CUHK | 2025 Fall

2024-09-21

学校概况

香港中文大学（The Chinese University of Hong Kong，缩写：CUHK），简称中大，是香港一所书院制公立研究型综合大学，成立于1963年，由崇基学院、新亚书院及联合书院合并而成。中大是香港首个研究型大学，并采用独特的书院制度。现有九所书院，包括创校前成立的三所书院（新亚书院、崇基学院、联合书院），以及后来成立的逸夫书院、晨兴书院、善衡书院、敬文书院、伍宜孙书院和和声书院。

中大共有八个学术学院：文学院、理学院、社会科学院、工商管理学院、医学院、教育学院、工程学院及法律学院，并设有研究院，提供硕士及博士课程。中大在社会科学、国学、医学等领域具有强大的研究实力，同时也是香港的天灾监测中心之一。

2014年，香港中文大学在广东省深圳市设立了香港中文大学（深圳），进一步扩展其学术影响力。中大一直位居全球顶尖大学之列，现QS全球排名第36名。

中大的校友和教授中有11位诺贝尔奖得主、1位菲尔兹奖得主和2位图灵奖得主，此外还产生了多个国际奖项得主。中大本科毕业生获得罗德奖学金的数目为大中华地区最多，学校的环球医学课程录取了香港最优秀的学生。

MPhil – PhD in Computer Science and Engineering

The Division of Computer Science and Engineering offers a part-time and full-time articulated M.Phil.-Ph.D. program in Computer Science and Engineering. This programme consists of two research oriented streams: M.Phil. and Ph.D. All full-time research students will receive Studentships during their normative study period.

The Programme is research-oriented. For the M.Phil. stream, the normative period of full-time study is 2 years. For the Ph.D. stream, the normative period for full-time study is 3 years for students with a research master’s degree and 4 years for students without a research master’s degree. All students in the Ph.D. stream will be admitted initially with a ‘pre-candidacy’ status. Upon satisfying the division’s candidacy requirements (coursework requirement, candidacy examination, and thesis proposal and oral defence), a student will be allowed to progress to the ‘post-candidacy’ status.

Admission of the M.Phil.-Ph.D. programme in Computer Science and Engineering is divided into two phases; the Early Admission (April – July) and Regular Admission (September – March). If you are applying our Ph.D. Programme, you are encouraged to apply for the Hong Kong Ph.D. Fellowship Scheme which provides a monthly stipend of HK$28,100.

Tuition Fee : HK$44,500 per annum for 2025-2026 intake regardless of the study mod (Subject to University’s approval and review)

CSE

账号：abinzzz1227@gmail.com
密码：cyb021124.
https://www.gradsch.cuhk.edu.hk/OnlineApp/amend_app_form.aspx

IE

Department of Computer Science and Engineering

Qi DOU

Dr. Qi DOU is an Assistant Professor with the Department of Computer Science & Engineering, and co-affiliated with T Stone Robotics Institute, at The Chinese University of Hong Kong. Her research interest lies in the interdisciplinary area of artificial intelligence and healthcare with expertise in medical image analysis and robotic surgery, with the mission to advance disease diagnosis and minimally invasive intervention via machine intelligence. In this area, she has published over 80 papers in top-tier conferences and journals with 8 ESI highly cited articles. Dr. Dou has won the IEEE ICRA Best Paper Award in Medical Robotics in 2021, Hong Kong Institute of Science Young Scientist Award in 2018, CUHK Engineering Faculty Outstanding Thesis Award in 2018, Best Paper Award of Medical Image Analysis-MICCAI in 2017, Best Paper Award of Medical Imaging and Augmented Reality in 2016, MICCAI Young Scientist Award Runner-up in 2016. Dr. Dou serves as the Programme Chair of MIDL 2021, SPC of AAAI 2021 and IJCAI 2021, Organization Committee of a series of workshops on healthcare and medical imaging related topics in top conferences such as NeurIPS, ICML, CVPR, ICCV, MICCAI. Before joining CUHK faculty, Dr. Dou was a postdoctoral research associate with BioMedIA Lab at the Department of Computing at Imperial College London. She received her Ph.D. degree in Computer Science & Engineering at CUHK in 2018, and Bachelor’s degree in Biomedical Engineering at Beihang University in 2014.

Research Interests:
My research is at the interdisciplinary field of medical image analysis, machine learning and robotic surgery intelligence, aiming to create synergistic advancements for innovative intelligent systems that achieve an impact to support delivery of higher-quality medical diagnosis, intervention and education via next-generation healthcare technology. Previous representative works include 3D deep learning for image image computing, model generalizability and FL on heterogeneous medical data, surgical video analysis for real-time cognitive assistance, and SurRoL.

Recent focus:

1. embodied intelligence for surgical robots,
1. smart XR for medical education,
1. multi-modal AI for healthcare applications,
1. robot sensing and learning in dynamic environments,
1. robot planning and smart object manipulation.

Publication:

[08/2024] One paper accepted to MedIA
[08/2024] One paper accepted to IEEE RA-L
[07/2024] Two papers accepted to ECCV 2024
[07/2024] Paper on EndoNeRF’s application to data-driven surgical simulation won the IJCARS-MICCAI 2022 Special Issue Best Paper Award 3rd Prize
[06/2024] Eight papers accepted to MICCAI 2024
[06/2024] One paper accepted to NEJM AI (The New England Journal of Medicine)
[06/2024] One paper accepted to IEEE TMI and one paper accepted to MedIA
[05/2024] To serve in the Organization Committee of IROS 2025
[05/2024] Survey paper on organ deformation modeling in AR-guided surgery accepted to Journal of Computer Assisted Surgery
[04/2024] Three papers accepted to IEEE TMI
[04/2024] One paper accepted to Gastroenterology (IF: 29.4)
[03/2024] To serve as Associate Editor for IEEE RA-L
[03/2024] To serve as Area Chair of NeurIPS 2024
[02/2024] Two papers accepted to CVPR 2024
[01/2024] One paper accepted to ICLR 2024

Openings (positions available all year round):
Postdoctoral Fellow / Research Associate:
1.Multiple positions are open to apply for being involved in projects on machine learning for medical image analysis.
Specific responsibilities and requirements are as follows:
1) Coordinate a multidisciplinary team of engineering researchers and clinical collaborators for the development of smart medical image diagnosis, detection, segmentation, prognosis and clinical decision-making systems.
2) Develop cutting-edge ML methods in terms of model generalizability, robustness, interpretability and human-in-the-loop test-time mechanisms for smart healthcare systems.
3) Generate high-quality papers to be published in top-tier conferences and high-impact journals.
4) Require a Ph.D. with good publication record in medical imaging, computer vision, AI/ML, statistics, or other related fields.
5) Experience in multi-modal learning, image segmentation, causality, and/or data-efficient learning are preferred.
6) Experience in processing medical endoscopy/MRI/CT/Ultrasound images are preferred.
7) Employment contract can be flexible and up to 3 years.

2.Two positions are open to apply for being involved in a project on robotic surgery intelligence.
Specific responsibilities and requirements are as follows:
1) Work together with experts of computer scientists, surgeons, roboticists to develop a novel AI+XR system to change the way of robotic surgery education, in terms of how novices perceive, visualize and interact with surgical robots during training.
2) Develop image-based robotic surgery perception, stereo reconstruction, reinforcement learning and planning techniques, and integrate them into visualization HMD, haptic devices and/or robotic platforms for evaluation and demos.
3) Generate high-quality papers to be published in top-tier conferences and high-impact journals.
4) Require a Ph.D. with good publication record in medical imaging, computer graphics, human-computer interaction, surgical robotics, or other related fields.
5) Experience in augmented reality, virtual reality, simulation, visualization system integration and development are preferred.
6) Experience working with Unity, Bullet, SOFA, Omniverse, OpenGL, TCP, UDP Socket and other relevant tools are preferred.
7) Employment contract can be flexible and up to 3 years.

3.Two positions are open to apply for being involved in a project on robotic vision and learning-based automation.
Specific responsibilities and requirements are as follows:
1) Work together with local and international experts of computer scientists and roboticists to explore learning-based robotic perception and automation for applications including but not limited to surgical robots.
2) Explore solutions for defined novel research problems and create cool demos.
3) Generate high-quality papers to be published in top-tier conferences and high-impact journals.
4) Require a Ph.D. with good publication record in robotic vision and learning, mechanical and automation engineering, or other related fields.
5) Experience in RL, imitation learning, sim2real transfer, 6D pose estimation, path planning are preferred.
6) Employment contract can be flexible and up to 3 years.

Research Assistant:

Multiple RA positions are available for all the above projects.
Specific responsibilities and requirements are as follows:
1) Work together with senior team members on research and development projects on AI for medical imaging and robotics.
2) Collect and process relevant data, deploy AI algorithms for inference, integrate systems for testing and demo.
3) Generate high-quality papers to be published in top-tier conferences and high-impact journals.
4) Require excellent communication skills and proficient in at least one programming language.
5) Employment contract can be flexible and up to 2 years, but minimum 6 months.

Alumnus
Ph.D. students:

Kai Chen (2024 PhD)
Meirui Jiang (2024 PhD)
Yonghao Long (2024 PhD)
Wenao Ma (2024 PhD, co-advise with Prof. Tiffany So),Current: Huawei
Bin Li (2024 PhD, co-advise with Prof. Yinhui Liu)
Ziyi Wang (2024 PhD, co-advise with Prof. Yinhui Liu)
Jinpeng Li (2024 PhD, co-advise with Prof. Pheng Ann Heng)
Yang Yu (2024 PhD, co-advise with Prof. Pheng Ann Heng)
Quande Liu (2022 PhD, co-advise with Prof. Pheng Ann Heng), CUHK Young Scholars Thesis Award 2022, Current: Tencent
Cheng Chen (2021 PhD, co-advise with Prof. Pheng Ann Heng), Current: Post-doc at Harvard Medical School
Xiaojie Gao (2021 PhD, co-advise with Prof. Pheng Ann Heng), Current: Post-doc at Hong Kong Centre for Logistics Robotics
Xueying Shi (2021 PhD, co-advise with Prof. Pheng Ann Heng), Current: Huawei

Interns/RAs/Postdocs:

Dr. Bingchen Gong (2023-24 Postdoc), Ph.D. at HKU - Our Lab - Postdoc at Ecole Polytechnique
Dr. Jianfeng Cao (2023-24 Postdoc), Ph.D. at HKCityU - Our Lab -
Anjie Le (2023 RA), B.E. at Cambridge - Our Lab - MPhil at Cambridge
Yunqi Huang (2023 RA), B.E. at HIT - MPhil at NUS - Our Lab - Ph.D. at UCL
Jiaqi Liu (2023 RA), B.E. at SJTU - M.S. at SJTU - Our Lab - Ph.D. at U. of Trento
Yuliang Xiao (2023 RA), B.E. at Pitt & SCU - M.S. JHU - Our Lab - Ph.D. at U. of Toronto
Qiyao Xue (2023 RA), B.E. at PolyU - Our Lab - PhD at University of Pittsburgh (ECE)
Yuqi Gong (2022-23 RA), B.E. at FNU - M.S. at CUHK - Our Lab -
Chong Tong Chow (2022 summer intern), B.E. at PKU - Our Lab - M.S. at JHU
Zhuoyang Zou (2022 RA), B.E. at CUHK - Our Lab - M.S at Northwestern University - Ph.D. at PSU (CS)
Zihao Chen (2021 summer intern), B.E. at SJTU - Our Lab- Ph.D. at Duke (ECE)
Penghui Du (2021 summer intern), B.E. at BUAA - Our Lab- M.S. at BUAA (AI)
Yunshuang Li (2021 summer intern), B.E. at ZJU - Our Lab - M.Se. at Upenn (Robotics) - Ph.D. at USC (Robot learning)
Zirui Wang (2021 summer intern), B.E. at BUAA - Our Lab- M.Sc. at CUHK (IE) - Ph.D.at HKBU
Yedi Zhang (2021 summer intern), B.E. at HUST - Our Lab- Ph.D. at UCL (ML)
Junming Chen (2020 RA), B.E. at WHU - Our Lab - M.Phil. at HKUST (CSE) - Ph.D. at HKUST (CSE)
Xiaoyang Zou (2020 summer intern), B.E. at HUST - Our Lab - Ph.D. at SJTU (Institute of Medical Robotics)

Exchange Ph.D. students:

Jie Zhang (HUST, 2024) ,
Zelong Tan (THU, 2024),
Yongtao Zhang (SZU, 2024),
Mengyuan Jin (NPU, 2024),
Longfei Gou (SMU, 2024),
Manxi Lin (DTU, 2023),
Junhu Fu (Fudan Univ, 2023),
Yuexuan Xia (NPU, 2023),
Xinan Sun (Tianjin Univ, 2022)

Jinwei Gu

Research Areas:

Artificial Intelligence
Deep Learning
Machine Learning
Rich Media
Computer Vision
Computer Graphics
AR/VR

Irwin King

CUHK MISC Lab:
The Machine Intelligence and Social Computing (MISC) Lab is led by Prof. Irwin King. We focus on fundamental as well as applied computational techniques for the collaborative and collective intelligence of group behaviors on the Internet. Our research topics include, but are not limited to:

Web intelligence, web data mining, knowledge discovery on the web, web analytics, web information retrieval
Learning to rank, ranking algorithms, relevance feedback, collaborative filtering, recommender systems
Human/social computation, social games, opinion mining, sentiment analysis, models and theories about social networks, social marketing, monetization of the web
Large graph and link-based algorithms
Security/privacy issues related to web intelligence and social computing, etc.

Current Students:

Liwei Wang

I am an Assistant Professor in Computer Science and Engineering department at The Chinese University of Hong Kong (CUHK). Before coming to HK, I have worked for more than two years as a Senior Researcher in Tencent AI Lab at Bellevue, US.

I got my PhD from Computer Science Department, University of Illinois at Urbana-Champaign, advised by Prof. Svetlana Lazebnik. Here is my Short Bio.

The Language and Vision (LaVi) Lab, which I founded at the Department of Computer Science and Engineering at CUHK, conducts research in Natural Language Processing (NLP) and Computer Vision, with a particular emphasis on the intersection of vision and language. Our work encompasses a range of topics including Language+Vision, Large Language Models, Multi-modal Large Models, and Embodied AI. If you want to join LaVi Lab, please send an email to lwwang@cse.cuhk.edu.hk

MMLab

The CUHK Multimedia Lab (MMLab) is one of the pioneering institutes on deep learning. In GPU Technology Conference (GTC) 2016, a world-wide technology summit, our lab is recognized as one of the top ten AI pioneers, and listed together with top research groups in the world (e.g. MIT, Stanford, Berkeley, and Univ. of Toronto). Today, we remain one of the most active research labs in computer vision and deep learning, publishing over 40 papers on top conferences (CVPR/ICCV/ECCV/NIPS) every year.
Our lab has a large group of talented students, plenty of computational resources, and steady financial support, and free research environment.

MMLab@CUHK

Xiangyu Yue

I am currently an Assistant Professor in the Department of Information Engineering at Chinese University of Hong Kong, with the Multimedia Lab (MMLab). I received my Ph.D. from Electrical Engineering and Computer Science at University of California, Berkeley, working with Prof. Alberto Sangiovanni Vincentelli and Prof. Kurt Keutzer at Berkeley AI Research. I am broadly interested in various areas including but not limited to:

computer vision,
multi-modal learning,
generative models,
foundation model,
transfer learning,
domain adaptation,
interpretable systems, etc.

Prior to Berkeley, I received my M.S. degree from Stanford University and B.S. degree from Nanjing University. I have spent time at Google Research, Google Robotics, Baidu AI Research, and Tencent AI Lab. I received the prestigious Lotfi A. Zadeh Award for my research work.