Maryn is an evolutionary geneticist interested in the genetic underpinnings of adaptation. A major focus of her current work is developing theory and methods for interpreting high-throughput mutagenesis studies, particularly in the context of within and across species variation. Before joining NITMB, Maryn was briefly a postdoc in Arvind Murugan's group at UChicago, where she studied complex signaling networks. She earned her PhD, also at the University of Chicago, working with Matthias Steinruecken, on population genetic theory and inference. Prior to this, she studied plant pathology and genetics with Michael Gore at Cornell University. Next year, Maryn will be starting her own lab in the Department of Biology at the University of Rochester.

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 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’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 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.

Ratul Biswas received his PhD in Mathematics from the University of Minnesota, where he worked under the supervision of Wei-Kuo Chen and Arnab Sen. His research lies in probability theory, with a focus on biologically inspired complex systems and stochastic models that bridge statistics, physics, biology, and computer science. He studies rugged fitness landscapes as models of evolutionary search, examines how network-level similarity and connectivity influence generalization and overparameterization effects in graph-based learning architectures, and analyzes the dynamics of non-reciprocally coupled systems to understand emergent behavior in interacting populations.

Chen-Wei (Milton) Lin is a PhD candidate in Mathematics at Johns Hopkins University under the supervision of David Gepner. His research focuses on the p-adic geometry and homotopy theory, especially within the relative Langlands program developed by Ben-Zvi, Sakellaridis, and Venkatesh. Later in his graduate studies, he collaborated with mathematical neuroscientist Chris Hillar, expanding his interests to biologically plausible algorithms and reinforcement learning. In his free time, Lin enjoys experimenting with new cooking recipes. He will join NITMB in 2026.

Giulia Garcia Lorenzana is a theoretical physicist working at the interface between the Statistical Physics of Disordered Systems and complex biological systems, ranging from Theoretical Community Ecology to Neuroscience. She leverages tools developed for randomly interacting spins to study systems composed of interacting species or neurons. She also draws inspiration from the non-equilibrium features of biological systems, such as non-reciprocal interactions, to define novel classes of models with emergent properties that could be widespread in living systems. At NITMB, Giulia is studying how spatial structure impacts the spontaneous activity of biological neural networks.

Aditya received his PhD in physics at Stanford University with interests in statistical and biological physics. As a PhD student Aditya he worked on understanding the evolution of biodiversity through mathematical models. How do ecological dynamics, from resource competition to host-pathogen interactions, influence the trajectory of evolution? What evolutionary forces, from drift to selection to recombination, lead to the immense biodiversity we see across vast spatiotemporal scales in nature? What are mechanisms for the coexistence of fine-scale diversity and how can we understand these theoretically?

Gabe Salmon is widely interested in how organisms spend energy and exert surprisingly decentralized control in high dimensional spaces. Precisely what new mathematical and biological behaviors are unlocked as cells—and their collectives—operate out of equilibrium? Working closely with experimentalists, he is motivated to build human-friendly mathematical tools for thinking about these new facilities, for instance in microbial ecosystems and for counting problems in biological guises. Gabe performed his doctoral work investigating gene regulation out of equilibrium and energy-limited cellular physiology with Rob Phillips at Caltech, following an undergraduate in Physics and Chemistry at Oberlin College. Gabe received his PhD from Caltech.

Mariya Savinov received their PhD in Mathematics from New York University’s Courant Institute of Mathematical Sciences under the supervision of Prof. Alex Mogilner. In their research, Savinov has used ideas from viscoelastic mechanics, fluid dynamics, percolation theory, and active matter to develop biomechanical models which reveal the role of friction, motor stress, and system size in actomyosin network dynamics. At the NITMB, Savinov seeks to develop new mathematical modeling approaches to investigate the underlying principles of adaptive collective dynamics of multicellular systems, generating experimentally testable predictions to explore with collaborators working on eukaryotic and prokaryotic model systems.

Li Shen studies biological systems through the lens of mathematics and computation, with a focus on discovering structural principles and providing mathematical understanding across scales. He received his PhD in Mathematics from Michigan State University under the supervision of Guo-Wei Wei. His research integrates algebraic topology, geometric topology, and machine learning to construct quantitative, multiscale topological representations of biological structures and interactions. At NITMB, Shen aims to extend these approaches to a broader range of biological systems, enabling predictive modeling and experimentally testable insights.

Adrianne Zhong is broadly interested in studying the diverse, dynamical behavior of biological systems through the lens of geometry, in particular the geometry of stochastic processes. Zhong received her PhD at UC Berkeley in physics under the supervision of Prof. Michael R. DeWeese, investigating the relationship between nonequilibrium stochastic thermodynamics and optimal transport theory. Before that, she researched nonneutral plasma physics with Prof. Joel Fajans also at UC Berkeley.