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At the single molecule level, experiments now follow the steps in the life's journey of a single protein; from its synthesis in a ribosome, to its activity in a complex dynamical environment, to its death by proteolysis. At the cellular level, experiments reveal with incredible detail how groups of proteins come together to regulate important events such as cell division. In an ideal world, experiments should not require much modeling to reveal physical insight -- the data should be self-evident. Yet biophysical data is noisy, complex and largely incomplete for a variety of reasons. This is especially true of data collected from live cells. On the theory side, we develop, adapt and use tools derived from statistics, statistical physics and stochastic processes, broadly defined, to understand living systems across multiple time and length scales. On this front, there are two main research directions in our group: 1) we develop methods to infer models from imaging and spectroscopy data in biophysics with a recent focus on Bayesian nonparametrics; 2) we are developing models to understand enzymatic and molecular motor efficiency. On the experimental front, we are exploring the role of hydrodynamics on the interaction of bacterial predators with their prey.
See "Projects" tab for a description of specific research directions.