Mayya Zhilova is an associate professor in the School of Mathematics at Georgia Tech and an affiliated member of the Machine Learning Center. She received her Ph.D. in statistics from the Humboldt University of Berlin in 2015. 

Her primary research interests lie in the areas of mathematical statistics, statistical learning theory, and uncertainty quantification, particularly in statistical inference for complex high-dimensional data, performance of resampling procedures for various classes of problems, functional estimation, and inference for misspecified models.

Galyna Livshyts completed her undergraduate studies in Kharkiv, Ukraine. She obtained her PhD from Kent State University in Ohio in 2015 under the supervision of Artem Zvavitch. Since 2015, Galyna has been an assistant professor at the School of Math, Georgia Institute of Technology. In Fall 2017, she was a postdoc at the MSRI program in Geometric Asymptotic Analysis and Applications at MSRI, Berkeley. Galyna is interested in High-dimensional Probability and Convexity, as well as Asymptotic Analysis and Random Matrix Theory.

Anton Leykin received the Ph.D. degree from the University of Minnesota, Twin Cities. He works in nonlinear algebra with a view towards algorithms and applications. A large part of his recent work concerns homotopy continuation methods, which includes both theory and implementation in Macaulay2 computer algebra system. He is a member of the ACM, AMS, and SIAM.

Rachel Ann Kuske is an American-Canadian applied mathematician and Professor and Chair of Mathematics at the Georgia Institute of Technology. 

Kuske received her Ph.D. in Applied Mathematics from Northwestern University in 1992. Her dissertation, Asymptotic Analysis of Random Wave Equations, was supervised by Bernard J. Matkowsky. From 1997 to 2002, she was assistant professor and then associate professor at the University of Minnesota. 

She is an expert on stochastic and nonlinear dynamics, mathematical modeling, asymptotic methods, and industrial mathematics. She served on the Scientific Advisory Board for the Institute for Computational and Experimental Research in Mathematics (ICERM), and as of 2021 she serves on ICERM's board of trustees.

Christine Heitsch is Professor of Mathematics at Georgia Tech, with courtesy appointments in Biological Sciences and Computational Science & Engineering as well as an affiliation with the Petit Institute for Bioengineering & Bioscience.

She is also Director of the new Southeast Center for Mathematics and Biology (SCMB), an NSF-Simons MathBioSys Research Center, and finishing her tenure directing the GT Interdisciplinary Mathematics Preparation and Career Training (IMPACT) Postdoctoral Program.

Heitsch's research interests lie at the interface between discrete mathematics and molecular biology, specifically combinatorial problems "as motivated by" and "with applications to" fundamental biomedical questions like RNA folding.

Students interested in pursuing graduate studies in discrete mathematical biology can do so through a number of GT PhD programs including Bioinformatics or Quantitative Biosciences as well as Algorithms, Combinatorics, and Optimization (ACO), Computational Science & Engineering (CSE), and (of course) Mathematics.