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啟發可持續的科研革新文化。

突破科研界限,追求新發現及建立嶄新的研究模式。

關於理學院
理學院致力發展最尖端的科研項目,力求取得突破性的研究成果,建立最新的科研典範。
在教學方面,全面而高質素的課程特別著重培養學生的恒毅力、好奇心和創意…
理學院致力營造一個富活力的學習環境,並著重學術成就、創新及合作。
王殷厚
理學院院長
活動
研討會, 演講, 講座 | 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.
研討會, 演講, 講座 | 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由理學院本科生在教職員的指導下編寫及設計。旨在透過有趣的科研文章以啟發及培育學生於科學及科研發現的興趣。
就讀
理學院
本科生
課程
注入新元素,使課程更加多樣化,並增加跨學科課程,培養學生獨立探索的能力。
研究生
課程
緊貼最新的科技發展,為學生提供具備啟發性思維的訓練。
學術單位
化學系
生命科學部
數學系
海洋科學系
物理系
化學系
化學系的教職員既充滿朝氣,亦具備群策群力的團隊精神。他們活躍於化學研究的各個領域,其研究成果更獲得國際認同。
生命科學部
生命科學部旨在促進生物科學的科研和教育之發展。
數學系
數學系的兩大基柱為追求卓越的研究成果及承諾提供高效優質的教學課程。
海洋科學系
本系旨在帶領各方認識不同領域的海洋科技,包括有關海洋保育,氣候變化,海洋資源管理,社會經濟及可持續發展的基礎概念和實踐。
物理系
物理系的使命乃由教學、科研及創新這鐵三角組成。
科目研究
突破科研界限,追求新發現及建立嶄新的研究模式。