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Info-Metrics Institute | Annual Prize Winners


Nataly K. Balasha

Nataly Kravchenko-Balasha completed her PhD in 2010 in experimental biochemistry at the Hebrew University under the supervision of Prof. Alexander Levitzki. Following graduation, Dr. Balasha was a postdoctoral fellow at the Hebrew university in the theoretical chemistry group of Prof. Raphael D. Levine. In July 2015 she completed her postdoctoral research at Caltech in the biophysical chemistry group of Prof. James R Heath and started a new job as an assistant professor at the Hebrew University in Jerusalem.

Her broad research interests are to develop thermodynamic based information theoretic approaches for system level analysis and for understanding of complex human diseases, such as cancer. Her goal is to apply concepts from physical science to gain quantitative and predictive understanding of changes in protein/transcriptional/metabolic regulatory networks that drive the emergent characteristics of diseased tissues. During her post-doctoral trainings she has established a quantitative approach that determines the stability of a phosphoprotein signaling network in two Glioblastoma (GBM, brain tumor) interacting cells and demonstrated how that stability dictates the cell-cell distance distribution in a bulk culture. Recently she found that free energy gradient of GBM cell-cell signaling directs cell-cell motion through exchanged proteins.

Also she has used thermodynamic based approaches to identify tumor specific unbalanced processes and their differential influence across the various tumors. Those processes may provide insights for identifying patient oriented combination therapies. 

Her grand vision is to develop a compact way to describe and to predict cellular processes that are often viewed as ‘complex’ biological phenomena through experimental-theoretical approach based on physico-chemical laws.


Jens Hainmueller

Jens Hainmueller is an Associate Professor in the Department of Political Science at Stanford University. He also holds a courtesy appointment in the Stanford Graduate School of Business. He is the Faculty Co-Director of the Stanford Immigration and Integration Policy Lab and a Faculty Affiliate at the Stanford Europe Center.

His research interests include statistical methods, immigration, political economy, and political behavior. He has published over 30 articles, many of them in leading journals in political science, economics, and statistics, such as the American Journal of Political Science, American Political Science Review, Journal of the American Statistical Association, Proceedings of the National Academy of Sciences, Review of Economics and Statistics, Political Analysis,Management Science, and International Organization. He has also published three open source software packages and his research has received awards from the American Political Science Association, the Society of Political Methodology, and the Midwest Political Science Association.

Hainmueller received his PhD from Harvard University and also studied at the London School of Economics, Brown University, and the University of Tübingen. Before joining Stanford, he served on the faculty of the Massachusetts Institute of Technology.

Justin B. Kinney

Justin B. Kinney is an Assistant Professor in the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory. Kinney completed his BA in Physics and Math from Cornell University in 2002. In 2008 he received his PhD in Physics from Princeton University for work performed under the advisement of Curtis Callan and Edward Cox. From 2010 to 2014 he was a Quantitative Biology Fellow at Cold Spring Harbor Laboratory.

Kinney's research uses an integrative combination of theory, computation, and experiment to advance the understanding of quantitative sequence-function relationships in molecular biology. Of particular interest is how the programs that govern when and where cells express different genes are encoded within genomic DNA. One arm of Kinney's research program focuses on developing and applying new experimental methods that use ultra-high-throughput DNA sequencing to dissect the biophysical basis of these regulatory programs. The other arm of his research effort centers on developing new mathematical and computational methods for addressing the statistical learning problems that are highlighted by this experimental work.