CUHK Information Day 2026

Venue
5/F podium & Room 603. Ho Sin-Hang Engineering Building, CUHK
Date: 
Friday, October 16, 2026
Time
Saturday, October 17, 2026 to 18:00
e_title: 
CUHK Information Day 2026
Not Available
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[Recruitment Talk] Airport Authority Hong Kong - Promotion of the IT Trainee Programme

Venue
Online via Microsoft Teams
Date: 
Thursday, October 15, 2026
Time
Monday, November 2, 2026 to 17:00
e_title: 
[Recruitment Talk] Airport Authority Hong Kong - Promotion of the IT Trainee Programme
Not Available
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[Career Booth] HSBC Technology Centre China Emerging Talent Programme 2027

Venue
5/F Podium, Ho Sin Hang Engineering Building, CUHK
Date: 
Wednesday, October 14, 2026
Time
Thursday, October 22, 2026 to 16:00
e_title: 
[Career Booth] HSBC Technology Centre China Emerging Talent Programme 2027
Not Available
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[Recruitment Talk] Huawei Recruitment Talk - CUHK Faculty of Engineering Special Session

 

Venue
SHB Reading Room (Room 603), 6/F, Ho Sin Hang Engineering Building, CUHK
Date: 
Wednesday, October 14, 2026
Time
Monday, October 26, 2026 to 17:00
e_title: 
[Recruitment Talk] Huawei Recruitment Talk - CUHK Faculty of Engineering Special Session
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[Recruitment Talk] HKSTP “AI for All” Inclusive Programme - Talent Foundry

Venue
LT3, Lady Shaw Building (LSB LT3), CUHK
Date: 
Wednesday, October 14, 2026
Time
Friday, October 23, 2026 to 16:00
e_title: 
[Recruitment Talk] HKSTP “AI for All” Inclusive Programme - Talent Foundry
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Name: 
LAI Yao
Title ( post ): 
Research Assistant Professor
Department: 
Department of Computer Science and Engineering
email: 
ylai [at] cse.cuhk.edu.hk
phone: 
3943 3276
Avatar: 
Class: 
faculty_member
Chinese Name: 
來耀
glossary_index: 
L
Name: 
LI Boyi
Title ( post ): 
Assistant Professor
Department: 
Computer Science and Engineering
email: 
boyilics [at] cse.cuhk.edu.hk
phone: 
3943 8396
Avatar: 
Class: 
faculty_member
Chinese Name: 
李柏依
glossary_index: 
L

[Recruitment Talk] 2027 CLP Engineering Graduate Trainee Programme

Venue
Room 603 (The Reading Room), Ho Sin Hang Engineering Building, CUHK
Date: 
Tuesday, September 29, 2026
Time
Tuesday, October 13, 2026 to 16:00
e_title: 
[Recruitment Talk] 2027 CLP Engineering Graduate Trainee Programme
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Professor Zhang Li’s team develops AI-driven magnetic tracking technology to advance clinical use of miniature medical robots

Date: 
2026-09-25
Thumbnail: 
Body: 

A research team led by Professor Zhang Li from the Department of Mechanical and Automation Engineering has developed an artificial intelligence (AI)-driven magnetic tracking technology. This innovation overcomes a long-standing global bottleneck in real-time micro-robot tracking, enabling the real-time localisation and closed-loop navigation of millimetre-scale wireless magnetic robots during magnetic actuation, paving the way for ultra-precise, minimally invasive therapies.

The research findings have been published in Science Robotics, the world’s leading and most prestigious journal in the field of robotics. This major breakthrough enables tiny wireless robots to be guided safely and precisely through confined and hard-to-access anatomical regions, including blood vessels, bile ducts and the spinal subarachnoid space, and could help advance their clinical translation for minimally invasive diagnosis and therapy. Notably, this study marks the sixth research paper by Professor Zhang’s team published to be published in the journal, underscoring CUHK’s sustained leadership in miniature and medical robotics.

Overcoming the “blind navigation” challenge in micro-robotics

Minimally invasive procedures play a critical role in modern healthcare, enabling diagnosis and treatment in hard-to-reach regions of the body while reducing patient trauma. Miniature wireless magnetic robots offer immense potential for next-generation minimally invasive medicine because they can be remotely guided by magnetic fields into confined anatomical environments that are difficult to reach using conventional medical devices or without open surgery.

Despite their potential, however, the clinical translation of miniature magnetic robots has been hindered by the difficulty of determining their precise location inside the body while they are being magnetically driven. Reliable real-time tracking is essential for guiding them safely to target sites. However, existing tracking technologies are difficult to apply because of the robots’ miniature size, wireless nature and the strong magnetic fields required for actuation.

To overcome this “blind navigation” challenge, the CUHK research team engineered an AI-driven magnetic tracking platform that enables real-time localisation of millimetre-scale wireless magnetic robots during magnetic actuation. The system achieved millimetre-level localisation accuracy at penetration depths of up to 10 centimetres inside biological tissue. The team further integrated the real-time tracking information into the actuation system to enable closed-loop navigation.

The technology was further validated in a large animal model, where miniature magnetic robots were navigated through the spinal subarachnoid space and bile duct of a goat. The experiments were conducted in an unshielded clinical environment with various sources of magnetic interference, and the estimated trajectories showed close agreement with X-ray observations. These results demonstrate robust in vivo closed-loop navigation of miniature magnetic robots in clinically relevant environments and represent an important step towards their clinical translation.

Professor Zhang Li said: “One of the key barriers to the clinical translation of miniature medical robots is the inability to track them accurately in real time while they are being actuated inside the body. Our platform introduces an AI-based approach to suppress actuation-induced interference, enabling reliable closed-loop navigation of millimetre-scale robots in clinically relevant environments. This brings miniature medical robots a major step closer to practical clinical applications in minimally invasive diagnosis and treatment.”

This milestone reflects CUHK’s robust innovation ecosystem and multi-institutional support. This study was supported by the Hong Kong Jockey Club Charities Trust through the JC STEM Early Career Research Fellowship, the Croucher Foundation, the Research Grants Council of Hong Kong (RGC), as well as the SIAT–CUHK Joint Laboratory of Robotics and Intelligent Systems and the Multi-scale Medical Robotics Center (MRC) under the InnoHK research platform.

 

Source: https://www.cpr.cuhk.edu.hk/en/press/cuhk-develops-ai-driven-magnetic-tracking-technology-to-advance-clinical-use-of-miniature-medical-robots/

A research team led by Professor Zhang Li from CUHK’s Department of Mechanical and Automation Engineering developed an artificial intelligence (AI)-driven magnetic tracking technology.

Professor Zhang and his research team's innovation enables real-time localisation and closed-loop navigation of millimetre-scale wireless magnetic robots during magnetic actuation, paving the way for ultra-precise, minimally invasive therapies.

 

The technology was tested in a goat model, where miniature magnetic robots were navigated through its spinal subarachnoid space and bile duct in an unshielded clinical environment with various sources of magnetic interference.

 

From the goat-model experiment, the estimated trajectories showed close agreement with X-ray observations. These results demonstrate robust in vivo closed-loop navigation of miniature magnetic robots in clinically relevant environments and represent an important step towards their clinical translation.

 

 

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Media Release

AI醫療|中大研發AI磁追蹤技術 助高精準微創診斷治療

香港中文大學成功研發一套由AI驅動的磁追蹤技術,能夠在磁場驅動毫米級無綫磁性微型機械人的同時,進行實時定位及體內閉環導航,系統在生物組織探測深度達10厘米的情況下仍可實現毫米級定位精準度,為高精準的微創診斷及治療開拓新方向。

Date: 
Thursday, September 24, 2026
Media: 
hket.com

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