Elena N. Naumova is the Chair of the Division of Nutrition Data Science, as well as a Professor at the Friedman School at Tufts University. Her research activities span a broad range of research programs in emerging and re-emerging diseases, environmental epidemiology, molecular biology, nutrition, and growth. Her primary expertise is in development of analytical tools for spatio-temporal and longitudinal data analysis applied to disease surveillance, exposure assessment, and studies of growth; creation and application of statistical tools to evaluate the influence of an extreme and/or intermediate event on spatial and temporal patterns.
Stuart Kauffman, originally a medical doctor, is an emeritus professor of biochemistry at the University of Pennsylvania and a seminal member and an external professor of the Santa Fe Institute. Also a MacArthur Fellow and a Trotter Prize winner, Kauffman has published three major books; among them is At Home in the Universe: The Search for the Laws of Self-Organization and Complexity (1995). Kauffman was the director of the Institute for Biocomplexity and Informatics (IBI). He is the pioneer and the founding father of biocomplexity research. Kauffman himself got interested in gene regulatory networks at San Francisco medical school in 1964 when he wanted to unravel the mysteries of cell differentiation. Quickly noticing the possibilities of his revolutionary ideas, he started working on biocomplexity and was eventually able to show that the behavior of genetic networks depends critica lly on the level at which the genes are connected.
Esteban Moro is a visiting professor at the MIT Media Lab. He is also an associate professor at the Universidad Carlos III de Madrid (Spain) and a member of the Joint Institute UC3M-Santander on Financial Big Data. He serves as a consultant for many public and private institutions and has held previous positions at the University of Oxford, Institute of Knowledge Engineering (Spain), and Instituto Mixto de Ciencias Matemáticas (Spain). Moro earned his BSc in Physics from the University of Salamanca and a PhD in physics from the Universidad Carlos III de Madrid. He has published more than 50 articles, and has led and participated in over 20 projects funded by government agencies and/or private companies. His areas of interests are applied mathematics, financial mathematics, viral marketing, and social networks. He received the "Shared University Award" from IBM in 2007 for modeling the spread of information in social networks and application to viral marketing.
Simon DeDeo is an Assistant Professor in Social and Decision Sciences at Carnegie Mellon University, and External Professor at the Santa Fe Institute. He was previously affiliated with Complex Systems and the Cognitive Science Program at Indiana University. DeDeo received his PhD in Astrophysics from Princeton University. He has also held post-doctoral fellowships at the Institute for Physics and Mathematics of the Universe at the University of Tokyo and at the Kavli Institute for Cosmological Physics at the University of Chicago. At the Laboratory for Social Minds, he and his colleagues undertake empirical investigations, and build mathematical theories, of both historical and contemporary phenomena. They range from the centuries-long timescales of cultural evolution to the second-by-second emergence of social hierarchy in the non-human animals, from the editors of Wikipedia to the French Revolution to the gas stations of Indiana. They create synthetic, deep-time accounts of major transitions in political order, with the goal of the predicting and understanding our species’ future.
Kush R. Varshney is a research staff member and manager in IBM Research AI, working from the Thomas J. Watson Research Center, Yorktown Heights, New York. He co-directs the IBM Science for Social Good initiative. He is also a data ambassador with DataKind, New York, New York. While at MIT, he was a research assistant with the Stochastic Systems Group in the Laboratory for Information and Decision Systems and a National Science Foundation Graduate Research Fellow. He has been a visiting student at Laboratoire de Mathématiques Appliquées aux Systèmes at École Centrale, Paris, and an intern at the Systems and Decision Sciences Section, Lawrence Livermore National Laboratory, Livermore, California, at Sun Microsystems, Burlington, Massachusetts, and at Sensis Corporation, DeWitt, New York. His research interests include statistical signal processing, machine learning, data mining, and image processing.
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