The M.S. in Data Science program was co-founded by three Lehigh faculty with unique perspective and expertise in creating impact through the tools and methods of data science and analytics:

 

 

Brian D. Davison is director of Lehigh's undergraduate minor in data science and teaches courses on data science, web search engines, and data mining, among others. He heads the Web Understanding, Modeling, and Evaluation (WUME) laboratory, and serves as editor-in-chief of the Association for Computing Machinery (ACM) journal Transactions on the Web. He spent his most recent sabbatical with the Core Data Science group at Facebook. His research includes search, mining, recommendation and classification problems in text, on datasets, on the Web and in social networks. Davison is an NSF Faculty Early CAREER award winner and one of twelve Microsoft Live Labs "Accelerating Search" award recipients. Dr. Davison's research has been supported by the National Science Foundation, the Defense Advanced Research Projects Agency, Microsoft, Amazon, and Sun Microsystems. As a graduate student, he led development in the Rutgers DiscoWeb search engine project which was later spun out as an internet startup called Teoma (and was subsequently purchased by Ask Jeeves.)


Daniel Robinson earned his Ph.D. in Mathematics from the University of California, San Diego. He previously served as a Postdoctoral Researcher at Oxford University, a Postdoctoral Researcher and Visiting Professor at Northwestern University, and most recently as an Assistant Professor at Johns Hopkins University. Daniel works at the intersection of Mathematical Optimization and Data Science. He designs, analyzes, and implements algorithms for solving continuous optimization problems arising in applications for healthcare and computer vision. His articles have appeared in top applied mathematics journals, such as Mathematical Programming and SIAM Journal on Optimization, as well as in top data science conference proceedings, such as the International Conference on Machine Learning and IEEE Conference on Computer Vision and Pattern Recognition. He is an Associate Editor for the reputed journals Computational Optimization and Applications and Optimization Methods and Software. Daniel was a founding member of Johns Hopkins' Mathematical Institute for Data Science (MINDS) and helped to establish JHU's Master of Science in Data Science program. He has (twice) received the Professor Joel Dean Award for Excellence in Teaching, and has been awarded numerous grants from the National Science Foundation and other funding agencies.


Parv Venkitasubramaniam's research interests are in developing theoretical foundations for privacy in networks. Specifically, he applies tools in statistical signal processing, information theory and game-theory to study fundamental trade-offs between privacy and performance in networks under different adversarial conditions. He is also interested in studying anonymity in a more general category of networked systems such as peer production, smart grid networks and transportation systems. His doctoral research was in the area of wireless sensor networks, specifically on distributed multiple access communication and statistical inference in large networks.


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