Meet the ICDS Team

Faculty Council

The ICDS Faculty Council provides guidance on the strategic directions of ICDS, establishing an overall vision for ICDS research. Specific focus areas include:

  • Providing intellectual strategic direction of ICDS by identifying and prioritizing research areas to elevate.
  • Recommending strategic investments to advance these research directions by building upon existing Penn State research strengths and advancing research methods and cyber-ecosystems as they relate to computation and data science, aligning with the ICDS mission and vision.
  • Developing, or supporting the development of, white papers and proposals directed at Penn State research administration and/or external sponsors to advance and build awareness of these research priorities.
Guido Cervone


Guido Cervone

  • Professor of Geography and Meteorology and Atmospheric Science
  • Associate Director, Institute for Computational and Data Sciences
  • ICDS Co-Hire

Department: Geography

Office: 205 Walker Building, University Park


Phone: 814-863-0179

Research Focuses: Remote sensing, environmental hazards, geoinformatics, social media

Website: Visit Guido's Website

Biography: Dr. Guido Cervone is professor of geography, meteorology and atmospheric science, an associate director for the Institute for Computational and Data Sciences, and a faculty affiliate of the Earth and Environmental Systems Institute. His background is in computational science and remote sensing, and his research focuses on the development and application of computational algorithms for the analysis of spatio-temporal remote sensing; numerical modeling; and social media big data related to environmental hazards and renewable energy. Dr. Cervone focuses on problems related to the fusion of heterogenous data at different temporal and spatial scales. He has been an affiliated scientist with the Research Application Laboratory (RAL) at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado, since 2012, and adjunct professor with the Lamont-Doherety Earth Observatory at Columbia University since January 2017. Dr. Cervone holds a B.S. in computer science from the Catholic University of America, and an M.S. in computer science (artificial science track) and a Ph.D. in computational science and informatics (computational intelligence and knowledge mining track), both from George Mason University.


Keith Cheng


Keith Cheng

  • Distinguished Professor of Pathology, Biochemistry & Molecular Biology and Pharmacology
  • ICDS Associate

Department: Pathology

Office: Room C7866A, Penn State Hershey College of Medicine


Phone: 717-531-5635

Research Focuses: Cancer genetics, genomic instability, cell differentiation, genetics of human pigmentation, web-based zebrafish atlas, image informatics, SNP database analysis

Website: Visit Keith's Website

Biography: Keith Cheng, M.D., Ph.D., geneticist and pathologist, is a Distinguished Professor and director of the Computational Phenomics Initiative in the Department of Pathology. He is a member of the Jake Gittlen Laboratories for Cancer Research of the Penn State Cancer Institute, the graduate Biomedical Sciences program at Hershey, the Genomics and Bioinformatics at University Park (past founding co-director), and has joint appointments in the departments of Biochemistry & Molecular Biology, and Pharmacology. He studies genetic and molecular mechanisms in diseases such as cancer, and is seeking the genetic origins of the differences in skin color between human populations. He received a B.A. in biochemical sciences from Harvard, an M.D. from New York University, and did his anatomic pathology residency training at Brigham & Women’s Hospital and University of Washington. He earned his Ph.D. in molecular genetics and was a senior fellow at University of Washington before joining Penn State in 1992. His 2005 cover story in Science describes zebrafish’s role in discovering SLC24A5’s key role in defining light skin color in Europeans.  His team is now using population genetics and zebrafish to find the equivalent gene in East Asians.


Steven Greybush


Steven Greybush

  • Associate Professor of Meteorology
  • ICDS Co-Hire

Department: Meteorology and Atmospheric Science

Office: 618 Walker Building, University Park


Phone: 814-867-4926

Research Focuses: Data assimilation, numerical weather prediction, weather and climate of Mars, atmospheric modeling, ensemble forecasting predictability, statistical and artificial intelligence applications

Website: Visit Steven's Website

Biography: Dr. Steven J. Greybush is an associate professor in the Department of Meteorology and Atmospheric Science, and a Co-Hire of the Institute for Computational and Data Sciences. He serves as associate director of the Center for Advanced Data Assimilation and Predictability Techniques. Dr. Greybush obtained a B. S. degree from Penn State, and an M.S. and Ph.D. in atmospheric and oceanic science from the University of Maryland. His research focus is on improving and applying techniques in data assimilation, predictability, ensemble forecasting, and machine learning to advance numerical weather prediction and enhance understanding of the atmosphere of the Earth and Mars. His expertise in data assimilation has enabled successful applications spanning winter storms and severe weather on Earth to dust storms on Mars. Dr. Greybush has led the effort to create the first ensemble reanalysis for the Martian atmosphere, the Ensemble Mars Atmosphere Reanalysis System (EMARS), which has been used to examine the Martian general circulation, polar vortex, water ice clouds, thermal tides, traveling weather systems, and dust storms. Dr. Greybush has worked to improve data assimilation techniques and ensemble design, as well as understand the physical mechanisms that limit the predictability of weather phenomena. He has applied machine learning and object-based approaches to improve numerical weather prediction model output and ensemble consensus forecasts. His research bridges the gap between modeling and observations, technique development and subject matter expertise, theory and application, and he has participated in interdisciplinary collaborations with colleagues in mathematics, statistics, engineering, and public health. Dr. Greybush’s professional service roles have included executive committee membership in the American Meteorological Society (AMS) Forecast Improvement Group, associate editor for AMS journals, and organizational roles for international workshops on planetary atmospheres and data assimilation.


Vasant Honavar


Vasant Honavar

  • Professor and Edward Frymoyer Chair of Information Sciences and Technology
  • Associate Director, Institute for Computational and Data Sciences
  • ICDS Co-Hire

Department: Information Sciences and Technology

Office: 301A Westgate Building, University Park


Phone: 814-865-3141

Research Focuses: Artificial intelligence, machine learning, data mining, big data analytics, bioinformatics and computational molecular and systems biology, discovery informatics, knowledge representation and semantic web, applied information integration and informatics

Website: Visit Vasant's Website

Biography: Dr. Vasant G. Honavar is the Edward Frymoyer Endowed Professor of Information Sciences and Technology, and an Associate Director of the Institute for Computational and Data Sciences at Penn State. He is a member of faculties of the graduate programs in Computer Science and Engineering,  Bioinformatics and Genomics, Informatics, Operations Research and Neuroscience and a founding member of the undergraduate program in the Data Sciences. Dr. Honavar received a Ph.D. in computer science from the University of Wisconsin-Madison, specializing in artificial intelligence. Dr. Honavar is an expert in artificial intelligence (AI) (machine learning, knowledge representation, causal inference), information integration, big data analytics, as well as applications in bioinformatics and health informatics. His research, funded by grants totaling over $60 million during 1990-2020 (documented in over 300 peer-reviewed publications, with over 14,500 citations, h-index =57), has resulted in foundational contributions in scalable approaches to learning predictive models from (distributed, heterogeneous, multi-modal, longitudinal, and ultra high-dimensional) big data; eliciting causal information from observational and experimental data; selective sharing of knowledge across disparate knowledge bases; representing and reasoning about preferences; composing complex software services from components; and applications in bioinformatics and systems biology (including characterization, analysis, and prediction of protein-protein, and protein-RNA interfaces, interactions, and complexes, analyses of biomolecular and brain networks, integrative analyses of multi-omics data).


Kandermir Mahmut


Mahmut Kandemir

  • Distinguished Professor of Computer Science and Engineering
  • Director of Graduate Affairs
  • ICDS Associate

Department: Computer Science and Engineering

Office: 354C Information Science and Technology Building, University Park


Phone: 814-863-4888

Research Focuses: Embedded systems, programming languages, compilers, power-aware computing, dependable computing, input/output systems

Website: Visit Mahmut's Website

Biography: Mahmut Taylan Kandemir is a professor in the Department of Computer Science and Engineering at Penn State, and a member of the Microsystems Design Lab. Dr. Kandemir's research interests are in optimizing compilers, high-performance computing, computer architecture, storage systems, and latest trends in public cloud services. He is the author of more than 100 journal publications and over 500 conference/workshop papers in these areas. He advised 32 doctoral and 20 master's students who have graduated, and is currently advising/co-advising 10 doctoral students and 2 master's students. He served in the program committees of 40 conferences and workshops in computer science and engineering. He is a member of the hall of fame of three top computer architecture conferences: MICRO, ISCA and HPCA. His research is/was funded by NSF, DOE, DARPA, SRC, Intel, and Microsoft. He is a recipient of an NSF Career Award and the Penn State Premier Research Award. He is a Fellow of IEEE. Between 2008-2012 and 2017, he served as the graduate coordinator of the Department of Computer Science and Engineering at Penn State. Dr. Kandemir’s research lab actively works on novel computer architectures including accelerators such as GPUs and FPGAs, application mapping and code optimization techniques for emerging multicore/manycore architectures, and large-scale data storage and management for high-performance computing systems.


Edward O'Brien


Edward O’Brien

  • Associate Professor of Chemistry
  • ICDS Co-Hire

Department: Chemistry

Office: 402 Chemistry Building, University Park


Phone: 814-867-5100

Research Focuses: Ribosome-associated protein folding, macromolecular self-assembly and chaperone interactions in living cells

Website: Visit Edward's Website

Biography: Dr. Ed O'Brien's research focuses on non-equilibrium phenomenon in molecular biology and chemistry through the development and application of theoretical and computational tools from chemistry, physics, and bioinformatics. Recent efforts have focused on the molecular origins of the kinetics of protein synthesis, how changes in protein synthesis rates alter protein structure and function, as well as the impact of these changes on messenger RNA half-lives. These efforts have contributed to the emergence of a new paradigm in which the kinetics of protein synthesis can have long-term biological consequences for cells and organisms. Dr. O'Brien received degrees in biochemistry and chemical physics and carried out postdoctoral research at University of Cambridge, and his research has been supported by the National Science Foundation, the National Institutes of Health, and international funding bodies including the Human Frontiers Science Program, and the Engineering and Physical Science Research Council in the United Kingdom. He is a recipient of the Presidential Early Career Award for Scientists and Engineers from the White House.



Sarah Rajtmajer

  • Assistant Professor of Information Sciences and Technology
  • ICDS Associate

Department: Information Sciences and Technology

Office: E351 Westgate Building


Phone: 814-863-2554

Research Focuses: Machine Learning, Network Science, Game Theory, Privacy and Security

Website: Visit Sarah's Website

Biography: Dr. Rajtmajer’s research integrates graph theory, game theory, and machine learning, with applications to social and biological phenomena. Recent work aims at characterization and modeling of behavior within evolving social networks both in the abstract as evolutionary games on structured populations, and in applied, data-driven scenarios related to privacy decision making, deviance, and abuse. Current focus includes implications of these behaviors for emerging topics in defense and intelligence, including state-backed disinformation. In addition, Dr. Rajtmajer is interested in understanding fundamental issues around credibility and reproducibility in big data science. In particular, the large datasets considered in computational social science bring additional challenges as privacy protection dovetails with the usual conversations around methods, reporting and dissemination standards, evaluation, and incentives. Before joining the Penn State faculty, she served as a consultant to the Defense Advanced Research Projects Agency on scientific programs aimed at breakthrough technologies for national security, with specific focus on initiatives in big data and computational social science.  Prior to her work in consulting, Dr. Rajtmajer was an Intelligence Community Postdoctoral Research Fellow at the Applied Research Laboratory and a Postdoctoral Scholar in the Department of Mathematics at Penn State. Dr. Rajtmajer has a PhD in Mathematics from the University of Zagreb, Croatia, and a BA in Mathematics from Columbia University.



Marcos Rigol

  • Professor of Physics
  • ICDS Co-Hire

Department: Physics

Office: 104 Davey Lab


Phone: 814-865-6460

Research Focuses: Non-equilibrium quantum dynamics, strongly correlated systems, ultracold gases, magnetism, disorder, computational physics

Website: Visit Marcos's Website


Aleksanda Slavkovic


Aleksandra Slavkovic

  • Professor of Statistics
  • ICDS Associate

Department: Statistics

Office: 412 Thomas Building, University Park


Phone: 814-863-4918

Research Focuses: Usability evaluation methods, human performance in virtual environments, statistical data mining, application of statistics to social sciences, algebraic statistics, and statistical approaches to confidentiality and data disclosure

Website: Visit Aleksandra's Website

Biography: Dr. Aleksandra (Seša) Slavković is a professor of statistics with a joint appointment in the Department of Public Health Sciences, and the associate dean for graduate education in the Eberly College of Science at Penn State. She received a Ph.D. (2004) in statistics, and a master of human-computer interaction (1999) from Carnegie Melon University, and B.A. in psychology from Duquesne University (1996). She joined the Penn State Department of Statistics in 2004, and has held visiting scholar positions at Cornell University, the University of California Berkeley, the University of Minnesota, and Utrecht University. In the statistics department she served as associate head for diversity and equity (2014-2017) and associate head for graduate studies (2013-2018). She currently serves on the NORC Advisory Committee on Statistics, has served on a number of National Academy of Sciences/National Research Council committees, and has chaired the American Statistical Association (ASA) Privacy and Confidentiality committee. Slavković is associate editor of the Annals of Applied Statistics and Journal of Privacy and Confidentiality. She is a Fellow of the ASA (2018), elected member of the International Statistical Institute (2012) and received the 2017 Graduate School Alumni Society Graduate Program Chair Leadership Award from Penn State. Her research interests include methodological developments in the area of data privacy and confidentiality in the context of small and large scale surveys of health, genomic, and network data. Her focus is on the interplay of tools from statistics and computer science that leads to formal privacy protection - such as differential privacy - and broad data access, but also offers guarantees of accurate statistical inference needed to support reliable science and policy. Other past and current research interests include evaluation methods for human performance in virtual environments, statistical data mining, application of statistics to information sciences and social sciences, algebraic statistics, and causal inference.