Daniel Kifer
Department of Computer Science and Engineering

Daniel Kifer is the director of CMLA and head of the undergraduate deep learning lab (UDLL). He received his Ph.D. degree from Cornell University in 2006. He joined the Department of Computer Science at Penn State in Fall 2008. His research interests include statistical privacy, computational social science, deep learning applications to the physical sciences, and deep learning training algorithms.  Professor Kifer has won the influential paper award at the 2017 IEEE International Conference on Data Engineering and is collaborating with the U.S. Census Bureau to develop practical applications of differential privacy. He is also currently serving on the Committee on National Statistics (CNSTAT).

Mehrdad Mahdavi
Department of Computer Science and Engineering

Mehrdad Mahdavi joined the Department of Computer Science and Engineering in 2018. Before joining PSU, he was a Research Assistant Professor at Toyota Technological Institute, at University of Chicago for two years. He has also worked at Voleon Group as a Member of Research Staff, and Microsoft Research and NEC Laboratories America. He obtained his P.h.D from Michigan State University, M.Sc in Computer Engineering department from Sharif University of Technology. He has won the Top Cited Paper Award from the journal of Applied Mathematics and Computation (Elsevier) in 2010 and the Mark Fulk Best Student Paper Award at the Conference on Learning Theory (COLT) in 2012. He is broadly interested in Computational and Statistical Machine Learning, and design and analysis of Randomized Algorithms with a focus on on large-scale machine learning, online learning, convex and non-convex optimization, and sequential and statistical Learning Theory.

Zhenhui (Jessie) Li
College of Information Sciences and Technology

Dr. Zhenhui (Jessie) Li is an associate professor of Information Sciences and Technology at the Pennsylvania State University. She is Haile family early career endowed professor. Prior to joining Penn State, she received her PhD degree in Computer Science from University of Illinois Urbana-Champaign in 2012, where she was a member of data mining research group.

Her research has been focused on mining spatial-temporal data with applications in transportation, ecology, environment, social science, and urban computing. She is a passionate interdisciplinary researcher and has been actively collaborating with cross-domain researchers. She has received NSF CAREER award, junior faculty excellence in research, and George J. McMurtry junior faculty excellence in teaching and learning award.

C. Lee Giles
College of Information Sciences and Technology

Dr. C. Lee Giles is the David Reese Professor at the College of Information Sciences and Technology at the Pennsylvania State University, University Park, PA. He is also Professor of Computer Science and Engineering, Professor of Supply Chain and Information Systems, and Director of the Intelligent Systems Research Laboratory. He directs the CiteSeerx project and codirects the ChemxSeer project at Penn State. He has been associated with Columbia University, the University of Maryland, University of Pennsylvania, Princeton University, the University of Pisa and the University of Trento.

David Miller
Department of Electrical Engineering

Prior to joining Penn State in 1995, Dr. Miller he was employed by General Atronics Corporation in Wyndmoor, Pa. Dr. Miller’s research interests include source and channel coding, image compression, statistical pattern recognition, and neural networks. Dr. Miller received the National Science Foundation Career Award in 1996, for the continuation of his research on learning algorithms for neural networks.

Jia Li
Department of Statistics

Jia Li is a Professor of Statistics at the Pennsylvania State University. Her research interests include statistical learning, data mining, image processing, retrieval/annotation, and composition analysis. She also worked as a Program Director at the National Science Foundation from 2011 to 2013, a Visiting Scientist at Google Labs in Pittsburgh from 2007 to 2008, a researcher at the Xerox Palo Alto Research Center from 1999 to 2000, and a Research Associate in the Computer Science Department at Stanford University in 1999. She received the M.Sc. degree in Electrical Engineering (1995), the M.Sc. degree in Statistics (1998), and the Ph.D. degree in Electrical Engineering (1999), all from Stanford University.

Paul Medvedev
Department of Computer Science and Engineering
Department of Biochemistry & Molecular Biology

Paul Medvedev is an Associate Professor in the Department of Computer Science and Engineering and the Department of Biochemistry and Molecular Biology and the Director of the Center for Computational Biology and Bioinformatics at the Pennsylvania State University. His research focus is on developing computer science techniques for analysis of biological data and on answering fundamental biological questions using such methods. Prior to joining Penn State in 2012, he was a postdoc at the University of California, San Diego and a visiting scholar at the Oregon Health & Sciences University and the University of Bielefeld. He received his Ph.D. from the University of Toronto in 2010, his M.Sc. from the University of Southern Denmark in 2004, and his B.S. from the University of California, Los Angeles in 2002.

David R. Hunter
Department of Statistics

David R. Hunter earned his Ph.D. in statistics from the University of Michigan in 1999, following a math degree from Princeton university in 1992 and two years teaching mathematics at a public high school in New Hampshire. He was assistant professor from 1999 to 2005 and associate professor from 2005 to 2012, both at Penn State University. He is a fellow of the American Statistical Association.

He has published widely on statistical models for networks and is a co-creator of the “statnet” suite of packages for network analysis in R. He co-coined the term “MM algorithms” and has written extensively on this and other EM-like algorithms. He has also extended the theory and computational practice of unsupervised clustering using nonparametric finite mixture models.

Aleksandra B. Slavković
Department of Statistics

The greatest beauty and value of statistics stem from its role in collaborative crossdisciplinary research. Dr. Slavkovic’s primary research interest is in the area of data privacy and confidentiality, focusing on statistical disclosure limitation, statistical utility and their interplay with differential privacy, in the context of small and large scale surveys, health and genomic data, and network data. Other related past and current research interests include evaluation methods for human performance in virtual environments, statistical data mining, application of statistics to social sciences, algebraic statistics, and causal inference. Slavkovic is a professor of Statistics, with appointments at the Department of Statistics and the Institute for CyberScience at Penn State University, University Park, and at the Department of Public Health Sciences, Pennsylvania State College of Medicine, Hershey. She received her Ph.D. from Carnegie Mellon University in 2004. She is currently serving as an Associate Editor of the Annals of Applied Statistics and Journal of Privacy and Confidentiality. She is a fellow of the American Statistical Association and an Elected member of the International Statistical Institute, and served on a number of National Academy of Sciences/National Research Council committees.

Matthew Reimherr
Department of Statistics

Dr. Reimherr received is PhD in Statistics from the University of Chicago in 2013, as well as an MS in Statistics and a BS in Mathematics from the University of Utah. His research is concerned with the statistical analysis of complex objects such as curves, surfaces, and images. He is especially interested in large human health applications that include irregular longitudinal data, genomic information, electronic health records, and/or biomedical images.

Bharath Sriperumbudur
Department of Statistics

Bharath Sriperumbudur is an Assistant Professor in the Department of Statistics at Pennsylvania State University. His research has focused on non-parametric methods in statistics and machine learning, particularly involving reproducing kernels. He received his Ph. D. in Electrical Engineering in 2010 from the Department of Electrical & Computer Engineering at the University of California, San Diego. Prior to coming to Penn State, he was a research fellow in the Statistical Laboratory, Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge and was a postdoctoral research associate at Gatsby Computational Neuroscience Unit, University College London. He serves on the editorial board of Journal of Machine Learning Research and has served as an area chair for NIPS, ICML and AISTATS

Rebecca J. Passonneau
Department of Computer Science and Engineering

Rebecca Passonneau is currently working on application of NLP techniques to education, such as automated content analysis of students’ summaries for their understanding of a text or curriculum; on NLP and knowledge exploration, such as analysis of whether a knowledge source is relevant to an input natural language question; and novel environments to collect textual dialog data. To address these and other questions directed at computational models of language use, Prof. Passonneau collaborates with faculty and students in many departments.

David Reitter
College of Information Sciences and Technology

David Reitter is an Associate Professor of Information Sciences and Technology. His research group studies computational approaches to understanding human cognition, with a focus on how people engage in natural-language dialogue, how they process sentences, and how memory encodes the linguistic knowledge that facilitates this. Professor Reitter uses cognitive and computational modeling methodologies ranging from cognitive architectures to deep neural representations based on large-scale data. Recent interests related to machine learning include language models and high-dimensional representations of semantics. David Reitter’s work is funded by the National Science Foundation and published in both the natural-language processing and cognitive science communities. Reitter holds a Ph.D. from the University of Edinburgh, was a research fellow at MIT Media Lab Europe and did his post-doc at Carnegie Mellon University before joining the Penn State faculty in 2012.

Chaopeng Shen
Department of Civil and Environmental Engineering

Chaopeng Shen joined the Department of Civil and Environmental Engineering in 2012. Before joining PSU, he was a postdoctoral researcher at Lawrence Berkeley National Laboratory. He obtained his Ph.D. from Michigan State University in hydrologic modeling. His research utilizes both process-based hydrologic modeling and hydrologic deep learning. Shen’s group, in collaboration with Daniel Kifer, is one of the first to bring big data time series deep learning into hydrology. He currently focuses on model-data integration, deep learning for hydrologic modeling, and extracting knowledge from large environmental data, including station-based measurements and remotely-sensed data.