Abstract The inner ear vestibular apparatus consists of five sensory organs, functioning to detect head rotation and acceleration. Within each vestibular organ, sensory hair cells are mechanoreceptors required for function. Genetic mutations, ototoxins, and infection can cause vestibular hair cell degeneration and loss of organ function, manifested clinically as dizziness or vertigo. In mouse vestibular organs, the speaker and his research group have defined glial-like supporting cells as hair cell precursors with limited regenerative capacity. The speaker will present their ongoing work using cellular reprogramming approaches to enhance the degree of hair cell regeneration. Lastly, he will present their studies on human vestibular hair cell degeneration and implications for regeneration.   About the Speaker Prof. Alan G. CHENG received his BS in Biomedical Engineering at the Johns Hopkins University, graduating Phi Beta Kappa and Tau Beta Pi. He then received his MD degree from the Albert Einstein College of Medicine and graduated with distinction in research in otobiology. He pursued his residency training in Department of Otolaryngology-Head and Neck Surgery at the University of Washington. During residency, he undertook a two-year NIH-sponsored research fellowship investigating mechanisms of hair cell degeneration. After residency he sought fellowship training in pediatric otolaryngology in Boston Children's Hospital. In 2007, he joined the Department of Otolaryngology-Head and Neck Surgery at Stanford University as a surgeon-scientist. He is currently the Edward C. and Amy H. Sewall Professor in the School of Medicine of Stanford University and also a Professor of Otolaryngology-Head and Neck Surgery and of Pediatrics (by courtesy) there. Prof. Cheng’s clinical practice based at the Stanford Ear Institute and Lucile Packard Children’s Hospital focuses on otologic diseases including congenital hearing loss and cochlear implantation, and chronic ear diseases in the pediatric population. In parallel, his research program focuses on inner ear hair cell development and regeneration. He has received funding from the US National Institutes of Health, US Department of Defense, the American Otological Society, and the California Institute for Regenerative Medicine for this research endeavor. Prof. Cheng is the recipient of the 2008 American Otological Society Clinician-Scientist Award, the 2013 American Academy of Otolaryngology-HNS Foundation Honor award, and the 2015 Geraldine Dietz Fox Young Investigator Award. He was elected a Member of the Collegium Oto-Rhino-Laryngologicum Amicitiae Sacrum in 2022.   For Attendees' Attention Seating is on a first come, first served basis.

Poincar-Einstein manifolds are a class of noncompact Riemannian manifolds with a well- defined boundary at infinity. They appear as the framework of AdS/CFT correspondence in string theory and have been studied intensively. I will discuss some recent results relating the Yamabe invariant of the boundary and that of the interior. The talk is based on joint work with my student Zhixin Wang.

High dimensional partial differential equations (PDE) are challenging to compute by traditional mesh-based methods especially when their solutions have large gradients or concentrations at unknown locations. Mesh-free methods are more appealing; however, they remain slow and expensive when a long time and resolved computation is necessary. In this talk, we present DeepParticle, an integrated deep learning (DL), optimal transport (OT), and interacting particle (IP) approach through a case study of Fisher-Kolmogorov-Petrovsky-Piskunov front speeds in incompressible flows. PDE analysis reduces the problem to the computation of the principal eigenvalue of an advection-diffusion operator. Stochastic representation via the Feynman-Kac formula makes possible a genetic interacting particle algorithm that evolves particle distribution to a large time-invariant measure from which the front speed is extracted. The invariant measure is parameterized by a physical parameter (the Peclet number). We learn this family of invariant measures by training a physically parameterized deep neural network on affordable data from IP computation at moderate Peclet numbers, then predict at a larger Peclet number when IP computation is expensive. Our methodology extends to a more general context of deep learning stochastic particle dynamics. For instance, we can learn and generate aggregation patterns in Keller-Segel chemotaxis systems.