4月14日
研讨会, 演讲, 讲座
CHEM/OCES Seminar - Understanding the Quantum Mechanical Effects in Biochemical Reactions with Multiscale Simulation
Speaker: Dr. Ruibin LIANG
Institution: Department of Chemistry, Stanford University, USA
Hosted by: Professor Ian D. WILLIAMS
4月10日
研讨会, 演讲, 讲座
Seminar on Statistics and Data Science - Gaussian Differential Privacy, with Applications to Deep Learning
Privacy-preserving data analysis has been put on a firm mathematical foundation since the introduction of differential privacy (DP) in 2006. This privacy definition, however, has some well-known weaknesses: notably, it does not tightly handle composition.
2020年3月27日 – 4月30日
比赛
Chun Wo Innovation Student Awards: Engineers for a Smarter Future
Competition:
Chun Wo Innovation Student Awards
– Engineers for a Smarter Future
Organized by:
Chun Wo Development Holdings Limited
3月26日
研讨会, 演讲, 讲座
Seminar on Statistics andData Science - Knockoffs with Side Information
We consider the problem of assessing the importance of multiple variables or factors from a dataset when side information is available.
3月26日
研讨会, 演讲, 讲座
CHEM - PhD Student Seminar - Halide Perovskite-based Resistive Random Access Memory (ReRAM) and Artifical Synapses
Student: Mr. Lingeswaran ARUNAGIRI
Department: Department of Chemistry, HKUST
Supervisor: Professor Henry YAN
3月25日
研讨会, 演讲, 讲座
Seminar on Applied Mathand Data Science - Accelerated Outlier Detection in Low-Rank and Structured Data: Robust PCA and Extensions
We study robust PCA for the fully observed setting, which is about separating a low rank matrix L and a sparse matrix S from their sum D=L+S.
3月25日
研讨会, 演讲, 讲座
Seminar on Applied Mathand Data Science - Online robust matrix factorization for dependent data streams
Online Robust Matrix Factorization (ORMF) algorithms seek to learn a reduced number of latent features as well as outliers from streaming data sets.
3月20日
研讨会, 演讲, 讲座
Seminar on Statistics and Data Science - Spectral methods for latent variable models
Latent variable models lay the statistical foundation for data science problems with unstructured, incomplete and heterogeneous information. Spectral methods extract low-dimensional geometric structures for downstream tasks in a computationally efficient way.
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