Jung Hee Seo, PhD

Associate Research Scientist, Department of Mechanical Engineering, Whiting School of Engineering, Johns Hopkins University.

Dr. Seo’s research expertise is in the areas of computational fluid dynamics and acoustics. He is currently conducting research on multi-physics computational modeling and analysis of cardiovascular flows.

Benjamin Zaitchik, PhD

Assistant Professor, Department of Earth & Planetary Sciences, Krieger School of Arts & Sciences, Johns Hopkins University.

Dr. Zaitchik’s research addresses problems of regional climate variability, water resource monitoring, disease early warning, and climate change adaptation. Prior to joining Johns Hopkins he worked in the NASA Hydrological Sciences Branch and as a Foreign Affairs Officer in the U.S. State Department.

Hong Kai Ji, PhD

Associate Professor, Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University.

Dr. Ji is interested in developing statistical and computational methods for analyzing high-throughput genomic data. He applies these tools to study gene regulatory programs in development and diseases.

Philip Bourne, PhD

KEYNOTE SPEAKER

Associate Director for Data Science, National Institutes of Health.

Philip E. Bourne of the National Institutes of Health: Associate Director for Data Science (ADDS) at the NIH; former Associate Vice Chancellor for Innovation and Industry Alliances; Professor in the Department of Pharmacology and Skaggs School of Pharmacy and Pharmaceutical Sciences at the University of California San Diego; Associate Director of the RCSB Protein Data Bank; Adjunct Professor at the Sanford Burnham Institute.

Daniel Robinson, PhD

Assistant Professor, Department of Applied Mathematics & Statistics, Whiting School of Engineering, Johns Hopkins University.

Daniel designs, analyzes, and implements algorithms for large-scale optimization and complementarity problems. Applications of current interest include real-time optimization in energy systems, subspace clustering in computer vision, and predictive modeling in healthcare.

Jaime E. Combariza, PhD

Director of MARCC, Associate Research Scientist, Department of Chemistry, Krieger School of Arts & Sciences, Johns Hopkins University.

Director of the new Maryland Advanced Research Computing Center, a shared computing facility located on the Bayview Campus of Johns Hopkins University and funded by a State of Maryland grant to Johns Hopkins University through IDIES. MARCC is jointly managed by Johns Hopkins University and the University of Maryland College Park.

S. Alexander Szalay, PhD

Director of IDIES, Professor of Astrophysics & Computer Science, Krieger School of Arts & Sciences, Johns Hopkins University.

Professor Szalay is the founding Director of IDIES, a Bloomberg Distinguished Professor, Alumni Centennial Professor of Astronomy, and Computer Science Department Professor. He is a cosmologist, working on the use of big data in advancing scientists’ understanding of astronomy, physical science, and life sciences.

Vadim Zipunnikov, PhD

Assistant Professor, Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University.

Vadim Zipunnikov is interested in real-world data applications, especially in brain imaging and wearable computing. As a member of the Statistical Methodology and Applications for Research in Technology Working Group he is constantly involved in collaborating on new problems with scientific teams.

Kristin Persson, PhD

KEYNOTE SPEAKER

Assistant Professor, University of California at Berkeley.

Kristin Persson leads the Materials Project at the Lawrence Berkeley National Laboratory. She is director of the 2012 BES-funded “Materials Project Center for Functional Electronic Materials Design”. Professor Persson is a PI of the Crosscutting Thrust of the recently launched JCESR hub, as well as the Batteries for Advanced Transportation Technologies (BATT) program; She is co-founder of the clean-energy start-up Pellion Technologies Inc.

Tamás Budavári, PhD

Assistant Professor, Department of Applied Mathematics & Statistics, Whiting School of Engineering, Johns Hopkins University.

Dr. Budavári’s research interests include computational statistics,Bayesian inference, low-dimensional embeddings, and streaming algorithms. He employs parallel processing on GPUs to improve processing times.