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.

Professor Goldsztein is originally from Buenos Aires, Argentina. In 1992 he received his undergraduate degree in mathematics from the University of Buenos Aires and in 1997 a Ph.D. in mathematics from MIT. During the three following years (1997-2000), he was a postdoctoral scholar and lecturer in applied mathematics at CalTech. Since 2000, he has been a faculty member of the School of Mathematics of Georgia Tech, where he is now a full professor. Professor Goldsztein enjoys applying mathematics that can be used in other other fields of science such as computational biology, machine learning, and the intersection between math and physics. Machine learning is among his areas of expertise.

Hannah Choi is an Assistant Professor in the School of Mathematics at Georgia Tech. Her research focuses on mathematical approaches to neuroscience, with primary interests in linking structures, dynamics, and computation in data-driven brain networks at multiple scales. Before coming to Georgia Tech, she was a postdoctoral fellow at the University of Washington and also a visiting scientist at the Allen Institute for Brain Science, and spent one semester at the Simons Institute for the Theory of Computing at the University of California, Berkeley as a Patrick J McGovern Research Fellow. She received her Ph.D. in Applied Mathematics from Northwestern University and her BA in Applied Mathematics from the University of California, Berkeley.

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.
 

Molei Tao received B.S. in Math & Physics in 2006 from Tsinghua Univ. (Beijing) and Ph.D. in Control & Dynamical Systems with a minor in Physics in 2011 from Caltech (advisor: Houman Owhadi, co-advisor: Jerry Marsden). Afterwards, he worked as a postdoc in Computing & Mathematical Sciences at Caltech from 2011 to 2012, and then as a Courant Instructor at NYU from 2012 to 2014. From 2014 on, he has been an assistant, and then associate professor in School of Math at Georgia Tech. He is a recipient of W.P. Carey Ph.D. Prize in Applied Mathematics (2011), American Control Conference Best Student Paper Finalist (2013), NSF CAREER Award (2019), AISTATS best paper award (2020), IEEE EFTF-IFCS Best Student Paper Finalist (2021), Cullen-Peck Scholar Award (2022), GT-Emory AI.Humanity Award (2023), a Plenary Speaker at Georgia Scientific Computing Symposium (2024), a Keynote Speaker at (2024) International Conference on Scientific Computing and Machine Learning, SONY Faculty Innovation Award (2024), Best Poster Award at 2024 international conference “Recent Advances and Future Directions for Sampling” held at Yale, and Richard Duke Fellowship (2025).

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 conducts research in nonlinear algebra with an emphasis on algorithm design and applications. His recent work centers on computer algebra, spanning topics from computer-assisted proofs to hybrid symbolic–numerical computation to parallel computing. He is also interested in applying methods from algebra and geometry to problems in science and engineering, with applications in areas such as computer vision and astrodynamics.