My research is primarily focused on developing mechanistic stochastic models of molecular motion, biomechanics, and chemistry in micron-scale environments such as cells and extracellular polymer matrices. Cells function through a highly complex set of interconnected chemical and mechanical processes, and on the micron scale, thermal fluctuations cause random molecular motion and impose random mechanical fluctuations on biopolymers. Hence, cellular processes are intrinsically complex, nonlinear, and stochastic. Mechanistic stochastic models are quickly becoming essential for understanding the micron-scale machinery of living organisms. One example from my work is the kinetic coupling between stochastic molecular motors and microtubules to boost and guide otherwise slow and random diffusive transport of cargo across the cell. Similar kinetic and diffusive processes are at play in extracellular mechanisms, e.g., antibody-based viral immunity. The utility of any mathematical model is deeply dependent on experimental validation of model predictions.

More recently, my research interests have evolved to address a major outstanding problem for stochastic modelers: access to sufficient experimental data for systematic statistical inference. Most labs are capable of efficiently acquiring terabytes of microscopy videos that image the motion of submicron molecular species. Currently, there is a prohibitively expensive and slow bottleneck in processing videos into time-series particle paths. Despite our excellent understanding of the optical physics of imaging of florescent particles, particle tracking still requires mundane human-assisted identification of particles. To address this challenge I have developed fully automated imaging methods using artificial neural networks, which are trained for pattern recognition tasks with machine learning.

Sam Lai, Ph.D., received his B.S. in Chemical and Biomolecular Engineering from Cornell in 2003, and Ph.D. in Chemical and Biomolecular Engineering at Johns Hopkins in 2007. He is currently an Associate Professor in the Division of Pharmacoengineering and Molecular Pharmaceutics.

Lai has 15+ years of experience in particle tracking, with dozens of publications and thousands of citations specifically on applications using particle tracking. His discoveries using particle tracking has launched multiple startup companies, including one that has completed multiple successful Phase II and III clinical trials.

M. Gregory (Greg) Forest is a Distinguished Professor of Mathematics at UNC Chapel Hill with joint appointments in Biomedical Engineering and Applied Physical Sciences. Greg received his PhD in Mathematics at the University of Arizona in 1979 and served on the faculty at Ohio State University from 1979-1996, when he was recruited to UNC to found and grow the applied mathematics program, now one of the premier programs in the U.S.

Greg's mathematical research interests are anchored in mechanistic modeling, analysis, and computation of materials ranging from nanocomposites, liquid crystal polymers, complex fluids, and soft biological materials such as mucus in the lung, gut, and cervix. Greg's applied research interests include lung biology with applications to lung disease, drug delivery, and mucosal immunology, several aspects of molecular cell biology, and the design of nanocomposites with extreme performance properties. All of these biological and biomedical applications rely heavily on particle tracking. Greg's research has been funded continuously for nearly 40 years by the National Science Foundation, the National Institutes of Health, the Army Research Office, the Air Force Office of Scientific Research, and the Department of Energy. Forest has nearly 200 refereed publications and has mentored scores of PhD students and postdocs.

Undergrad at UNC-CH

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Undergrad at Duke