Tissue engineering provides a viable means of regenerating bone and skeletal muscle following injuries. Our lab has developed advanced biomaterial-based approaches to promote recovery following volumetric bone and muscle loss. To further improve biomaterial design, we focused on developing a robust understanding of heterotypic cellular interactions that are critical for healing. Specifically, both bone and skeletal muscle are highly vascularized and innervated tissues. Consequently, angiogenesis and neural infiltration are critical processes underlying their functional regeneration. Through our quantitative lightsheet microscopy platform, which can image the entire tissues at single-cell resolution, we are mapping the neurovascular associations during homeostasis, aging, and biomaterial-mediated healing to determine remaining gaps. Combined with transcriptomic approaches, this data is employed to identify novel therapeutic targets for bone and skeletal muscle.

Temporal lobe epilepsy (TLE) is among the most common neurological disorders, yet about 30% of patients do not respond to existing medications and continue to experience seizures. To advance new therapies, I will present our recent work developing engineered phenotyping platforms for biomarker discovery in mouse models of TLE.

"How the Brain Reads Natural and Artificial Signals from Visual Cortex" with John Maunsell, PhD, Albert D. Lasker Distinguished Service Professor Department of Neurobiology and Director of the Neuroscience Institute, University of Chicago

“Your Brain in Motion: How Neuroimaging and Computer Vision Transform Our Understanding of Brain Biomechanics” seminar with Mehmet Kurt, PhD, Associate Professor of Mechanical Engineering, University of Washington

In this seminar...
"I will review recent uses of dynamical systems models to analyze and interpret neural population dynamics in monkey cerebral cortex. Viewed through the lens of dynamical systems theory (DST), the activity of single neurons are but noisy reflections of underlying latent variables that define and govern the collective activity of the neural population. By focusing on the collective level, DST analyses are bringing remarkable clarity and intelligibility to single neuron data sets that are otherwise deeply puzzling. These results raise contentious issues among neuroscientists and philosophers of neuroscience. Are DST models explanatory, or merely descriptive? If explanatory, do they provide mechanistic insight? I will review both sides of the argument."