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NITMB Faculty
Olga Anosova
University of Liverpool
I'm a data scientist developing the emerging area of Geometric Data Science for applications in mathematical crystallography, geometry of proteins, and mathematical modelling. My PhD thesis was devoted to foundational results on invariant manifolds in dynamical systems. Later work applied invariant theory to crystallography, developing complete, continuous, and polynomial-time invariants for classifying periodic point sets and crystal structures under rigid motion and isometry, leading to efficient geometric representations and predictive models. The new mathematical definition of a crystal structure as an equivalence class under rigid motion (IUCrJ 2024) and implementation of structural invariants (Pattern Recognition 2026) led to uncovering large-scale duplication in the Cambridge Structural Database, Google's GNoME dataset, and Protein Data Bank (MATCH 2025).
Changqing Cheng
Binghamton University
Dr. Changqing Cheng is an Associate Professor in the School of Systems Science and Industrial Engineering at Binghamton University. His research interest lies in computational engineering and network science for quality and resilience assurance in interdisciplinary innovations. He has been working on statistical learning and nonlinear dynamics analysis for simulation, optimal design and optimization of complex systems, with applications in healthcare systems, including epidemic modeling and chronic diseases diagnosis. He has also explored interdisciplinary applications in non-biology domains, including manufacturing and power networks. He is always inspired by new mathematical discoveries, particularly from the perspective of complexity, statistical learning, and nonlinear dynamics that are underlying most complex systems.
Calina Copos
Northeastern University
Calina Copos is an Assistant Professor of Mathematics and Biology at Northeastern University. Her research group is broadly interested in mathematical biology and numerical and computational methods for PDEs, with a particular focus on the cell cytoskeleton and cell migration in tissue development, homeostasis, and regeneration. Prof. Copos received her Ph.D. in Applied Mathematics at University of California, Davis and did her postdoctoral work at the Courant Institute at New York University.
Robert Eisenberg
Rush University; Illinois Institute of Technology; University of Illinois Chicago
John Glasser
Emory University
John Glasser studied population biology, epidemiology, and biostatistics at Princeton, Duke, and Harvard Universities, served as an Epidemic Intelligence Service officer in the CDC’s Division of Reproductive Health, the late Richard Levins’ postdoctoral fellow in mathematical biology, and a mathematical epidemiologist in the National Center for Immunization and Respiratory Diseases. Currently, he is an Adjunct Professor in the Department of Health Policy and Management and member of the graduate faculty in Population Biology, Ecology, and Evolution at Emory University and an Associate Editor of Mathematical Biosciences. He is interested in transmission modeling to design or evaluate and possibly improve vaccination policy to prevent or control respiratory diseases.
Trevor GrandPre
Washington University in St. Louis
Trevor GrandPre is an Assistant Professor of Physics at Washington University in St. Louis, where his research focuses on non-equilibrium statistical physics and soft condensed matter, with a particular focus on critical phenomena in biological systems. His group investigates how physical laws shape living matter, studying topics such as the regulation of biomolecular condensates, which are membraneless cellular compartments that form through phase separation, and their roles in gene regulation and cell-cycle control. Other areas of interest include phase transitions in adaptive immune systems, such as the co-evolution of bacteria and phages as well as immune cell dynamics, and the use of physics-informed machine learning to construct optimal coarse-grained models of proteins and other biophysical systems. Trevor earned his B.S. in Physics from DePaul University in 2014 and his Ph.D. in Physics from the University of California, Berkeley in 2021. From 2021 to 2025, he was an independent postdoctoral fellow at the Center for the Physics of Biological Function (CPBF) and the Princeton Center for Theoretical Science (PCTS), and a Schmidt Science Fellow at Princeton University.
Jingrui He
University of Illinois Urbana-Champaign
Dr. Jingrui He is a Professor at School of Information Sciences, University of Illinois at Urbana-Champaign. She received her PhD from Carnegie Mellon University in 2010. Her research focuses on heterogeneous machine learning, active learning, neural bandits, and self-supervised learning, with applications in security, agriculture, social network analysis, healthcare, and finance. Dr. He is the recipient of the 2016 NSF CAREER Award, the 2020 OAT Award, the 2025 Amazon Research Award, three times recipient of the IBM Faculty Award in 2018, 2015 and 2014 respectively, and was selected as IJCAI 2017 Early Career Spotlight. Dr. He has more than 200 publications at major conferences (e.g., ICML, NeurIPS, ICLR, KDD) and journals (e.g., TMLR, TKDD, JMLR, JAIR), and is the author of two books. Her papers have received the Distinguished Paper Award at FAccT 2022, the Outstanding Paper Award at ICCV 2025 MMRAgI Workshop, as well as Bests of the Conference at ICDM 2016, ICDM 2010, and SDM 2010. Dr. He is a Distinguished Member of ACM, a Senior Member of AAAI and IEEE. She is also the Program Co-chair of IEEE BigData 2023.
Sergei Maslov
University of Illinois Urbana-Champaign
I’m a Professor of Bioengineering and Physics at the University of Illinois UrbanaChampaign. I codirect the Center for Artificial Intelligence and Modeling at the Carl R. Woese Institute for Genomic Biology and am affiliated with Argonne National Laboratory. My passion is blending theoretical statistical physics, computational biology and machine learning to tackle both big-picture and handson questions in microbial ecology and systems biology. I build simple but powerful “bottomup” models to tease apart the dynamics of complex systems—from whole microbial communities down to biomolecular networks.
Here are a few highlights from my recent work:
Microbial ecosystems: I model how communities stay diverse, how many stable “states” they can have, and how we might steer them. For example, I’ve used simulations of nutrient competition, crossfeeding and phagehost coevolution in gut microbiome models—insights that can improve health, manage environmental systems and help control outbreaks.
Systems biology: I study how the burstiness and noise in generegulatory and proteininteraction networks shape cellular function and robustness. My models infer RNAvelocity, explore learning dynamics in dimerization networks, and quantify how nonspecific interactions affect cell behavior.
Origin of life: Using physicsbased approaches, I’ve mapped out conditions under which simple chemical reactions (templateassisted ligation) can assemble long, structured polymers like ribozymes—shedding light on Darwinian processes in the RNA world.
Deep learning: I apply LLMs and specialized models (e.g., ProBERTA, DRBERT) to predict gene fitness, epistasis and regulatory links, to analyze protein interactions, and to forecast microbial community behavior and drug responses. NITMB’s focus on marrying math and biology is a perfect fit for my interdisciplinary style. I’m excited to collaborate with the NITMB community to push those boundaries further—and to help train the next generation of computational biologists. https://maslov.bioengineering.illinois.edu
Mikhail Tikhonov
Washington University in St Louis
Mikhail Tikhonov is a theoretical physicist and quantitative biologist, appointed an Associate Professor of Physics at Washington University in St Louis. His group applies ideas and methods from statistical physics to study eco-evolutionary feedbacks in high-diversity microbial ecosystems, and evolution in changing or fluctuating environments. He received his Master's degree from Ecole Normale Superieure in 2009, and his Ph.D. from Princeton in 2014.
Gökçe Dayanıklı
University of Illinois Urbana-Champaign
I'm an Assistant Professor in the Department of Statistics at the University of Illinois Urbana-Champaign, with affiliate appointments in Industrial & Enterprise Systems Engineering and the Carl R. Woese Institute for Genomic Biology. My research focuses on the mathematical modeling and analysis of complex societal and biological systems, including the design of optimal incentives and policies that account for human behavioral responses to those interventions. I integrate theory from stochastic optimal control, game-theoretic frameworks such as mean field games and control and Stackelberg equilibria, and computational tools including Monte Carlo simulation, machine learning, and reinforcement learning to develop scalable algorithms. My primary application areas include epidemic spread modeling and mitigation, and climate policy. I am also interested in interdisciplinary applications in non-biology domains, such as market design, economics, and opinion dynamics.
Tahra Eissa
University of Colorado Anschutz School of Medicine
Dr. Tahra Eissa is an Assistant Professor in the Departments of Physiology& Biophysics and Psychiatry at the University of Colorado Anschutz School of Medicine. Her interests center around computational neuroscience with a focus on the cognitive strategies and neural mechanisms that support decision-making and inference of latent environmental statistics. Her work incorporates human online behavioral testing, intracranial brain recordings, and a wide range of behavioral and neural modeling approaches. She has a strong emphasis on interdisciplinary work and collaboration, bridging experimental findings and computational modeling to better understand the human brain.
Dwueng-Chwuan Jhwueng
Feng-Chia University
I am a Professor of Statistics at Feng Chia University in Taichung, Taiwan. My research focuses on statistical phylogenetics, developing and applying methods at the intersection of stochastic processes and comparative biology. My group investigates diffusion-based and heteroskedastic rate models (OU/GBM/CIR; ARMA/GARCH-type branch-rate processes) for trait evolution and creates likelihood-free (ABC) and Bayesian pipelines for model fitting and selection. Recent projects include phylogenetic negative binomial regression for count traits, diagnostic checklists for model adequacy and identifiability on trees and networks, and reproducible R/Python toolkits for simulation-based inference. I am dedicated to connecting foundational theory with empirical applications and teaching through seminars, tutorials, and code clinics that make advanced methods in mathematical biology more accessible. https://tonyjhwueng.info/
https://stat.fcu.edu.tw/en/teachers-detail/?id=T00237&unit_id=CB07
Kresimir Josic
University of Houston
Krešimir Josić is John and Rebecca Moores Professor in the Department of Mathematics at the University of Houston with adjunct appointments in Biology and Biochemistry at UH, and BioSciences at Rice. He received his Phd in 1999 under the supervision of C. Eugene Wayne, in applied dynamical systems. Over the last 25 years he has worked in different areas of mathematical biology, with a focus on understanding the structure of neuronal activity, and modeling single celled organisms from the level of signaling pathwaysto large microbial populations. He also regularly contributes to the program Engines of Our Ingenuity which is broadcast by over 50 NPR stations nationwide.
Vitaliy Kurlin
University of Liverpool
Professor Vitaliy Kurlin is a mathematician by training and leads the Data Science Theory and Applications group in the Materials Innovation Factory at the University of Liverpool, UK. The group develops the emerging area of Geometric Data Science for applications in crystallography, chemistry, and structural biology. The recent funding includes the Royal Academy of Engineering Industry Fellowship, EPSRC New Horizons, and the Royal Society APEX Fellowship in the UK.
Laura Patricia Schaposnik Massolo
University of Illinois at Chicago
Laura P. Schaposnik is an Argentinian Professor of Mathematics at the University of Illinois, Chicago. Her research sits at the intersection of geometry, topology, and mathematical physics, with a focus on moduli spaces of decorated bundles, and also leads applied projects in network science and mathematical modeling. Prof. Schaposnik received the 2025 Presidential Early Career Award for Scientists and Engineers (PECASE), as well as an NSF CAREER award, a Simons Fellowship, and a Humboldt Fellowship. She is actively involved in mentoring and outreach, including the development of bilingual STEM books for young readers. Prof. Schaposnik will deliver the AMS-MAA Invited Lecture at MathFest 2026.
Jake Soloff
University of Michigan
Jake A. Soloff is an assistant professor of statistics at the University of Michigan. His research examines the theoretical foundations of statistical machine learning, aiming to develop methods that are both principled and broadly applicable. He received his PhD in statistics from UC Berkeley in 2022, advised by Aditya Guntuboyina and Michael I. Jordan, and completed a postdoc at the University of Chicago with Rina Foygel Barber and Rebecca Willett.
Kristina Wicke
New Jersey Institute of Technology
Kristina Wicke obtained her PhD in Biomathematics from the University of Greifswald (Germany) in 2020. She then spent two years as a President’s Postdoctoral Scholar at The Ohio State University (USA) and is now an Assistant Professor in the Department of Mathematical Sciences at the New Jersey Institute of Technology (USA). Her research focuses on mathematical phylogenetics, particularly on the combinatorial properties of phylogenetic trees and networks, the estimation of phylogenetic trees and networks from genomic data, and the application of phylogenetic methods in biodiversity conservation.
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