NITMB Fellows
NITMB sponsors a NITMB Fellows Program, in which we support independent Postdoctoral Fellows for a three year term. NITMB Fellows develop and conduct independent research programs aligned with the Institute’s interest in constraints and the capabilities of living systems under the mentorship of NITMB leaders. They can collaborate with NITMB members or others outside of the Institute. The Program seeks to recruit young scientists trained in either mathematics (pure and applied), physics, or biology who have demonstrated exceptional research promise, with the promise that they will develop into tomorrow’s leaders. They must also demonstrate a deep commitment to the NITMB goals for engagement and community. We expect that in their future careers, NITMB Fellows will act as bridges, bringing the mathematics and biology communities closer together.
Current Fellows
Maryn Carlson
Maryn Carlson is broadly interested in uncovering the genetic mechanisms by which organisms adapt to new or changing environments, at the cellular, population, and species levels. Carlson has been a postdoc in Arvind Murugan's group at the James Franck Institute, where she worked on several questions related to protein evolution. She 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
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.
Doruk Efe Gökmen
Efe Gökmen previously was a graduate student at the Institute for Theoretical Physics at ETH Zurich, following Gökmen's undergraduate studies at Bilkent University, Turkiye. As a theoretical physicist, Gökmen's expertise lies at the crossroads of machine learning, statistical physics, and information theory. Gökmen's work involves developing novel mathematical techniques and algorithms to identify collective building blocks that store the relevant information in complex systems. At NITMB, Gökmen's focus is on developing effective coarse-grained models to capture emergent hierarchical organization across multiple scales in living systems.
Alasdair Hastewell
Alasdair Hastewell’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. Hastewell received 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, Hastewell did his undergraduate studies at MIT in Mathematics and Physics.
Xueying Wang
Xueying Wang earned her Ph.D. in Physics from the University of Illinois, Urbana-Champaign. Wang's 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. Wang 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.
