10 Oct 2025
Seminar, Lecture, Talk
IAS / School of Science Joint Lecture - Use of Large Animal Models to Investigate Brain Diseases
Abstract Genetically modified animal models have been extensively used to investigate the pathogenesis of age-dependent neurodegenerative diseases, such as Alzheimer (AD), Parkinson (PD), Huntington (HD) diseases, and Amyotrophic Lateral Sclerosis (ALS). The common feature of these diseases…
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19 Oct 2020
Seminar, Lecture, Talk
PHYS Seminar - Expert Sharing Seminar
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8 Oct 2020
Seminar, Lecture, Talk
PHYS Seminar - Collective Motion of Active Matter in Confinement
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6 Oct 2020
CHEM Seminar - Catalytic Enantioselective Redox-Neutral Reactions and Divergent Heterocycle Syntheses
Speaker: Professor Yu ZHAO Institution: Department of Chemistry, National University of Singapore Hosted by: Professor Jianwei SUN
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6 Oct 2020
Seminar, Lecture, Talk
PHYS Seminar - Heurisitic Machinery to Uncover Hidden Features of SU(N) Fermions with Neural Networks 
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29 Sep 2020
Seminar, Lecture, Talk
MATH - Seminar on Pure Mathematics - Cluster structurer on K-theoretic Coulomb branches, part II.
It was conjectured by Davide Gaiotto, that K-theoretic Coulomb branches of 3d N=4 SUSY gauge theories possess structure of cluster varieties. In this talk, I will explain how to attack this conjecture in the case of quiver gauge theories.
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25 Sep 2020
Seminar, Lecture, Talk
MATH - Seminar on Pure Mathematics - Introduction to Coulomb branches and their cluster structure
Braverman, Finkelberg and Nakajima have recently proposed a mathematical definition of the Coulomb branch of a 3d N=4 gauge theory of cotangent type, associating to each such theory a family of associative algebras deforming the algebra of functions on an affine Poisson
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11 Sep 2020
Seminar, Lecture, Talk
MATH - Seminar on Scientific Computation - Towards the high-fidelity computation and modeling of compressible turbulent flows
In this talk, we will focus on the high-fidelity computations of turbulence from the perspective of modeling and numeric.
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28 Aug 2020
Seminar, Lecture, Talk
MATH - Seminar on Statistics and Machine Learning - ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm
The theory and practice of stochastic optimization has focused on stochastic gradient descent (SGD) in recent years, retaining the basic first-order stochastic nature of SGD while aiming to improve it via mechanisms such as averaging, momentum, and variance reduction.
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