Result Announcement of the IDS Research Seed Funds 2023

Result Announcement of IDS Research Seed Funds 2023

We are pleased to announce the results of the IDS Research Seed Funds 2023 (IDS-RSF 2023)! After the vigorous assessment by the HKU IDS Steering Committee members, the following outstanding interdisciplinary research projects have been awarded.  The objective of the IDS Research Seed Funds is to strengthen the research synergy among IDS scholars and faculty members from other departments, and we look forward to witnessing the fruitful outcome of these collaborations.

Congratulations to all the awardees!

List of Awarded Research Projects for IDS-RSF 2023
(in chronological order of the PI’s surname)

Individualised Bleeding and Stroke Risk Prediction in Patients with Atrial Fibrillation: A Machine Learning Approach Using Multimodal Healthcare Big Data

Research Theme(s): Application of Data ScienceMachine Learning
Professor Esther Chan
Principal Investigator
Professor, Department of Pharmacology and Pharmacy
Dr Qingpeng Zhang
Co-Principal Investigator
Associate Professor, HKU IDS / Department of Pharmacology and Pharmacy

Co-Investigators include:

  1. Dr Celine Chui, Assistant Professor, School of Nursing & School of Public Health
  2. Dr Eric Wan, Assistant Professor, Department of Family Medicine and Primary Care & Department of Pharmacology and Pharmacy
  3. Dr Gary Lau, Clinical Associate Professor, Department of Medicine, School of Clinical Medicine
  4. Prof Reynold Cheng, Associate Director, HKU IDS; Professor, Department of Computer Science

Project Abstract:

Oral anticoagulation therapy is essential among patients with atrial fibrillation (AF) to prevent thromboembolic events. However, the use of oral anticoagulants, including non-vitamin K antagonist oral anticoagulants (NOACs) and warfarin, is associated with bleeding events. This study aims to develop a machine learning method to predict the individualised bleeding and stroke risk in patients with AF by integrating multimodal medical information and considering the dynamic changes in patient’s medical history and prognosis, quantify and contrast the risk-benefit of different interventions to individual patients and subpopulations with different comorbidities, and identify modifiable risk factors and estimate their effect on individualised bleeding and stroke risks.

It is anticipated that further external funding could be acquired to support the development of a web-based application or mobile app for use by healthcare professionals and patients. The application will present individualised risk of stroke and bleeding, and visualisation of the effect of different interventions and modifiable risk factors in reducing these risks to promote real-world clinical practice.

Split Learning over 5G+ Edge Computing:
Enabling Deep Learning on Resource-constrained Devices

Research Theme(s): Application of Data ScienceMachine Learning
Dr Xiaohao Chen
Principal Investigator
Assistant Professor, Department of Electrical and Electronic Engineering
Dr Xihui Liu
Co-Principal Investigator
Assistant Professor,
HKU IDS / Department of Electrical and Electronic Engineering

Project Abstract:

Despite the inherent privacy preservation, on-device training, including federated learning, often imposes excessive computing and memory demands on end devices. This issue is further exacerbated by the continuous growth in AI model sizes, such as the emergence of large language models. To address these significant challenges, this project introduces the edge split learning framework, which leverages 5G+ multi-access edge computing (MEC) systems to facilitate model training for pervasive end devices by splitting models into user-side and server-side parts. This approach retains sensitive raw data on local devices while offloading the majority of the workload to MEC servers based on model splitting. The project aims to serve as an initial effort in designing such MEC systems from both learning and resource management perspectives. First, we devise resource-efficient split learning techniques that substantially reduce communication and computing costs compared to existing split learning schemes, which is particularly crucial for wireless edge systems. Second, we propose learning-oriented radio and computing resource management strategies to enhance performance for split learning at the 5G+ edge. These tasks converge to enable edge split learning at the 5G+ edge, which could play a critical role in not only advancing 5G+ MEC technologies but also realizing the vision of Artificial Intelligence of Things.

Intelligent Tutoring System for Collaborative Learning: A Hypergraph Approach to Analyzing Asynchronous Learning Process Data

Research Theme(s): Foundation of Data ScienceApplication of Data ScienceMachine Learning

Dr Shihui Feng
Principal Investigator
Assistant Professor, Academic Unit of Human Communication, Development, and Information Sciences
Dr Alec Kirkley
Co-Principal Investigator
Assistant Professor,
HKU IDS / Department of Urban Planning and Design

Project Abstract:

This project focuses on developing an intelligent AI-supported collaborative learning system (AICLS) that analyses asynchronous learning process data using a new proposed hypergraph approach to provide adaptive knowledge co-construction coaching. A new principled and non-parametric hypergraph segmentation algorithm will be developed to analyze the dynamics of students’ cognitive engagement in small-group collaborative learning tasks. This project is well aligned with the HKU IDS themes of Smart Society and Fundamental Data Science, and will make significant theoretical, methodological, and practical contributions to the research field of AI in education. The expected deliverables of the project include research articles and an external grant application.

CREC – An LLM-based Conversational Public Legal Knowledge Recommendation System

Research Theme(s): Explainable AIApplication of Data ScienceMachine Learning

Professor Benjamin Kao
Principal Investigator
Professor, Department of Computer Science
Dr Chao Huang
Co-Principal Investigator
Assistant Professor,
HKU IDS / Department of Computer Science

Project Abstract:

With advances in information technology, legislation and judgments (i.e., the “primary legal sources” used by legal professionals), are available online for public accesses. However, due to the highly technical nature of the legal knowledge, it is generally very difficult for the general public to navigate the large volume of legal information, identify the correct legal issue, and find the relevant legal rules that they need. The project’s objective is to bridge the legal knowledge gap by applying data science technology such as large-scale pre-trained language models (LLMs) and conversational recommendation systems. Specifically, we study how LLMs help construct a “Legal Question Bank” (LQB) that serves as model questions whose answers could be found in specific legal documents. Moreover, we develop a natural language conversational recommendation system that converses with a user to understand the user’s legal situation in order to shortlist relevant legal questions from the LQB. Finally, we study how to use LLMs to verbally explain to the user the legal concepts exemplified by each shortlisted question and how the question is legally relevant to the user’s situation.

HKU IDS Scholar Dr Chao Huang Is Shortlisted for the 2023 CCF-Tencent Rhino-Bird Young Faculty Open Research Fund

HKU IDS Scholar Dr Chao Huang Is Shortlisted for the "2023 CCF-Tencent Rhino-Bird Young Faculty Open Research Fund (2023年度CCF-騰訊犀牛鳥基金)"

HKU IDS scholar, Dr Chao Huang, Assistant Professor in our Institute and Computer Science, has his research project shortlisted in the 2023 CCF-Tencent Rhino-Bird Young Faculty Open Research Fund (2023年度CCF-騰訊犀牛鳥基金)! Receiving more than 230 applications from 90 universities, 26 scholars have been shortlisted for their innovative research projects focusing on the area of artificial intelligence, and only 2 awardees come from Hong Kong.

The Rhino-Bird Fund encourages research work on large-scale language models, content generation technology, machine learning, and deep learning. It aims to establish a collaboration platform for industry-university research, connect industry practice with academic research, and support outstanding young scholars worldwide for social development. Dr. Chao Huang’s research interests cover data mining and machine learning. For more information about Dr Huang, please browse: https://datascience.hku.hk/people/chao-huang/

Let us extend our greatest congratulations to Dr Huang. The full list of shortlisted scholars can be viewed here: https://www.ccf.org.cn/Collaboration/Enterprise_Fund/News/tx/2023-07-31/794523.shtml

IDSS 2301 – Immersing Oneself in a Very Meaningful Vacation through the HKU IDS Summer Course – Stay Proud as Our First Batch of Potential Data Scientists!

Immersing Oneself in a Very Meaningful Vacation through
HKU IDS Summer Course IDSS 2301– Stay Proud as Our First Batch of Potential Data Scientists!

The 2-week programme IDSS 2301: Data Science for Beginners: Theory, Algorithms and Applications, organized by the HKU Musketeers Foundation Institute of Data Science and supported by the HKU Summer Institute, has marked its successful conclusion on July 20, 2023! It was the first-and-ever intensive undergraduate course offered by the HKU IDS and we were very excited to have welcomed over 110 young faces across the Greater Bay Area and the globe. Being a potentially new batch of data scientists showing keen interest in the theoretical aspects of machine learning and artificial intelligence, the students not only had much fun at the Opening Ceremony on July 10, 2023, where light-hearted ice-breaking activities and an introductory guest lecture by the Institute’s Director, Professor Yi Ma, were given, but also embarked on an amateur yet interdisciplinary learning journey of data science knowledge through the lectures by the research community at HKU IDS, including Dr Man Chung Yue (HKU IDS/ Department of Industrial and Manufacturing Systems Engineering), Dr Yue Xie (HKU IDS / Department of Mathematics), and Dr Sebastian Morel-Balbi (Post-doctoral Fellow).

According to the post-programme evaluation form, more than half of respondents “strongly agreed” that the course “had met their expectations” and would “highly recommend the course to other students”. Over 80% of the participants also agreed that the three instructors were “well-prepared for the classes”.

Students even commented to individual instructors as being “brilliant presenters” whose “way of imparting knowledge helps [him/her] comprehend well”. The lectures were also rated as “well-explained” and able to help the participants “understand lots of things after class”.

The fact that students were inspired to demonstrate critical thinking and understanding towards various aspects of data and machine learning was also reflected on the final day of the course, during which students celebrated for their graduation from the short course by giving a 10-minute group presentation in front of the instructors and their peers on July 20, 2023. The presentation themes covered a wide range of aspects relevant to daily applications of data science, including the following:

  1. Smart Cities
  2. Education
  3. Natural Science
  4. Business & Finance
  5. Legal & Public Policy
  6. Healthcare
  7. Environment & Energy


Students with outstanding performance enjoyed a moment of honour and applause during the Prize Presentation cum Closing Ceremony when the groups were awarded for the “Best Group Presentation Award” as well as the “Most Popular Vote” with the specially designed souvenirs from the Institute.

“The value of a college education is not the learning of many facts but the training of the mind to think. —Albert Einstein”

Packed as it might seem, the IDSS 2301 summer programme aimed to render undergraduate students a foundational training of how machine learning comes into play, and what artificial intelligence is supposed to be complementing human intelligence. We hope the summer programme will be a first step for students to develop interest in this heated area of research, and sooner or later, they could possibly be a new member of the HKU IDS family. 

HKU & Shanghai AI Lab Signed Research Collaboration Agreement – Strong Joint Force in Talent Development in Artificial Intelligence Across China and the Globe

HKU & Shanghai AI Lab Signed Research Collaboration Agreement –
Strong Joint Force in Talent Development in Artificial Intelligence Across China and the Globe

On July 13, 2023, the Musketeers Foundation Institute of Data Science (“the Institute”) welcomed our first strategic research collaborative partner, the Shanghai Artificial Intelligence Laboratory (“the Lab”), at the campus of The University of Hong Kong! The agreement signing ceremony marked a moment to celebrate and commemorate as both units witnessed a new milestone for cross-border research collaboration in the field of artificial intelligence. 

The collaboration aims at delivering world-class impactful research in AI and nurturing top research talents. Apart from the officiating guests from the Lab and HKU who came to grace the event, we have Mr Hailong Shang, Legislative Councillor of the Hong Kong SAR Government as our guest of honor to witness the ceremony with other key members of both sides. Professor Max Shen, Vice-President and Pro-Vice-Chancellor (Research) and Professor Yanfeng Wang, Assistant to Director at the Lab were the authorized parties to sign the agreement: it was an eventful day that it kick-started the beginning of a strategic alliance for AI research development between the Lab and HKU. At the ceremony, HKU and the Lab promised to join force in nurturing young talents in delivering world-class innovative research outputs beneficial to the community in Hong Kong and the Great Bay Area.

<Front Left to Right> Professor Max Shen, Vice-President and Pro-Vice-Chancellor (Research), The University of Hong Kong and Professor Yanfeng Wang, Assistant to Director at the Shanghai Artificial Intelligence Laboratory represented HKU and the Lab respectively to sign the research collaboration agreement.

<Left to Right> The other officiating guests to the ceremony joined as witnesses to the signing processes. 

In his welcoming address, Professor Max Shen exclaimed his excitement over the launch of this collaborative relationship for the Institute, and the University as a whole. He said, “I have been longing to see the strong partnership between HKU and the Lab to happen. And today, this aspiration has eventually come true. I very much look forward to the fruitful accomplishment brought by this research collaboration, which not only aligns with the University’s commitment in delivering research excellence, but also contributes to the betterment of mankind through the advancement of sci-tech projects on data science.”

Professor Xiao-ou Tang, the Lab’s Director, further remarked the significance of this collaboration, as an inaugural joint platform for talent nurturance, whose students will benefit from the wealth of resources and connections with the Lab, and at the same the academic research support and manpower at HKU. Professor Tang added, “Our collaboration will ultimately work towards building a ‘Whampoa Military Academy’ in the field of AI, a globally renowned breeding ground for outstanding data scientists in both Shanghai and Hong Kong. Congratulations on our concerted efforts in promoting scientific research development in artificial intelligence!”

The Institute’s Director, Professor Yi Ma, echoed Professor Tang on his visions, “Indeed, the most important matter is to create models of knowledge transfer and talent development that can be duplicated for use in other institutions. With our good people and good hardware, I trust we can achieve great things.”

Professor Max Shen, Vice-President and Pro-Vice-Chancellor (Research) of the University,
exchanged gift with Professor Yanfeng Wang, Assistant to Director of the Shanghai AI Lab, to mark the day.
The officiating guests with HKU IDS management
The officiating guests with HKU IDS scholars and management
Professor Xiao-ou Tang, Professor Yi Ma with our future young researchers: the summer research students at HKU IDS.

Professor Yi Ma Assumes Headship of Department of Computer Science

HKU IDS Director Professor Yi Ma Assumes Headship of the Department of Computer Science

Our Institute’s Director, Professor Yi Ma, Professor, Chair of Artificial Intelligence, has assumed Headship of the Department of Computer Science at HKU with effect from June 1, 2023. He is also Chair Professor of both HKU IDS and the Department of Computer Science.

For full bio of Professor Ma, please browse: https://datascience.hku.hk/people/yi-ma/

HKU IDS Scholar Dr Xihui Liu’s Project Is Shortlisted for “2023 Tencent AI Lab Rhino-Bird Focused Research Program”

HKU IDS Scholar Dr Xihui Liu's Project Is Shortlisted for "2023 Tencent AI Lab Rhino-Bird Focused Research Program” (2023騰訊AI Lab犀牛鳥專項研究計劃)

HKU IDS scholar, Dr Xihui Liu, Assistant Professor in our Institute and Electrical and Electronic Engineering, has her research project titled “Egocentric Multimodal Video Understanding (基於自然語言大模型的多模態第一人稱視頻表徵學習和交互式推理)”, shortlisted in the “2023 AI Lab Rhino-Bird Special Research Program (2023騰訊AI Lab犀牛鳥專項研究計劃)”. Most of the applicants to the Program come from top-tier tertiary institutions in the region, and only 6% of the projects got shortlisted.

The Tencent AI Lab Rhino-Bird Special Research Program focuses on cutting-edge and original research in the domains of artificial intelligence, such as machine learning, computer vision, natural language processing, speech technology, and robotics. Dr. Xihui Liu’s research interests cover computer vision and machine learning. For more information about Dr Liu, please browse: https://datascience.hku.hk/people/xihui-liu/

Congratulations to Dr Liu for her excellent works!

IDS Seminar: Robust Deep Learning under Distribution Shift

Title: Robust Deep Learning under Distribution Shift
Speaker: Dr Yingyu Liang, Assistant Professor,  Department of Computer Science, University of Wisconsin-Madison
Date: June 29, 2023
Time: 10:30am – 11:30am
Venue: Tam Wing Fan Innovation Wing II / Zoom

Mode: Hybrid. Seats for on-site participants are limited. A confirmation email will be sent to participants who have successfully registered.

Abstract

Deep learning has achieved remarkable success in various application domains such as computer vision, natural language processing, and game playing. However, this success is based on the assumption that the test data distribution is identical to the training data distribution. In practice, this assumption usually does not hold, leading to distribution shift. As a result, deep neural networks often suffer a significant drop in their performance under distribution shift. There are two kinds of distribution shifts: one occurs naturally during the data collection process while the other is constructed by some adversaries. I will discuss our recent research on addressing these two kinds of distribution shifts. Specifically, I will talk about how to estimate the generalization of deep neural networks in test time under distribution shift and how to use selective prediction to enhance adversarial robustness.

Speaker

Dr Yingyu Liang
Assistant Professor @ Department of Computer Science, University of Wisconsin-Madison; Recipient of the NSF CAREER Award

Yingyu Liang is an Assistant Professor at the University of Wisconsin Madison. He received his Ph.D. from the Georgia Institute of Technology and was a postdoctoral researcher at Princeton University. His research aims at providing theoretical foundations for modern machine learning models and designing effective algorithms for real-world applications. Recent focuses include optimization and generalization in deep learning, robust machine learning, and their applications. He is a recipient of the NSF CAREER award.

Dr Liang’s full profile can be accessed here: https://pages.cs.wisc.edu/~yliang/

For information, please contact:
Email: datascience@hku.hk

HKU IDS Scholar Seminar Series #7: Generalized Power Methods for Group Synchronization Problems

Title: Generalized Power Methods for Group Synchronization Problems
Speaker: Dr. Man Chung Yue, Assistant Professor, IDS & Department of Industrial and Manufacturing Systems Engineering, HKU
Date: Jul 6, 2023
Time: 10:30am – 11:30am

Mode: Hybrid. Seats for on-site participants are limited. A confirmation email will be sent to participants who have successfully registered.

Abstract

Group synchronization problems (GSPs) aim at recovering a collection of group elements based on their noisy pairwise comparisons and find a wide range of applications in areas such as machine learning, molecular biology, robotics and computer vision. Existing approaches to GSPs are designed only for a specific subgroup, do not scale well and/or lack theoretical guarantees. In this talk, we present a unified approach to the important sub-class of GSPs associated with any closed subgroup of the orthogonal group, which consists of a suitable initialization and an iterative refinement step based on the generalized power method. Theoretically, we show that our approach enjoys a strong guarantee on the estimation error under certain conditions on the group, measurement graph, noise and initialization. We also show that the group condition is satisfied for the orthogonal group, the special orthogonal group, the permutation group and the cyclic group, which are all practically relevant subgroups of the orthogonal group. We then verify the conditions on the measurement graph and noise for standard random graph and random matrix models. Finally, based on the classical notion of metric entropy, we develop a novel spectral-type estimator for GSPs, which can be used as the initialization of our approach.

Speaker

Dr. Man Chung Yue
Assistant Professor @ HKU IDS & Department of IMSE
Dr. Man Chung Yue is an Assistant Professor jointly affiliated with the Musketeers Foundation Institute of Data Science and the Department of Industrial and Manufacturing Systems Engineering at The University of Hong Kong. Prior to joining HKU, he was an Assistant Professor in the Department of Applied Mathematics at The Hong Kong Polytechnic University. From 2017 to 2019, he worked as a Research Associate at Imperial College London. He received his Ph.D. in Systems Engineering and Engineering Management in 2017 and B.Sc. in Mathematics in 2012, both from The Chinese University of Hong Kong. His research focuses on optimization, data science and decision-making under uncertainty.
For full biography of Dr. Yue, please refer to: https://datascience.hku.hk/people/man-chung-yue/

For information, please contact:
Email: datascience@hku.hk

IDS Guest Seminar: The Algorithmic Explainability Bait and Switch

Title: The Algorithmic Explainability Bait and Switch
Speaker: Assistant Professor, Department of Statistical Sciences & Department of Philosophy, University of Toronto
Date: June 16, 2023
Time: 11:15am – 12:30pm

Mode: Hybrid. Seats for on-site participants are limited. A confirmation email will be sent to participants who have successfully registered.

Register Now

Abstract

Explainability in machine learning (ML) is emerging as a leading area of academic research and a topic of significant legal and regulatory concern. Indeed, a near-consensus is emerging in favour of explainable ML among lawmakers, academics, and civil society groups. In this project, we challenge this prevailing trend. We argue that explaining ML predictions is at best unnecessary or misleading and at worst socially harmful. Unlike interpretable ML, which we endorse where it is feasible, explainable ML can deliver on none of the benefits it is touted for – e.g., engendering trust, increasing understanding, and promoting algorithmic safety and reliability.

Speaker

Dr. Boris Babic
Assistant Professor @ Department of Statistical Sciences & Department of Philosophy, University of Toronto
Boris Babic is a tenure track assistant professor at The University of Toronto, where he has a joint appointment in the Department of Statistical Sciences and the Department of Philosophy. He is also a faculty fellow of the Schwartz Reisman Institute for Technology and Society, and a visiting assistant professor of Decision Sciences at INSEAD. He received a PhD in Philosophy and an MSc in Statistics from the University of Michigan, Ann Arbor, and a JD from Harvard Law School. He completed his postdoctoral scholarship at the California Institute of Technology (Caltech). His primary research interests are in legal, ethical, and policy dimensions of artificial intelligence and machine learning as well as in the foundations of Bayesian inference. He is one of the founding associate editors of ACM Transactions on Probabilistic Machine Learning. His work has appeared in Science, Nature Machine Intelligence, Nature Digital Medicine, Philosophy of Science, the British Journal for the Philosophy of Science, and the Harvard Business review, among others.

For information, please contact:
Email: datascience@hku.hk

HKU IDS Scholar Dr Chao Huang’s Project Is Shortlisted for “2023 Tencent WeChat Rhino-Bird Special Research Programme”

HKU IDS Scholar Dr Chao Huang's Project Is Shortlisted for "2023 Tencent WeChat Rhino-Bird Special Research Programme (2023騰訊微信犀牛鳥專項研究計劃)

HKU IDS scholar, Dr Chao Huang, Assistant Professor in our Institute and Computer Science, has his research project titled “Improving Ranking Performance in Recommender Systems (推薦系統排序模型性能優化研究)”, shortlisted in the “2023 Tencent WeChat Rhino-Bird Special Research Programme (2023騰訊微信犀牛鳥專項研究計劃), among applicants from top-tier tertiary institutions regionally! Dr. Huang is the only awardee from The University of Hong Kong in this Programme.

The Programme supports research projects on innovative applications on different scenarios of WeChat use. All shortlisted research teams are deemed leading experts in their respective research fields. Dr. Chao Huang’s research interests cover data mining and machine learning. For more information about Dr Huang, please browse: https://datascience.hku.hk/people/chao-huang/

Let’s extend our big round of applause to Dr Huang for his outstanding works!

The full list of shortlisted projects can be viewed here: Full List_Shortlisted Projects 2023