Our Team

Matt’s research is focused on quantitative experimental and modeling approaches to gain programmatic control over cell differentiation and function. He applies mathematical modeling, machine learning, and statistical analysis to engineer and rewire cellular physiology and to synthesize new types of cells that don’t exist in nature. He also develops simplified cellular systems in which physical models can be applied to control geometry and morphology.

Matt is also the PI for the Single-Cell Profiling and Engineering Center (SPEC) which brings single-cell genomic capabilities to the Caltech campus and drives new technology development.

Shahriar is working on the physics of (synthetic) active materials. Using theory and simulations, he is trying to understand the mechanics of active networks that are driven by synthetic proteins of different properties, towards the ultimate goal of controlling the dynamics of active networks.

Yuning is interested in leveraging the AI tools of graph neural networks and generative models to characterize the spatial and temporal interactions within living systems. In parallel, he also commits to advancing AI techniques motivated by those biological challenges.

Joe is interested in exploring and repurposing innovative solutions evolved by microorganisms for a broad range of applications. Co-advised by Sarkis Mazmanian, his current projects include discovery of commensal microbiota immune-modulators and engineering of synthetic microbial consortia for bioremediation of environmental pollutants.

Alex explores intelligence through the lens of dynamical system theory and predictive coding. He is passionate about narrowing the gap between artificial and natural intelligence, drawing inspiration from neuroscience, mathematics, and physics.

James’ main research interest is understanding human intelligence and reasoning. Currently, he studies navigation as a building block for intelligence. Language and reasoning, for example, navigate an abstract, conceptual space. Using machine learning, neuroscience, and differential geometry, James Gornet studies how neural network may represent and solve these abstract navigation problems.

Shichen’s work is on combining concepts from physics, biology and engineering to decode non-equilibrium mechanisms in active matter. He is currently working on exploiting the self-organizing mechanisms of active matter for engineering programmable bio-material and to answer the question of “what is life”.

Co-advised by Professor Richard Murray, Zach’s research is at the interface of synthetic biology and machine learning. His work focuses on building open-source tools for creative protein engineering/discovery and applying said tools to guide the design of synthetic cells.
TRILL website: trill.readthedocs.io

Meera is interested in how tissues form and break down in disease contexts. She is developing an RL + cell-simulation-based framework to determine strategies to control tissue behavior in multicellular systems.

Arjuna is broadly interested in harnessing AI-based protein folding and structure prediction models to study the evolution of cellular systems, including cytoskeletal active matter. He is also developing next-generation protein design workflows informed by constraints on protein evolution.

Meng Wang is working at the interface between machine learning and neuroscience, particularly interested in framing the learning behavior in biology into rigorous computation, currently focus on Reinforcement Learning.

Timothy focuses on understanding how machine learning builds an understanding of the world through prediction and actions. More specifically, he examines the internal representation through a mathematical lens, aiming to form a unified theory that explains how a world model is formed, that can lead to real decision-making AI agents.