The Department is committed to conducting impactful multi-disciplinary research through recognising our strategic strengths and pooling talents. To further advance our research and spearhead innovative across disciplines, an array of initiatives which include strengthening international exposure and pursing international scholarly recognition, fostering a more open and supportive research culture, increasing engagement of multi-disciplinary research, recruiting and retaining high-caliber staff are well introduced.
The Department is proud to count among our faculty and they contribute fundamentally to our success.
All Areas
#5
Hong Kong
#41
Asia
#149
World
Artificial Intelligence
#3
Hong Kong
#32
Asia
#71
World
Databases
#3
Hong Kong
#8
Asia
#18
World
The Department has secured significant amount of research funds in the past 5 years. The funding solidifies our research strengths and recognises our efforts in creating impactful innovative research.
| Funding Schemes | |
|---|---|
| No. of Funded Project(s) | |
| Collaborative Research Fund (CRF) | 3 |
| General Research Fund (GRF) | 20 |
| Early Career Scheme (ECS) | 6 |
| Research Impact Fund (RIF) | 3 |
| RGC Senior Research Fellow Scheme (SRFS) | 1 |
| Innovation and Technology Fund (ITF) | 2 |
| National Natural Science Foundation of China (NSFC) | 9 |
| National Natural Science Foundation of China/RGC Joint Research Scheme | 2 |
| Research Matching Grant Scheme (RMGS) | 6 |
| Others | 24 |
| Total | 76 |
| Prof. CHEN, Li |
| Associate Editor |
| Prof. CHEN, Li |
| Editor-in-Chief |
| Dr. ZHANG, Eric Lu |
| Editorial Board Member |
| Prof. LIU, Jiming |
| Associate Editor |
| Prof. LIU, Jiming |
| Associate Editor |
| Dr. DAI, Henry Hong Ning |
| Technical Committee |
Information as at 8 February 2024
The Department strongly believes that the significance of up-to-date research facilities as a pre-requisite for top research and teaching. High-performance computing facilities, like FAT CPU, FAT GPU servers and GPU clusters are well equipped to enhance our research capabilities and meet ever-expanding research need on artificial intelligence and big data.
List of high-performance computing facilities:
| 12 FAT CPU servers with at least 1TB RAM | ||
| 3 FAT GPU servers, each with 4 Nvidia V100 GPUs and 1TB RAM | ||
| 2 FAT GPU servers, each with 4 Nvidia Ampere A100 GPUs and 1TB RAM | ||
| Three GPU clusters: | ||
| Cluster 1: |
4 GPU nodes, each with two Nvidia Ampere A100 1 GPU node, each with eight Nvidia Ampere A100 3 GPU nodes, each with four Nvidia Tesla V100 8 GPU nodes, each with two Nvidia Tesla K80 4 GPU nodes, each with two Nvidia Tesla P40 |
|
| Cluster 2: |
2 GPU nodes, each with two Nvidia Ampere A100 5 GPU nodes, each with four Nvidia Tesla V100 |
|
| Cluster 3: |
2 GPU nodes, each with eight Nvidia Ampere A100 6 GPU nodes, each with four Nvidia Tesla V100 |
|
| One BigDATA cluster: | 5 data nodes, each with dual 6-core Intel Xeon CPUs | |
| HUAWEI GPU: | 1 server with Intel Xeon Gold CPUs and eight Neural Network Processing Units | |
