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理学院致力营造一个富活力的学习环境,并着重学术成就、创新及合作。
王殷厚
理学院院长
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研讨会, 演讲, 讲座 | 2023年06月29日
IAS / School of Science Joint Lecture - Regeneration of the Mouse and Human Vestibular System — A Balancing Act
  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.
研讨会, 演讲, 讲座 | 2023年06月23日
Department of Mathematics - Seminar on Applied Mathematics - Deep Particle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method
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.
No. 24
Science Focus
Science Focus由理学院本科生在教职员的指导下编写及设计。旨在透过有趣的科研文章以启发及培育学生于科学及科研发现的兴趣。
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理学院
本科生
课程
注入新元素,使课程更加多样化,并增加跨学科课程,培养学生独立探索的能力。
研究生
课程
紧贴最新的科技发展,为学生提供具备启发性思维的训练。
学术单位
化学系
生命科学部
数学系
海洋科学系
物理系
化学系
化学系的教职员既充满朝气,亦具备群策群力的团队精神。他们活跃于化学研究的各个领域,其研究成果更获得国际认同。
生命科学部
生命科学部旨在促进生物科学的科研和教育之发展。
数学系
数学系的两大基柱为追求卓越的研究成果及承诺提供高效优质的教学课程。
海洋科学系
本系旨在带领各方认识不同领域的海洋科技,包括有关海洋保育,气候变化,海洋资源管理,社会经济及可持续发展的基础概念和实践。
物理系
物理系的使命乃由教学、科研及创新这铁三角组成。
科目研究
突破科研界限,追求新发现及建立崭新的研究模式。