In March 2017, University Provost Jean Morrison announced that Boston University would further enhance its data science infrastructure through the creation of a Data Science Faculty Fellows program.

An integral part of the Data Science Initiative, the Faculty Fellows program brings together uniquely talented faculty whose expertise transcends traditional boundaries, leveraging the field’s three methodological disciplines of Computer Science, Mathematics & Statistics, and Electrical & Computer Engineering to enable fundamental advances across the academic landscape.

Current Data Science Faculty Fellows

Margrit Betke
Professor of Computer Science, College of Arts & Sciences

Professor Betke has been a member of BU’s Computer Science faculty since 2000 and co-leads the Image and Video Computing Research Group in her department and the AI Research Initiative at the Hariri Institute for Computing. Her current work applies machine learning and computer vision to such areas as medical imaging, interfaces for people with disabilities, assessing home-based physical therapy, quantifying political bias in the news, and analyzing online product availability and pricing in relation to world events. A senior member of the Association for Computing Machinery and the Institute of Electrical and Electronics Engineers (IEEE), she has published extensively in premier journals and is supported by several major grants from the National Science Foundation and Google. She holds a PhD and MS in electrical engineering and computer science from Massachusetts Institute of Technology.


John Byers
Professor of Computer Science, College of Arts & Sciences

Professor Byers has been a member of BU’s Computer Science faculty since 1999 and a Hariri Institute Fellow since 2012. His academic research is data-driven and centers on two disciplines: the science of computer networking and the empirical study of online platforms, notably Airbnb, Groupon, and Yelp. His academic honors include an NSF CAREER Award and two test-of-time paper awards at ACM SIGCOMM and IEEE ICDE. Since 2005, he is also the founding Chief Scientist at Cambridge-based Cogo Labs, an incubator and startup accelerator leveraging world-class proprietary technology in digital marketing. Professor Byers holds a Ph.D. in Computer Science from University of California, Berkeley and a B.A. in Computer Science, Economics, and Mathematics from Cornell University.


Christine Cheng
Assistant Professor of Biology, College of Arts & Sciences

A member of the BU faculty since 2016, Christine Cheng integrates cell and molecular biology with computational approaches to help understand and identify potential therapeutic targets to diseases including Alzheimer’s disease, HIV infection, and opioid addiction. Her current projects are focused on massively-parallel single-cell transcriptomic and epigenetic profiling. Christine has recently received significant grant support from NIH. She is a past recipient of the NIH’s Ruth L. Kirschstein National Research Fellowship and has published numerous articles in top journals including Nature Genetics, Nature Communications, Science Signaling, and Cell Systems. She is a graduate of National Taiwan University and holds a PhD in bioinformatics and systems biology from University of California, San Diego and an MS in computer science from Stanford University.


Mark Crovella
Professor of Computer Science, College of Arts & Sciences

A member of BU’s Computer Science faculty since 1994 and past department chair, Mark Crovella works to improve the understanding, design, and performance of parallel and networked computer systems – mainly through the application of data mining, statistics, and performance evaluation. His recent work has focused on the analysis of social and biological networks with applications to recommender systems, public opinion analytics, and computational biology. He is an elected fellow of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE), holds 10 patents derived from his research, and has published a book and more than 200 widely-cited papers on networking and computer systems. He holds a doctorate from the University of Rochester, a master’s degree from the State University of New York at Buffalo, and a bachelor’s degree from Cornell University.


Ahmed Ghappour
Associate Professor of Law, School of Law

Professor Ghappour has been a member of the BU faculty since 2017. His cross-disciplinary research focuses on the interplay between emerging technologies and law enforcement, particularly in the context of the modern surveillance state and cybersecurity. Prior to his legal career, Professor Ghappour was a computer engineer focused on design automation, diagnostics, distributed systems architecture and high performance computing. He holds a law degree from New York University School of Law and a B.S. in Electrical & Computer Engineering from Rutgers University.


Jonathan Huggins
Assistant Professor of Mathematics & Statistics, College of Arts & Sciences

Jonathan Huggins will formally join the Mathematics & Statistics faculty in January 2020 from Harvard University, where he has been a postdoctoral fellow in biostatistics. Jonathan’s research centers on the development of fast, trustworthy machine learning and AI methods that balance the need for computational efficiency and the desire for statistical optimality with the inherent imperfections that come from real-world problems, large datasets, and complex models. In addition to affiliations with Dana-Farber Cancer Institute and the Broad Institute of MIT and Harvard, he has published over a dozen papers in leading statistics and machine learning journals and conference proceedings. He received his doctorate and master’s degree in computer science from MIT and his bachelor’s degree in mathematics from Columbia University.


Eric Kolaczyk
Professor of Mathematics & Statistics, College of Arts & Sciences

Eric Kolaczyk has been a member of the Mathematics & Statistics faculty since 1998 and is director of the department’s Program in Statistics. He is an internationally recognized leader in statistics at its interface with multi-scale and network analysis, whose applied work has had broad implications in areas including bioinformatics, computational neuroscience, computer network traffic analysis, and social work. The author of three books and dozens of widely cited journal articles, he is a senior member of IEEE and an elected fellow of the American Association for the Advancement of Science, the Institute of Mathematical Statistics, the American Statistical Association, and the International Statistical Institute. He is a graduate of The University of Chicago and holds a PhD and MS in statistics from Stanford University.


Elaine Nsoesie
Assistant Professor of Global Health, School of Public Health

Dr. Nsoesie applies data science methodologies to global health problems, particularly in the realm of surveillance of chronic and infectious diseases. Her previous research has centered on the modeling of infectious diseases, using statistical and computational approaches to better understand the spread of disease and improve public health practice. She is currently focused on machine learning frameworks for monitoring risk factors for chronic diseases using social media data and on quantifying and addressing bias in digital data used in public health research. She has published extensively in premier health, technology, and informatics journals and received significant grant support from NIH and the Robert Wood Johnson Foundation. She is a graduate of University of Maryland College Park and holds a PhD in genetics, bioinformatics, and computational biology and an MS in statistics, both from Virginia Tech.


Francesco Orabona
Assistant Professor of Electrical & Computer Engineering, College of Engineering

Francesco Orabona joined the Department of Electrical & Computer Engineering in 2018, where he leads the Optimization and Machine Learning Lab. His research bridges the mathematical foundations of learning theory and data science, with applications to scientific, societal, and real-world engineering problems. It has led to the development of autonomous online learning algorithms that require minimal human supervision – first-of-its-kind work that is now part of Microsoft’s Machine Learning toolkit. The author of more than 60 peer-reviewed articles, he is a graduate of University of Naples (Italy), where he additionally earned his MS in electrical engineering. He holds a PhD in electrical engineering from University of Genoa.


Yannis Paschalidis
Professor of Electrical & Computer Engineering, College of Engineering

Yannis Paschalidis has been a College of Engineering faculty member since 1996 and is director of the BU-based Center for Information and Systems Engineering. An internationally-recognized leader in systems and control, networks, decision theory, optimization, and operations research, he is currently developing predictive analytics with applications to a number of areas with significant impact on society, including computational biology, digital health, smart cities, and transportation systems. He is a fellow of IEEE, the founding editor-in-chief of the IEEE Transactions on Control of Network Systems, and has published extensively in top scientific and engineering journals. A graduate of National Technical University of Athens (Greece), he holds a PhD and MS in electrical engineering and computer science from the Massachusetts Institute of Technology. In addition to Electrical & Computer Engineering, he holds affiliated appointments with Systems Engineering and Biomedical Engineering.


Kate Saenko
Associate Professor of Computer Science, College of Arts & Sciences

Kate Saenko has been a member of the Computer Science faculty since 2016 and is director of BU’s Computer Vision and Learning Group, where she specializes in machine learning, concentrating on the development of new systems to enhance vision and language understanding. The recipient of several active federal grants supporting her research into artificial intelligence, she has published extensively in leading computer science journals, serves as program chair for the 2020 Conference on Computer Vision and Pattern Recognition, and in 2017 received the Most Innovative Solution award, alongside her students, in the IEEE Large-Scale Activity Recognition Challenge. She holds a doctorate and master’s degree in electrical engineering and computer science from MIT and a bachelor’s degree in computer science from University of British Columbia.


Venkatesh Saligrama
Professor of Electrical & Computer Engineering and Systems Engineering, College of Engineering

Venkatesh Saligrama joined the Department of Electrical & Computer Engineering in 2001, where he explores problems arising in machine learning, statistical signal processing, and control and information theory. His recent research has focused on machine learning under budget constraints, identifying racial and gender bias in AI systems, and developing new tools for video analytics in highly cluttered and urban scenarios. An elected fellow of the IEEE, he is a past recipient of the Presidential Early Career Award, NSF CAREER Award, and Office of Naval Research Young Investigator Award. He has edited a book, published dozens of widely-cited articles and papers in leading journals and conference proceedings, and is incoming chair of the Big Data Special Interest Group of the IEEE Signal Processing Society. He received his doctorate and master’s degree from MIT and his bachelor’s degree from Indian Institute of Technology in Madras.


Adam Smith
Professor of Computer Science, College of Arts & Sciences

Professor Smith arrived at BU in 2017 as the first faculty member recruited through the Data Science Initiative. He has earned international recognition for founding a new field within data science that puts on solid bases the modeling and evaluation of the privacy of individuals in the age of big data. Over the last few years, his research sponsors have included NSF, NIH, the U.S. Census Bureau, the U.S. Army Research Laboratory, Google, and the Sloan Foundation. He is a past recipient of an NSF CAREER Award, a U.S. Presidential Early Career Award for Scientists and Engineers, and the Gödel Prize for writings on theoretical computer science. Professor Smith holds a doctorate and a master’s degree in computer science from the Massachusetts Institute of Technology and a bachelor’s degree in mathematics and computer science from McGill University. In addition to Computer Science, he holds a secondary appointment as Professor of Electrical & Computer Engineering, and an affiliated appointment as Professor of Mathematics & Statistics.