MASTER OF SCIENCE IN 
DATA SCIENCE

John Springer

John Springer is a Professor in the Department of Computer and Information Technology (CIT) at Purdue University. His main research area is Data Science with recent emphasis on data-intensive applications in Cybersecurity, and he serves as the leader of the Purdue DATA Laboratory. Dr. Springer currently serves in a variety of leadership roles at Purdue; these include as the co-chair of the CIT Undergraduate Curriculum Committee and the co-chair of Purdue’s Integrative Data Science Initiative (IDSI) Curriculum Committee.

Chris Brinton

Chris Brinton is the Elmore Rising Star Associate Professor of Electrical and Computer Engineering (ECE) at Purdue University, where he leads the ION research lab. ION conducts research at the intersection of networking, communications, and machine learning. Contemporary network architectures we investigate include Fog computing systems, the Internet of Things (IoT), NextG Wireless, and social learning networks (SLN), and our foundational techniques include convex and non-convex optimization, machine learning, and signal processing. Since joining Purdue, he has received several external early career research awards, including NSF’s CAREER Award, ONR’s YIP (Young Investigator Program) Award, DARPA’s YFA (Young Faculty Award), AFOSR’s YIP Award, and Intel’s RSA (Rising Star Faculty Award).

Jen Tang

Professor Tang’s teaching interests are applied statistics, quantitative methods, and business analytics. His current research includes the areas of applied statistics, statistical quality control, and reliability analysis. Professor Tang also has considerable expertise in the distribution theory of multivariate statistical analysis. He was/is on the editorial boards of IIE Transactions and Production and Operation Management Journal. Prior to joining the Krannert faculty, Professor Tang was a member of technical staff at Bell Communications Research (Bellcore, 1984-91) and an assistant professor in the Department of Mathematics, Statistics, and Computer Science at Marquette University (1981-84). He received his Ph.D. in Mathematical Statistics from Bowling Green State University (1977-1981) and BS in Applied Math from Soochow University in Taipei (1969-1973).

Gang Shao

Gang Shao is an assistant professor of data science at Purdue Libraries and School of Information Studies. His research interests include data science education and information science using machine learning, Natural Language Processing (NLP), and computer vision. He is also a remote sensing scientist using optical imagery and lidar to understand land ecosystems and the changing climates.

Cherie Maestas

Cherie D. Maestas, is Professor and Head of the Department of Political Science at Purdue University. Previously she served as the Marshall A. Rauch Professor of Political Science and Director of the interdisciplinary Public Policy Program in the College of Liberal Arts and Sciences received her PhD from the University of Colorado, Boulder in 2000. Dr. Maestas received her PhD from the University of Colorado, Boulder in 2000. Her current research examines how emotions and personal characteristics (e.g. emotion regulation, personality traits, political predispositions) moderate the effects of media messages and policy framing.

Kyle Hultgren

Kyle Hultgren is the founding director of the Purdue University College of Pharmacy’s Center for Medication Safety Advancement. Dr. Hultgren is a co-author of a process improvement program in partnership with Purdue University and the United States Veterans Health Administration that has been provided to thousands of professionals in health systems nationwide. His current research includes extensive work on dashboards and measurement systems for evaluating and improving medication use systems and integration of publicly available social media data as potential surveillance signals. As a result of this work, he led his Center through the creation of SafeRx, a large, curated, relational database containing millions of adverse drug events.

Bryce Dietrich

Bryce Dietrich is an Associate Professor in the Department of Political Science and research scholar at the Center for C-SPAN Scholarship and Engagement (CCSE). His research uses novel quantitative, automated, and machine learning methods to analyze non-traditional data sources such as audio (or speech) data and video data. Professor Dietrich’s work has appeared in the Nature, American Political Science Review, Journal of Politics, Political Analysis, and Political Psychology. This work has also received grant support from major organizations, including the National Institutes of Health and the National Science Foundation. He received his Ph.D. from the University of Illinois and was recently a postdoctoral research fellow at Harvard’s Kennedy School and Northeastern University.

Jacob Hosen

Dr. Jake Hosen uses high frequency sensors to study how microbes and environmental change influence ecosystem functions and water quality. Before joining the Department of Forestry & Natural Resources, he was a postdoctoral associate at the University of Florida and Yale University. In his previous positions, Jake worked with a national team to study how microbial metabolism stabilizes water quality in river networks. Dr. Hosen received his B.Sc. from the College of William & Mary in Chemistry and Biology, and a Ph.D. in Behavior, Ecology, Evolution & Systematics from the University of Maryland. In his position at Purdue, Dr. Hosen will leverage internet of things technology to deploy environmental sensors networks that will provide real-time sensing for predictive ecosystem models.

Vinayak Rao

Vinayak Rao is an Assistant Professor at the department of Statistics at Purdue University. He completed his PhD at the Gatsby Unit at University College London. He then spent two years as a postdoc at the department of Statistical Science at Duke University before joining Purdue in 2014. Vinayak’s research interests lie in the areas of probabilistic machine learning, Bayesian statistics and modeling and computation for stochastic processes.