Maryn will be starting as an NITMB Fellow this summer. Broadly, she is interested in uncovering the genetic mechanisms by which organisms adapt to new or changing environments, at the cellular, population, and species levels. Since 2023, Maryn has been a postdoc in Arvind Murugan's group at the James Franck Institute, where she has been working on several questions related to protein evolution. Maryn conducted her PhD research at the University of Chicago, working with Matthias Steinruecken, on population genetic theory and inference. She previously studied plant pathology and genetics with Michael Gore at Cornell University.

Federica Ferretti is a theoretical physicist with a background in classical equilibrium and non-equilibrium statistical mechanics, broadly interested in statistical models and quantitative approaches for biology. Ferretti obtained a PhD from La Sapienza University of Rome, with Prof. Irene Giardina as advisor. During their PhD, Ferretti worked on the development of inference methods for the collective dynamics of bird flocks and quantification of irreversibility in polar active matter. In 2022 Ferretti joined the Chakraborty group at MIT to work with Prof. Arup Chakraborty and Prof. Mehran Kardar on the adaptive immune system. Ferretti's most recent research interests include the characterization of B cell epitope immuno-dominance and stochastic aspects of affinity maturation dynamics.

Efe will join the NITMB and The University of Chicago as a postdoctoral fellow in September 2024. Prior, Efe was a graduate student at the Institute for Theoretical Physics at ETH Zurich, following Efe's undergraduate studies at Bilkent University, Turkiye. As a theoretical physicist, Efe's expertise lies at the crossroads of machine learning, statistical physics, and information theory. Efe's work involves developing novel mathematical techniques and algorithms to identify collective building blocks that store the relevant information in complex systems. At NITMB, Efe's focus is on developing effective coarse-grained models to capture emergent hierarchical organization across multiple scales in living systems.

Alasdair’s research interests lie at the intersection of numerical applied mathematics and biophysics, combining techniques from spectral methods, optimization, and dynamical systems theory with experimental data. He works closely with experimental collaborators to develop data analysis and model inference frameworks broadly applicable across various experimental systems, from animal behavior to bacterial swarming and developmental biology. Alasdair will receive his Ph.D. in applied mathematics in May 2024 from the Massachusetts Institute of Technology, where his advisor was Prof. Jörn Dunkel. Before graduate school, Alasdair did his undergraduate studies at MIT in Mathematics and Physics.

Xueying Wang is a Postdoctoral Fellow at the National Institute for Theory and Mathematics in Biology. Earning her Ph.D. in Physics from the University of Illinois, Urbana-Champaign, her research tackles the dynamical properties of complex, chaotic, and out-of-equilibrium systems, including fluid turbulence, biological and artificial neural networks, ecological systems, and active matter. In her doctoral work, she developed a spatially extended stochastic ecological model of energy flow in a fluid undergoing the transition to turbulence and predicted the four different phases encountered during the progression to fully developed turbulence in the quasi-one-dimensional flow. She employs a combination of computational and analytical techniques derived from statistical physics in her research. She has widespread research interests ranging from fluid turbulence to generalized learning & adaptation and structural stability & emergent functionality. For more information, see personal website and Google Scholar page.