HKU IDS Postdoctoral Research Fellowship Scheme 2023-24 is now open for application!

HKU IDS Postdoctoral Research Fellowship Scheme 2023-24 welcomes applications from now until September 30, 2023!

HKU IDS is launching the HKU IDS Postdoctoral Research Fellowship Scheme 2023-24 (“the Scheme”). Successful awardees will have the chance to work with to work with our HKU IDS community in a conducive research environment. Moreover, awardees will also be given a fruitful training opportunity to develop their career in the academia through the Scheme.

For interested applicants, please be advised to contact a preferred HKU IDS scholar to garner his/her interest in collaborating with you before making your application.

Applicants should then apply online by completing an electronic application form on or before September 30, 2023 for this round of application. For more details about the Scheme, please browse the circular below:

Learn more

HKU IDS Scholar Seminar Series #6: Constrained optimization: application, algorithm and complexity

Title: Constrained optimization: application, algorithm and complexity
Speaker: Dr. Yue Xie, Research Assistant Professor, IDS & Department of Mathematics, HKU
Date: Jun 7, 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

In this talk, the speaker will discuss constrained optimization. The speaker will focus on two important subclasses: bound-constrained nonconvex optimization and linear programming. Typical applications of them include nonnegative matrix factorization and optimal transport (OT) problems, which are popular topics in both mathematics and data science. To resolve the former subclass, the speaker will propose a projected Newton-CG algorithm. This algorithm is designed to possess both practicality and worst-case complexity guarantees matching the best known in literature. For the linear programming formulation of OT, the speaker will discuss random block coordinate descent (RBCD) methods. A direct advantage of these methods is to save memory. In addition, the speaker and his team’s preliminary numerical experiments show that it competes well with the classical Sinkhorn’s algorithm.

Speaker

Dr. Yue Xie
Research Assistant Professor @ HKU IDS & Dept of Mathematics
Dr. Yue Xie is a Research Assistant Professor in Musketeers Foundation Institute of Data Science (HKU-IDS) and Department of Mathematics at the University of Hong Kong. He was a postdoc at UW Madison working in the nonconvex optimization group led by Professor Stephen J. Wright. He received his PhD degree in Pennsylvania State University and Bachelor degree from Tsinghua University. Dr. Yue Xie has been focusing on algorithm design and analysis to address nonconvex and stochastic optimization problems with all types of applications including machine learning and data science. He has published/served as the referee of top-tier journals including Mathematical Programming, SIAM Journal on Optimization, and IEEE Transactions on Automatic Control, etc. He has delivered numerous presentations at major international conferences such as International Conference on Continuous Optimization (ICCOPT), International Symposium on Mathematical Programming (ISMP), SIAM Conference on Optimization and International Conference on Machine Learning (ICML).
More details about him can be found at: https://yue-xie.github.io.

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

IDS Guest Seminar: Query Evaluation under Differential Privacy

Title: Query Evaluation under Differential Privacy
Speaker: Wei Dong, Ph.D. Candidate, Department of Computer Science of Engineering, Hong Kong University of Science and Technology
Date: May 29, 2023
Time: 11:00am – 12:00nn

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

Abstract

Differential privacy (DP) has garnered significant attention from both academia and industry due to its potential in offering robust privacy protection for individual data during analysis. With the increasing volume of sensitive information being collected by organizations and analyzed through SQL queries, the development of a general-purpose query engine that is capable of supporting a broad range of SQLs while maintaining DP has become the holy grail in privacy-preserving query release. However, there are two significant challenges. First, guaranteeing privacy in a relational database with multiple relations, foreign keys, and the join operator is challenging since individuals can make large and correlated contributions to the query results. Second, noise injection is essential for privacy protection, but traditional notions of optimality, such as instance optimality and worst-case optimality, are either unachievable or meaningless when evaluating relational queries under DP, further complicating the task of achieving an optimal privacy-utility trade-off. In this talk, I will give a selective overview of my recent research in addressing these challenges in SQL queries answering under DP.

Speaker

Wei Dong
Ph.D. Candidate @ Department of Computer Science and Engineering, Hong Kong University of Science and Technology
Wei Dong is a final-year Ph.D. candidate in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology. His general areas of interest include database theory and algorithms, data security and privacy, and statistics. His research has been recognized by the academic community and appeared in top conferences, such as SIGMOD, PODS, S&P, CCS, NeuIPS, and KDD. He received the Best Paper Award in SIGMOD 2022. He is also the receipt of HKUST Engineering PhD Research Excellence Award 2023, which is a distinguished honor granted to single Ph.D. student from the School of Engineering at HKUST.

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

IDS Guest Seminar: Visual Perception and Learning in an Open World

Title: Visual Perception and Learning in an Open World
Speaker: Dr Shu KONG, Assistant Professor, Department of Computer Science and Engineering, Texas A&M University
Date: May 23, 2023
Time: 10:00am – 11:00am

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

Abstract

Visual perception is indispensable in numerous applications spanning autonomous vehicles and interdisciplinary research. Today’s visual perception algorithms are often developed under a closed-world paradigm, which assumes the data distribution and categorical labels are fixed a priori. This assumption is unrealistic in the real open world, which contains vast situations that are unpredictable and dynamic. As a result, closed-world visual perception systems appear to be brittle in the open-world. For example, equipped with such visual perception systems, an autonomous vehicle could fail to recognize a never-before-seen overturned truck and cause collision; it could fail to detect a pedestrian in a dark night and cause casualties. In this talk, I will present my solutions to recognizing unknown objects, segmenting and detecting general objects, and improving object detection using multimodal signals. If time allows, I will share my thought about open-world visual perception and learning, and sketch my future research.

Speaker

Dr Shu KONG
Assistant Professor @ Department of Computer Science and Engineering, Texas A&M University
Shu Kong is on the faculty in the Department of Computer Science and Engineering, Texas A&M University, after postdoc training in the Robotics Institute, Carnegie Mellon University. He received the PhD degree in computer science from the University of California, Irvine. His research interests include computer vision, applied machine learning, and their broad applications. His current research focus is on visual perception and learning in the open world. His recent paper on this topic received honorable mention for Best Paper / Marr Prize at ICCV 2021. His latest interdisciplinary research develops high-throughput pollen analysis tools, which were featured by the National Science Foundation as that “open new era of fossil pollen research.”
Dr Su’s Google Scholar page: https://scholar.google.com/citations?user=sm9FdLoAAAAJ&hl=en

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

IDS Seminar: Modeling the 3D Physical World for Embodied Intelligence

Title: Modeling the 3D Physical World for Embodied Intelligence
Speaker: Dr Hao Su, Assistant Professor, Department of Computer Science and Engineering, Jacobs School of Engineering,
University of California, San Diego
Moderator: Professor Kenneth Wong, Head of Department of Electrical and Electronic Engineering, HKU
Date: May 9, 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.

Seminar recording:

Abstract

Embodied AI is a rising paradigm of AI that targets enabling agents to interact with the physical world. Embodied agents can acquire a large amount of data through interaction with the physical world, which makes it possible for agents to close the perception-cognition-action loop and learn autonomously from the world to revise its internal model. In this talk, I will present the work of my group to build an eco-system towards building embodied AI.

Speaker

Dr Hao Su
Assistant Professor @ Department of Computer Science and Engineering, University of California, San Diego
Hao Su is an Assistant Professor of Computer Science at the University of California, San Diego. He is the Director of the Embodied AI Lab at UCSD. He works on algorithms to model, undertand, and interact with the physical world. His interests span computer vision, machine learning, computer graphics, and robotics – all areas in which he has published and lectured extensively. He has more than 75,000 citations according to Google Scholar. Hao Su obtained his Ph.D. from Stanford in 2018. At Stanford and UCSD he developed widely used datasets and softwares such as ImageNet, ShapeNet, PointNet, PartNet, SAPIEN, and more recently, ManiSkill. He also developed new courses to promote machine learning methods for 3D geometry and embodied AI. He served as the Area Chair or Associate Editor for top conferences and journals in computer vision (ICCV/ECCV/CVPR), computer graphics (SIGGRAPH/ToG), robotics (IROS/ICRA), and machine learning (ICLR/NeurIPS).
Dr Su’s Google Scholar page: https://scholar.google.com/citations?user=1P8Zu04AAAAJ&hl=en

Moderator

Prof Kenneth K.Y. Wong
Head @ Department of Electrical and Electronic Engineering, HKU
Prof. Kenneth Kin-Yip Wong is currently a Professor in the Department of Electrical and Electronic Engineering in the University of Hong Kong. He is a senior member of the IEEE (Photonics Society), OSA, and SPIE. He received combined B.E. (1st class honor with medal award) degree in electrical engineering and B. S. degree in physics from the University of Queensland, Brisbane, Australia, in 1997. He received the M.S. degree in 1998 and the Ph.D. degree in 2003, both in electrical engineering at Stanford University. He was a member of the Photonics and Networking Research Laboratory at Stanford University. His research field included DWDM systems, SCM optical systems, fiber nonlinearity, and fiber optical parametric amplifiers. He is author or coauthor of over 50 journal and conference papers.
For full biography of Prof. Wong, please browse his website at: https://www.eee.hku.hk/people/kywong/

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

Research team led by HKU IDS Scholar Dr Chao Huang ranked 1st & 3rd in the Most Influential Papers of SIGIR 2022 (2023-04 ver.)!

Research team led by HKU IDS Scholar Dr Chao Huang ranked 1st and 3rd in the Most Influential Papers of SIGIR 2022 (2023-04 ver.)!

Huge congratulations to our HKU IDS scholar, Dr Chao Huang, and his research group for being featured in the Most Influential SIGIR Papers (2023-04 ver.). Two research papers under Dr Huang’s supervision, namely “Hypergraph Contrastive Collaborative Filtering” and “Knowledge Graph Contrastive Learning for Recommendation” are ranked 1st and 3rd respectively!

SIGIR (Annual International ACM SIGIR Conference on Research and Development in Information Retrieval) is one of the top-tier data mining and information retrieval conferences in the world. Please refer to the website below for the paper abstracts:
https://www.paperdigest.org/2023/04/most-influential-sigir-papers-2023-04/

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/

Accounting Executive (at the rank of Clerk II)

Careers at IDS

Accounting Executive (at the rank of Clerk II)

Position  

Applications are invited for the appointment as Accounting Executive (at the rank of Clerk II)
in the HKU Musketeers Foundation Institute of Data Science (HKU IDS) (Job Ref.: 522723), (to commence as soon as possible, on a two-year fixed-term basis with contract-end gratuity and University contribution to a retirement benefits scheme, totalling up to 10% of basic salary, with the possibility of renewal subject to satisfactory performance).

Applicants should possess a Higher Diploma, OR 5 passes in HKCEE including English (min. Grade C if Syllabus A/Level 2 from 2007), Chinese (Level 2 from 2007) and Mathematics, OR have obtained a minimum of Level 2 or equivalent in 5 subjects in HKDSEE including English Language, Chinese Language and Mathematics, with at least 3 years’ accounting experience, preferably in tertiary institutions or in the education sector and/or auditing.  They should have a good command of written and spoken English and Chinese, good interpersonal, communication and organisational skills, computer literacy, and ability to multitask under pressure and work independently as well as in a team.  They should also be detail-oriented and self-motivated with a strong sense of responsibility.  Those with strong knowledge of PC and software applications such as MS Word, Excel and PowerPoint, as well as experience with Oracle Financials, are highly preferred. 

The appointee will assist in financial and administrative matters of the Institute, including budgetary control, payments and receipts processing, preparation of accounting statements, staff reimbursement and outside practice, maintenance of database for finance records, and office inventory.  He/She will also provide clerical support for tasks as assigned by supervisor. 

Shortlisted candidates will be invited to attend a written test and an interview.  

A highly competitive salary commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits.

Application

The University only accepts online application for the above post.  Applicants should apply online and upload an up-to-date C.V.  Review of applications will commence as soon as possible and continue until September 30, 2023, or until the post is filled, whichever is earlier.

Apply Now

HKU IDS Scholar Dr Tao Yu received the 2023 Google Research Scholar Award!

HKU IDS Scholar Dr Tao Yu received the 2023 Google Research Scholar Award!

The Institute is proud to announce that our HKU IDS scholar, Dr. Tao Yu, Assistant Professor in the HKU IDS and Department of Computer Science, has been awarded the 2023 Google Research Scholar Award!

Dr. Yu’s award-winning proposal, under the category of “Structured data, extraction, semantic graph, and database management”, is titled “Building Natural Language Interfaces for Data Science with Language Models“. His research interests cover Natural Language Processing and Artificial Intelligence. For more information about Dr. Yu, please browse: https://datascience.hku.hk/people/tao-yu/

The Google Research Scholar Program aims to support early-career engineers and scientists who are in pursuit of world-class research projects. Congratulations to Dr. Yu on his outstanding achievement!

HKU IDS Scholar Seminar Series #5: Denoising Diffusion-based Generative Modes for Visual Content Generation

Title: Denoising Diffusion-based Generative Modes for Visual Content Generation
Speaker: Dr. Xihui Liu, Assistant Professor, IDS & Department of Electrical and Electronic Engineering, HKU
Date: May 5, 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.

Seminar recording:

Abstract

Denoising diffusion models, also known as score-based generative models, are a class of generative models that can produce high-fidelity images, videos and 3D models from noise. They define a forward diffusion process that gradually adds noise to the input data, and a reverse diffusion process that iteratively denoises the noise using a learned score function. In this talk, we will introduce the foundations and applications of denoising diffusion-based generative models. We will cover the theoretical background, the training and sampling methods, the recent advances and challenges, and the practical use cases of these models. We will also demonstrate some examples of image/video/3D generation using denoising diffusion-based generative models and discuss their advantages and limitations compared to other generative models.

Speaker

Dr. Xihui Liu
Assistant Professor @ HKU IDS & Dept of EEE
Dr. Xihui Liu is an Assistant Professor at the Musketeers Foundation Institute of Data Science (HKU IDS) and the Department of Electrical and Electronic Engineering (EEE)The University of Hong Kong. Before joining HKU, she was a postdoc Scholar at UC Berkeley, advised by Prof. Trevor Darrell. She obtained her Ph.D. degree from Multimedia Lab (MMLab), the Chinese University of Hong Kong, supervised by Prof. Xiaogang Wang and Prof. Hongsheng Li. She received her bachelor’s degree in Electronic Engineering at Tsinghua University.
Her research interests cover computer vision, machine learning, and artificial intelligence, with special emphasis on visual synthesis, generative models, vision and language, and multimodal AI. She was awarded Adobe Research Fellowship 2020, MIT EECS Rising Stars 2021, and WAIC Rising Stars Award 2022.

Moderator

Prof Kenneth K.Y. Wong
Head @ Department of Electrical and Electronic Engineering, HKU
Prof. Kenneth Kin-Yip Wong is currently a Professor in the Department of Electrical and Electronic Engineering in the University of Hong Kong. He is a senior member of the IEEE (Photonics Society), OSA, and SPIE. He received combined B.E. (1st class honor with medal award) degree in electrical engineering and B. S. degree in physics from the University of Queensland, Brisbane, Australia, in 1997. He received the M.S. degree in 1998 and the Ph.D. degree in 2003, both in electrical engineering at Stanford University. He was a member of the Photonics and Networking Research Laboratory at Stanford University. His research field included DWDM systems, SCM optical systems, fiber nonlinearity, and fiber optical parametric amplifiers. He is author or coauthor of over 50 journal and conference papers.
For full biography of Prof. Wong, please browse his website at: https://www.eee.hku.hk/people/kywong/

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

IDS Interdisciplinary Seminar Series: The Philosophy of Artificial Intelligence: What it Is, Why it Matters, and How it Can Influence the Development of AI

Host: HKU Musketeers Foundation Institute of Data Science
Co-host: AI & Humanity Lab, Department of Philosophy, HKU

IDS Interdisciplinary Seminar - by Professor Herman Cappelen

Title: The Philosophy of Artificial Intelligence: What it Is, Why it Matters, and How it Can Influence the Development of AI 
Speaker: Professor Herman Cappelen, Chair Professor of Philosophy; Director, AI & Humanity Lab, The University of Hong Kong
School of Humanities, The University of Hong Kong
Date: Apr 26, 2023
Time: 3:30pm – 4:30pm

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

Seminar recording:

Abstract

There’s broad agreement that the rapid development of artificial intelligence raises fundamental and existential challenges for humanity. This talk outlines the ways in which this development also raises challenging philosophical questions, why those questions matter, and how computer scientists and philosophers can collaborate on solving them.

Speaker

Professor Herman Cappelen
Chair Professor of Philosophy, School of Humanities; Director of AI & Humanity Lab @ The University of Hong Kong

Prof. Cappelen is Chair Professor to the Department of Philosophy at the University of Hong Kong and the Director of AI&Humanity-Lab@HKU. He is also serving in the Steering Committee of the HKU Musketeers Foundation Institute of Data Science.

Before coming to HKU, he was on the faculty at the University of Oxford, the University of St Andrews and the University of Oslo. Cappelen is the author of 10 monographs and over 50 papers, covering many areas of philosophy. Cappelen and Josh Dever wrote the first philosophy book on explainable and interpretable AI:  Making AI Intelligible: Philosophical Perspectives (Oxford University Press 2021). With Rachel Sterken (HKU), he is the editor of Communicating with AI: Philosophical Perspectives (forthcoming Oxford University Press 2023). Cappelen and Dever’s new book in progress is called: In Defence of Artificial Intelligences: An Essay in Inhuman Philosophy. Cappelen is an elected member of Academia Europaea and the Norwegian Academy of Arts and Sciences.  

For Prof. Herman’s full biography, please browse: https://datascience.hku.hk/professor-cappelen-herman/ 

Moderator

Dr. Janet Hsiao
Head and Associate Professor, Department of Psychology; Affiliate of AI & Humanity Lab @ The University of Hong Kong

Dr. Janet Hsiao is an associate professor in the Department of Psychology and a principal investigator of the State Key Laboratory of Brain and Cognitive Sciences at University of Hong Kong. She received her Ph.D. in Informatics from University of Edinburgh and was a postdoctoral researcher in the Temporal Dynamics of Learning Center at University of California San Diego. She is also serving in the Steering Committee of the HKU Musketeers Foundation Institute of Data Science.

As a cognitive scientist, she is best known for her research on learning and visual cognition. She investigates universal principles and specific factors that modulate development of perceptual representations and information processing strategies during learning and expertise acquisition such as face recognition, reading, and object detection and identification. She adopts an interdisciplinary approach, using a variety of methods and techniques from artificial intelligence, experimental psychology, psycholinguistics, and cognitive neuroscience to study the human mind at different levels of analysis and organization. Her unique interdisciplinary approach has deepened and broadened our understanding of individual differences in learning and visual cognition and how they connect to cognitive performance and disorders.

For Dr. Hsiao’s full biography, please browse: https://datascience.hku.hk/dr-hsiao-janet/

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