更多科大概览
5月14日
研讨会, 演讲, 讲座
MATH_PhD Student Seminar - Cross-population genetic prediction by harnessing the shared genetic basis between ancestries
The polygenic risk score (PRS) derived from the genome-wide association studies (GWASs) predicts the individualized genomic predisposition to complex traits/diseases.
5月13日
研讨会, 演讲, 讲座
MATH_PhD Student Seminar - Identification of genetic signatures of human face by AI-Driven facial phenotyping
Traditional human face genome-wide association studies (GWASs) are limited by small sample size and inefficient landmarks-based phenotyping methods.
5月13日
研讨会, 演讲, 讲座
MATH_PhD Student Seminar - Perfectly matched layer for optical modes
We develop the method of perfectly matched layer (PML) for the calculation of optical modes in microcavities. The PML is constructed by implementing the complex coordinate transformation in the far field region.
5月13日
研讨会, 演讲, 讲座
MATH_PhD Student Seminar - Computational Resolution Limit: a Modern View of a Classical Problem
Given an image generated by the convolution of point sources with a bandlimited function, the inverse problem is to reconstruct the source number, positions, and amplitudes. It is well-known that it is impossible to resolve the sources when they are close enough in practice.
5月13日
研讨会, 演讲, 讲座
CHEM - PhD Student Seminar - Material Engineering in Thin-Film Transistor
Student: Mr. Chen HU
Department: Department of Chemistry, HKUST
Supervisor: Professor Shihe YANG
5月9日
会议, 座谈会, 论坛
Symposium on Risk Management and Business Intelligence 2020 - Applying AI in Business
The Symposium on Risk Management and Business Intelligence 2020 will be held on 9 May 2020 (Saturday) afternoon by ZOOM.
5月8日
研讨会, 演讲, 讲座
Seminar on Statistics and Data Science - Clustering via Uncoupled Regression (CURE)
In this talk, we first consider a canonical clustering problem where one receives unlabeled samples drawn from a balanced mixture of two elliptical distributions and aims for a classifier to estimate the labels.
5月7日
研讨会, 演讲, 讲座
Seminar on Data Science and Applied Mathematics - Partial Differential Equation Principled Trustworthy Deep Learning
This talk contains two parts: In the first part, I will present some recent work on developing partial differential equation principled robust neural architecture and optimization algorithms for robust, accurate, private, and efficient deep learning.
浏览理学院过往举办的活动。