Our Team
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Ton Dieker is an expert in random processes and computer simulation algorithms, and he develops tools so that data for one system can be used to make predictions about modified systems for which no data is available. Such tools are useful to regulators in predicting how well banks respond to financial shocks, to scientists in predicting future climate under various carbon-emissions rates, and to engineers in predicting how factory layouts will boost performance.
Of particular interest to Dieker are tractable approximations of performance metrics in stochastic networks that can be used to quickly explore initial system designs, to reduce computational burdens associated with simulation, or even to eliminate the need for simulation altogether. Such approximations have the potential to improve operational efficiencies in hospitals, among other applications.
Dieker received an MSc in Operations Research from the Vrije Universiteit Amsterdam in 2002 and a PhD degree in Mathematics from the University of Amsterdam in 2006.
Shih-Fu Chang is Dean of Columbia Engineering and Morris A. and Alma Schapiro Professor. His research is focused on computer vision, machine learning, and multimedia information retrieval. His work on content-based visual search in the early 90’s set the foundation of this vibrant area. Identified as the most influential researcher in the field of Multimedia in 2016, he has made research innovations that have been used in systems and products for image/video search engines, online crime prevention, mobile search, and brain machine interfaces.
For his long-term contributions, he has been awarded the ACM Multimedia SIG Technical Achievement Award, the IEEE Signal Processing Society Technical Achievement Award, the Honorary Doctorate from the University of Amsterdam, and the IEEE Kiyo Tomiyasu Award. He received the Great Teacher Award from the Society of Columbia Graduates. He served as Chair of Columbia Electrical Engineering Department (2007-2010), the Editor-in-Chief of the IEEE Signal Processing Magazine (2006-8), and founder/advisor for several companies.
In his current capacity as Senior Executive Vice Dean of Columbia Engineering, he plays a key role in the School’s strategic planning, major research initiatives, international collaboration, and faculty development. He is a Fellow of the American Association for the Advancement of Science (AAAS), ACM, and IEEE, and an elected Academician of Academia Sinica.
Ryan P. Abernathey is an Associate Professor of Earth And Environmental Science at Columbia University and Lamont Doherty Earth Observatory. He received his Ph.D. from MIT in 2012 and a B.A. from Middlebury College. He joined Columbia in 2013 after a postdoc at Scripps Institution of Oceanography. Ryan is a physical oceanographer who studies the large-scale ocean circulation and its relationship with Earth‚Äôs climate. A central theme is how ocean “mesoscale” turbulence, i.e. eddies, waves, and jets on scales of tens to hundreds of kilometers, contributes to the transport of momentum, heat, and geochemically relevant tracers. Regionally, his main focus is the Southern Ocean, which surrounds Antarctica and links the three main ocean basis. High-resolution numerical modeling and satellite remote sensing are key tools in this research, which has led to an interest in high performance computing and big data.
In Feb. 2016, Prof. Abernathey was awarded an Alfred P. Sloan Research Fellowship in Ocean Sciences and an NSF CAREER award for a project entitled “Evolution of Mesoscale Turbulence in a Changing Climate.” He received a NASA New Investigator Award in 2013. Together with Prof. Tony Jebara (Computer Science) and Dr. Joaquim Goes (LDEO), Abernathey also recently received a Columbia Research Initiatives for Science and Engineering (RISE) grant to apply machine learning techniques to ocean satellite observations. He is an active participant in and advocate for open source software, open data, and reproducible science.
Ivan Corwin is a professor of mathematics, a member of the Irving Center for Cancer Dynamics, Probability and Society Initiative, Program for Mathematical Genomics, Quantum Initiative, and a member of the executive committee for the Data Science Institute. He is also on the scientific advisory board for the NSF-funded Institute for Computations and Experimental Research in Mathematics (ICERM) and Mathematical Science Research Institute (MSRI).
Ivan studies aspects of probability and mathematical physics including random interface growth, interacting particle systems, random matrix theory and stochastic partial differential equations. He received his PhD from the Courant Institute in 2011 and has since held positions at Microsoft Research, MIT, Institut Henri Poincare (at the Poincare Chair), U.C. Berkeley (as a Visiting Miller Professor), and Columbia.
He has held a Clay Research Fellowship, a Packard Fellowship, and Simons Fellowship, a Schramm Fellowship and is a Fellow of the American Mathematical Society and of the Institute of Mathematical Statistics. He was the recipient of the 2021 Loeve prize in probability, 2018 Alexanderson Award, 2014 Rollo Davidson Prize, 2012 Young Scientist Prize of the I UPAP, and gave an invited lecture at the 2014 International Congress of Mathematicians.
Garud Iyengar is the Avanessians Director of the Data Science Institute (DSI) and a Professor of Industrial Engineering and Operations Research at Columbia Engineering. As the Avanessians Director of DSI, he leads education and research initiatives for Columbia’s central hub of data science scholarship with more than 400 affiliated faculty. He also co-leads the University’s Artificial Intelligence Initiative in partnership with Jeannette Wing, the Executive Vice President for Research, and Shih-Fu Chang, Dean of The Fu Foundation School of Engineering and Applied Science.
His research interests are broadly in the areas of control, machine learning, and optimization. His current projects focus on the areas of large-scale power systems and supply chains, causal inference, large scale game solving, and modeling of cellular processes. His research has been funded by the National Science Foundation, Office of Naval Research, and the Department of Energy, among others. He is the author of over 90 publications and chapters, holds two patents, was an Amazon Scholar from 2019-2024, and was elected an INFORMS Fellow in 2018.
Iyengar, who has been on the faculty since 1998, has held a range of academic leadership roles. He was Columbia Engineering’s Senior Vice for Dean of Research and Academic Programs from 2021-2024, and Chair of the Industrial Engineering and Operations Research department from 2013-2019. At DSI, where he has been deeply involved since the institute’s 2012 founding, he was Associate Director for Research from 2017-19 and also played a critical role in shaping important programs like the DSI Seed Funds Initiative and the Postdoctoral Scholars Program.
Iyengar received a BTech in electrical engineering from the Indian Institute of Technology in 1993 and a PhD in electrical engineering from Stanford University in 1998.
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Despina Kontos, PhD, is a computer scientist with expertise in artificial intelligence, machine learning, and big data analytics for multi-modality imaging data. She is a professor of radiology and vice chair of artificial intelligence and data science research in the Department of Radiology at Columbia University Irving Medical Center (CUIMC) and director of biomarker imaging at NewYork-Presbyterian Hospital—with additional appointments in the Departments of Biomedical Informatics and Biomedical Engineering. She is also the founding director of Columbia University’s Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID), a multidisciplinary center dedicated to developing and integrating quantitative imaging and non-imaging biomarkers for personalized disease prediction, particularly in cancer.
Dr. Kontos has made seminal contributions to the leveraging of artificial intelligence tools for risk prediction in breast and lung cancer. Her research program focuses on investigating the role of imaging as a quantitative biomarker for improving cancer screening, prognostication, and treatment. She has developed innovative computational methodologies that have enabled the investigation of novel cancer phenotypic biomarkers via imaging, and has translated these biomarkers through extensive clinical and epidemiologic studies to answer important research questions for personalizing cancer care.
With a primary focus on breast cancer, her work has contributed to a fundamental transition in the interpretation of breast cancer images, by showing that imaging data can be mined to extract sophisticated phenotypic signatures with independent diagnostic, prognostic, and predictive value. Her lab is also pursuing related research in lung cancers, specifically on evaluating the integration of CT radiomic features with liquid biopsy data to characterize lung tumor heterogeneity for predicting response to targeted therapy and immunotherapy.
Dr. Kontos is the recipient of numerous grants from both federal agencies and private foundations, including the National Institutes of Health (NIH), the Department of Defense (DOD), the American Cancer Society (ACS), and the Radiological Society of North America (RSNA). She is the author of more than 100 publications in high-impact journals, and her lectures have gained national recognition.
Dr. Kontos studied engineering as an undergraduate at the University of Patras in Greece. She received her PhD in computer and information sciences from Temple University in Philadelphia, followed by postdoc training in radiology at the University of Pennsylvania. She has certificates in Biostatistics and Epidemiology from the University of Pennsylvania; Cancer Biology and Targeted Therapeutics from Harvard University; and AI for Decision Making: Business Strategies and Applications from the Wharton School of Business. Dr. Kontos is a member of the RSNA Research & Education Foundation Fund Development Committee.
David Blei joined Columbia in Fall 2014 as a Professor of Computer Science and Statistics. His research involves probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference. He works on a variety of applications, including text, images, music, social networks, user behavior, and scientific data.
Professor Blei earned his Bachelor’s degree in Computer Science and Mathematics from Brown University (1997) and his PhD in Computer Science from the University of California, Berkeley (2004). Before arriving to Columbia, he was an Associate Professor of Computer Science at Princeton University. He has received several awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), and Blavatnik Faculty Award (2013).
Alessandra Casella is Professor of Economics and Professor of Political Science at Columbia University, co-director of Columbia’s Institute for Social and Economic Research and Policy, and fellow of the National Bureau of Economic Research (Cambridge, Ma), and the Center for Economic Policy Research (London, UK). She is a graduate of Universita’ Bocconi and received her PhD in Economics from MIT. She has taught at UC Berkeley and held a position as Directeur d’ Etudes at the Ecole des Hautes Etudes in Sciences Sociales (EHESS) (Paris and Marseilles).
She founded the Columbia Experimental Laboratory for the Social Sciences, which she directed from 2012 to 2022. Casella is a fellow of the Econometric Society and of the Society for the Advancement of Economic Theory, has been a Guggenheim fellow, a member of the Institute of Advanced Studies in Princeton, a Russell Sage fellow and a Straus fellow at the NYU Law School. Since 2016, she is on the board of editors of the American Economic Review.
Casella is a graduate of Universita’ Bocconi and received her PhD in Economics from MIT. She has taught at UC Berkeley and held a position as Directeur d’ Etudes at the Ecole des Hautes Etudes in Sciences Sociales (EHESS) (Paris and Marseilles).
Casella’s recent work is in political economy and experimental economics.