A common theme of her research is the development and application of statistical and geostatistical data fusion methods for optimizing the use of limited in situ and remote sensing environmental data. Dr. Michalak has served on several scientific committees and organized workshops on simulating complex systems, and on observations and models for inferring greenhouse gas emissions. She is a recipient of the Presidential Early Career Award for Scientists and Engineers.

Shashi Shekhar is the McKnight Distinguished University Professor in the Department of Computer Science at the University of Minnesota. He holds a B.Tech. in computer science from the Indian Institute of Technology in Kanpur, India, and an M.S. and a Ph.D. in computer science from the University of California, Berkeley. Dr. Shekhar pioneered the research area of spatial data mining via pattern families (e.g., collocation and spatial outliers) and also has research interests in spatial databases. He co-authored a textbook on spatial databases and received IEEE’s Technical Achievement Award for contributions to spatial databases, data mining, and geographic information systems. Dr. Shekhar has served on two NRC committees concerned with geospatial intelligence—the Committee on Basic and Applied Research Priorities in Geospatial Science for the National Geospatial-Intelligence Agency and the Committee on Future Workforce for Geospatial Intelligence. He is also a member of the Computing Community Consortium Council and chaired its spatial computing workshop in 2014. He is a fellow of the American Association for the Advancement of Science and of IEEE.

Shaowen Wang is a professor of geography and geographic information science and a faculty affiliate in the Department of Computer Science, Department of Urban and Regional Planning, and School of Information Sciences at the University of Illinois at Urbana-Champaign. He is also associate director for cyberGIS at the National Center for Supercomputing Applications, and founding director of the university’s CyberGIS Center for Advanced Digital and Spatial Studies. Dr. Wang received a B.S. in computer engineering from Tianjin University, an M.S. in geography from Peking University, and an M.S. of computer science and a Ph.D. in geography from the University of Iowa. His research interests include geographic information science and systems, advanced cyberinfrastructure and cyberGIS, complex environmental and geospatial problems, computational and data sciences, high-performance parallel and distributed computing, and spatial analysis and modeling. He is president of the University Consortium for Geographic Information Science, and served on its board of directors from 2009 to 2012. He also serves on the advisory board of the National Science Foundation’s Extreme Science and Engineering Discovery Environment program. He was an NCSA Fellow in 2007, and received the NSF CAREER Award in 2009.