NITMB Fellows

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. She obtained a PhD from La Sapienza University of Rome, with Prof. Irene Giardina as advisor. During her PhD, Federica worked on the development of inference methods for the collective dynamics of bird flocks and the quantification of irreversibility in polar active matter. In 2022, she joined the Chakraborty group at MIT to work with Prof. Arup Chakraborty and Prof. Mehran Kardar on the adaptive immune system. At NITMB, Federica is studying mechanisms of implicit regularization in population dynamics, inspired by affinity maturation and protein evolution.

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 biology, combining techniques from spectral methods, optimization, and dynamical systems theory with experimental data. At the NITMB, he works closely with experimental collaborators to develop data analysis and model inference frameworks that are broadly applicable across experimental datasets, with a particular focus on locomotion across scales from single-celled microorganisms to multi-limbed animals and datasets where only some dynamical variables are observed. Prior to joining the NITMB, Alasdair received his Ph.D. in applied mathematics in May 2024 from the Massachusetts Institute of Technology, where his advisor was Prof. Jörn Dunkel.

Xueying Wang is a theoretical physicist interested in non-equilibrium statistical mechanics and information theory. Building on her Ph.D. from the University of Illinois, Urbana-Champaign, where she modeled phase transitions and wave propagation in fluid turbulence and ecological systems, her current research focuses on how non-equilibrium thermodynamics governs biological complexity. Her work integrates analytical and computational techniques to investigate how thermodynamic constraints limit the ability of biological systems to predict and adapt, maintaining effective simplicity despite their high-dimensional, far-from-equilibrium nature.

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. Aditya is broadly interested in how evolution produces the wide range of forms and functions found in biology, from the structure and diversity of microbial communities to the mechanics and dynamics of proteins. What evolutionary forces produce the huge range of diversity found in microbes living in the same habitat? How does ecology constrain and shape this evolution? What are the mechanical constraints on the evolution of proteins, and what signatures does evolution leave on protein physics?

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 is an applied mathematician broadly motivated by biomechanical questions arising in cell biology. They are particularly interested in identifying the mechanisms by which multicellular systems, both eukaryotic and prokaryotic, exhibit robust collective behaviors. Savinov combines ideas from mechanics, dynamical systems, and active matter with data-driven techniques to develop mathematical modeling approaches which generate testable predictions they explore with experimental collaborators. Savinov received their PhD from New York University’s Courant Institute of Mathematical Sciences under the supervision of Prof. Alex Mogilner, where Savinov developed biophysical models to reveal the roles of friction, motor stress, and system size in actomyosin network dynamics.

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.