January 31, 2020
It is a great pleasure to share that LIDS faculty member Caroline Uhler and LIDS Affiliate Member Elchanan Mossel were named 2019 Simons Investigators by the Simons Foundation.
Simons Investigator awards are given to outstanding theoretical scientists who are undertaking long-term study of fundamental questions. Awardees receive support for five years.
Caroline received her award in the Mathematical Modeling of Living Systems (MMLS) category. Investigators in this program work on mathematical and theoretical approaches to topics in the life sciences. Caroline’s citation reads:
Caroline Uhler has made major contributions to the development of methods in statistics and machine learning for applications in genomics. Her work to date has broken new ground on providing a systematic approach to studying graphical models. In particular, she uncovered statistical and computational limitations for causal inference and developed a novel framework for causal structure discovery from a mix of observational and interventional data. This led to new models and algorithms for inferring gene regulatory networks and for disease diagnostics by integrating gene expression data with the 3-D organization of the genome.
Elchanan received his award in the Mathematics category. Investigators in this program are establishing creative new research directions in theoretical math and providing leadership in the field. Elchanan’s citation reads:
Elchanan Mossel’s primary research fields are probability theory, combinatorics, theoretical computer science and statistical inference. Mossel is broad and collaborative in his research. Much of his work spans different areas of mathematics or bridges between mathematics and other sciences. With collaborators, he made fundamental contributions to discrete Fourier analysis and its applications to computational complexity and voting theory. In the area he named ‘combinatorial statistics,’ his collaborative work includes important discoveries on tree broadcast models and associated reconstruction problems, detection of block models, the inference of evolutionary histories and, more recently, deep inference.
To learn more about the program and the 2019 cohort, please visit: https://www.simonsfoundation.org/grant/simons-investigators/?tab=awardees