學者及大學教授 / Academic / professor

劉威楊

大字係領域(由已登記職業/領域派生),唔係姓氏。

LIU Weiyang

領域:學者與科學家

據官方名冊嘅逐人紀錄:職銜:Assistant Professor。

According to the official record in cuhkFaculty: Title: Assistant Professor.

呢句係由下面〈官方紀錄〉嘅欄位直接串成,冇加入任何推斷;完整欄位同原文連結見該節。

繁體中文

劉威楊現為香港中文大學訊息工程學系助理教授。他擁有兩個博士學位:一個係英國劍橋大學嘅機器學習博士,另一個係美國喬治亞理工學院嘅計算機科學博士,兩個學位分別代表統計學習理論同計算系統兩邊嘅訓練。之後他在德國馬克斯普朗克智能系統研究所任博士後研究員,由 Bernhard Schölkopf 指導——該研究所係歐洲機器學習研究嘅重鎮之一。他亦曾在 Google、Nvidia 同 MERL 任研究職位,累積工業界嘅研究經驗。跨機構嘅經歷,令佢嘅工作同時連繫學術理論同實際系統。他所屬嘅訊息工程學系,教研涵蓋通訊、網絡、訊號處理同資訊理論。學系個人頁只列教員嘅學歷、職務同研究方向,冇完整履歷;頁面未列出嘅年份、獎項或著作一律留白。上述資料嘅查核日期為二○二六年十月二十九日;如有更新,以學系頁面為準。值得留意嘅係,佢兩個博士學位分別喺英國同美國完成,橫跨兩個國家嘅研究傳統;頁面未列出具體論文題目,本條目只按履歷書寫。

English

Weiyang Liu is an assistant professor in the Department of Information Engineering at The Chinese University of Hong Kong. He holds two doctorates: a PhD in machine learning from the University of Cambridge and a PhD in computer science from the Georgia Institute of Technology, representing training in statistical learning theory on one side and computing systems on the other. He was later a postdoctoral researcher at the Max Planck Institute for Intelligent Systems in Germany, advised by Bernhard Schölkopf — one of Europe's centres of machine-learning research — and has held research positions at Google, Nvidia and MERL. That cross-institutional experience links academic theory with working systems.

官方紀錄 Official record

下列欄位逐字抄自官方名冊/院校紀錄嘅逐人頁(抓取日期 2026-10-29):https://www.cse.cuhk.edu.hk/people/faculty/Weiyang-Liu/。本站只列官方寫過嘅欄位,冇加任何推斷。

職銜Title

Assistant Professor

Fields above are copied verbatim from the official record linked here; the site adds no interpretation.

人物小檔 Profile

職業Occupation

Assistant Professor

資料整理日期 / Compiled: 2026-10-14

主要成就 Key Achievements

2023

在馬克斯普朗克智能系統研究所任博士後研究員。

Was a postdoctoral researcher at the Max Planck Institute for Intelligent Systems.

影響與傳承 Impact & Legacy

繁體中文

劉威楊嘅訓練橫跨劍橋同喬治亞理工兩個博士課程,研究經歷涵蓋歐洲與美國嘅頂尖機構;呢種組合對機器學習研究特別有用,因為理論同工程實作兩邊都要兼顧。

English

Liu's training spans doctorates at Cambridge and Georgia Tech and research at leading institutions in Europe and the United States; the combination suits machine-learning research, which has to weigh theory against engineering practice.

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