HKU IDS Scholar Prof Chao Huang Received the Prestigious “Brilliant Star” Award at the 2024 WAIC Yunfan Award!

HKU IDS Scholar Prof Chao Huang Received the Prestigious "Brilliant Star" Award at the 2024 WAIC Yunfan Award!

HKU IDS faculty members are remarkable achievers in academia! We are thrilled to announce that Professor Chao Huang, Assistant Professor at HKU IDS / Computer Science, had been selected for the “Brilliant Star” Award at the 2024 World Artificial Intelligence Conference (WAIC) Yunfan Award (「WAIC雲帆獎2024 – 世界人工智慧大會組委會及機器之心」). The award serves to honour Prof Huang and his research group for their outstanding achievements and contributions in the area of AI open-source development.

Jointly organized by the WAIC Organizing Committee, the Shanghai Artificial Intelligence Laboratory, the Global University AI Alliance, and the AI Young Scientists Alliance, the Yunfan Award is deemed a highly esteemed accolade that recognizes young talents in the field of artificial intelligence and data science.

Official News Coverage 

Our very heartfelt congratulations to Prof Huang for continuously striving for academic excellence! Prof Chao Huang’s research interests cover data mining and machine learning. For more information about Prof Huang, please browse: https://datascience.hku.hk/people/chao-huang/

Sailing through a Highly Interactive Journey on Applied Data Science: HKU IDS Summer Course IDSS 2401 “Data Science For Societies”

Sailing through a Highly Interactive Journey on Applied Data Science:
HKU IDS Summer Course IDSS 2401 “Data Science For Societies”

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Organized by HKU Musketeers Foundation Institute of Data Science and supported by the HKU Summer Institute, the Institute was delighted to host a batch of summer students in its first summer course, IDSS2401 – Data Science for Societies, between June 24 and July 5, 2024! We welcomed interested undergraduates with basic statistics and mathematics background across the Greater Bay Area and the globe, to join our two course instructors, HKU-100 IDS scholars including Professor Chao Huang (HKU IDS / Department of Computer Science) & Professor Alec Kirkley (HKU IDS / Department of Urban Planning and Design), in exploring the numerous possibilities in daily life applications of interdisciplinary data science research in making our city smarter and merrier. 

According to the post-programme evaluation form, 100% of the respondents agreed that the course instructors “were always well prepared for the class”, and that over 90% of the participants “strongly agreed” that IDSS2401 was helpful in providing them with knowledge and understanding on the underlying concepts surrounding the theme. 

 

Almost 90% of the respondents “strongly agreed” that the course “had met their expectations” and 95% of them would “highly recommend the course to other students”.  

 

Student complimented individual course instructors as “very enthusiastic professor who gives each discussion comprehensive comments, and that “lectures are extremely detailed and engaging”.  

The encouraging survey results reflected on the remarkable teaching quality and research expertise of our HKU IDS faculty members who are area experts on the lectures delivered at IDSS2401. The summer programme rendered a wealth of key concepts and applications of AI, with a focus on smart cities, healthcare, and topics in the computational social sciences, which successfully helped them develop a foundation in this exciting area of study, as revealed from their outstanding performances at the group presentations at the end of the course.  

Students were not only rewarded with a fruitful HKU experience with multiple cultural activities as well as a sense of comradeship among their study peers over the 2-week interval, but gained a thorough understanding on some fundamentals of data science and machine learning. We hope the summer programme was able to help students fully utilize their summer vacation, and we really look forward to the second SI programme IDSS2402 which shall commence really soon!  

AI, Law, and Philosophy Workshop

AI, Law, and Philosophy Workshop

Date: July 16, 2024 (Tues)
Time: 9:30am – 6:00pm
Venue: 11/F Cheng Yu Tung Tower, Faculty of Law, The University of Hong Kong

Abstract

Organizers:

Boris Babic, Associate Professor, The University of Hong Kong, IDS, Philosophy, Law
Haochen Sun, Professor, The University of Hong Kong Faculty of Law

Welcome Remarks

9:30 – 9:35am
Yi Ma, Director & Chair Professor of HKU Musketeers Foundation Institute of Data Science

Presentations

9:35 – 10:20am
Yong Lim, Associate Professor, Seoul National University School of Law

10:20 – 11:05am
Annette Zimmermann, Assistant Professor, University of Wisconsin–Madison Department of Philosophy

Break (11:05 – 11:30am)

11:30am – 12:15pm
Nicholson Price, Professor of Law, University of Michigan Law School

Lunch (12:15pm – 2:15pm) 

2:15 – 3:00pm
Sangchul Park, Assistant Professor, Seoul National University School of Law

3:00 – 3:45pm
Boris Babic, Associate Professor, The University of Hong Kong, IDS, Philosophy, Law

Break (3:45 – 4:15pm)

4:15 – 5:00pm
Vincent Chiao, Associate Professor, University of Toronto Faculty of Law

5:00 – 5:50pm

Closing Panel and Closing Remarks

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

Summer Research Programme 2024 (SRP2024) – A New Research Journey at HKU IDS!

HKU IDS hosts our new batch of participants at the Summer Research Programme (SRP) 2024!

The 10-week intense research training programme organized by the HKU Graduate School has commenced!

HKU IDS proudly receives a total of 6 outstanding undergraduate students are working with our HKU IDS Scholars, who are experts in interdisciplinary research fields from across disciplines, on research projects focusing on cutting-edge technologies in data science. 

SRP Student Mentor(s)
Zi Zhu Prof. Yanchao Yang
Jiefeng Wu Prof. Yanchao Yang
Prof. Xihui Liu (Co-mentor)
Liulu Chen Dr. Yue Xie
Prof. Qingpeng Zhang (Co-mentor)
Haolun Wang Prof. Difan Zou
Prof. Yanchao Yang (Co-mentor)
Xusen Xiao Prof. Qingpeng Zhang
Lei Fang Prof. Yingyu Liang


On June 28, 2024, HKU IDS warmly welcomed our SRP participants in our orientation session with office tour, welcoming messages from HKU IDS mentors, fun games, and a tea reception.

The SRP2024 participants also put their hearts and souls into preparing for their final oral presentations, via the Project Plan Oral Presentations.

The SRP students hosted at HKU IDS joined the other participants at the grand Opening Ceremony upon their arrival at HKU.

The Transferrable Skills Retreat was another compulsory session during which SRP students acquired important skills alongside their academic journey, prompting them to become better researchers.

RGC Funding Results of HKU IDS Professors and Scholars in the Year 24/25 Exercise!

RGC Funding Results of HKU IDS Professors in the Year 24/25 Exercise!

HKU IDS is very thrilled to announce that several of our HKU IDS management members, as well as HKU IDS scholars, on outstanding results on receiving external grants funded by the Research Grants Council (RGC) in the capacity as Principal Investigators.

The list of awarded projects by HKU IDS faculty members is as follows: 

RGC General Research Fund (GRF) 2024/25 Exercise

Project NumberDepartmentPIProject TitleAwarded Amount (HK$)Duration
17301024HKU IDS / Urban Planning & DesignProfessor A.W. KirkleyDynamical coarse-graining of time series data over spatial networks for urban and climate science applications481,83424
17616324HumanitiesProfessor B. BabicThe Legal and Ethical Foundations of Generative Models: Cooperation, Transparency, and Privacy478,23036
17209324Computer ScienceProfessor P. LuoNew Methods for Large-scale Motion Prediction for Car Accident Avoidance via Adversarial Diffusion Models692,64524
17300824MathematicsDr. Y. XieA study of algorithms and complexity to solve high-dimensional nonconvex optimization683,97336
17310124Industrial and Manufacturing Systems EngineeringProfessor M.C. YueSingle-loop First-order Algorithms for Large-scale (Distributionally) Robust Optimization910,74236

RGC Early Career Scheme (ECS) 2024/25 Exercise

Project NumberDepartmentPIProject TitleProject Fund (HK$)ECS Grants / Awards (HK$)Grand Total Awarded (HK$)Duration
27207224HKU IDS / Electrical and Electronic EngineeringProfessor Y. YangInfoBodied AI: Learning Mutual Information for Embodied AI828,73950,000878,73936
27309624HKU IDS / Computer ScienceProfessor D. ZouAdvancing Sampling Methods via Diffusion-Based Monte Carlo781,65750,000831,65736

Thank you all for making HKU IDS proud, and we look forward to seeing the fruitful outcomes of the captioned projects!

For the full summary of funding results of The University, please refer to the website below: https://www.rss.hku.hk/funding/funding-results

HKU IDS Scholar Seminar Series #10: Why Larger Language Models Do In-context Learning Differently?

HKU IDS Scholar Seminar Series #10: Why Larger Language Models Do In-context Learning Differently?

Title: Why Larger Language Models Do In-context Learning Differently?
Speaker: Prof. Yingyu LIANG, Associate Professor, IDS and Department of Computer Science, HKU
Date: July 11, 2024
Time: 10:30am – 11:30am

Venue: IDS Seminar Room, P603, Graduate House / Zoom
Mode: Hybrid. Seats for on-site participants are limited. A confirmation email will be sent to participants who have successfully registered. 

Abstract

Large language models (LLM) have emerged as a powerful tool for AI, with the key ability of in-context learning (ICL), where they can perform well on unseen tasks based on a brief series of task examples without necessitating any adjustments to the model parameters. One recent interesting mysterious observation is that models of different scales may have different ICL behaviors: larger models tend to be more sensitive to noise in the test context. This work studies this observation theoretically aiming to improve the understanding of LLM and ICL. We analyze two stylized settings: (1) linear regression with one-layer single-head linear transformers and (2) parity classification with two-layer multiple attention heads transformers (non-linear data and non-linear model). In both settings, we give closed-form optimal solutions and find that smaller models emphasize important hidden features while larger ones cover more hidden features; thus, smaller models are more robust to noise while larger ones are more easily distracted, leading to different ICL behaviors. This sheds light on where transformers pay attention to and how that affects ICL. Preliminary experimental results on large base and chat models provide positive support for our analysis. This joint work with Zhenmei Shi, Junyi Wei, and Zhuoyan Xu will appear in ICML’24.

Speaker

Prof. Yingyu LIANG
Associate Professor @ HKU IDS & Department of Computer Science
Prof. Yingyu Liang is an Associate Professor in the Musketeers Foundation Institute of Data Science and Department of Computer Science at The University of Hong Kong. He is also an Associate Professor at the Department of Computer Sciences at the University of Wisconsin-Madison. Before that, he was a postdoc at Princeton University. He received his Ph.D. in 2014 from Georgia Tech, and M.S. (2010) and B.S. (2008) from Tsinghua University. He is a recipient of the NSF CAREER award. His research group aims at providing theoretical foundations for modern machine learning models and designing efficient algorithms for real world applications. Recent focuses include optimization and generalization in deep learning, robust machine learning, and their applications. For full biography of Prof. Liang, please refer to: https://datascience.hku.hk/people/yingyu-liang/

Moderator

Prof. Yi Ma
Director; Professor, Chair of Artificial Intelligence @ HKU IDS & Department of Computer Science 

Professor Yi Ma is a Chair Professor in the Musketeers Foundation Institute of Data Science (HKU IDS) and Department of Computer Science at the University of Hong Kong. He took up the Directorship of HKU IDS on January 12, 2023. He is also a Professor at the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He has published about 60 journal papers, 120 conference papers, and three textbooks in computer vision, generalized principal component analysis, and high-dimensional data analysis. 

Professor Ma’s research interests cover computer vision, high-dimensional data analysis, and intelligent systems. For full biography of Professor Ma, please refer to: https://datascience.hku.hk/people/yi-ma/

Testing

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

IDS Interdisciplinary Seminar – Unveiling Large Language Models for Visual Perception, Generation, Interaction, and Beyond

IDS Interdisciplinary Seminar - by Professor Ping LUO

Title: Unveiling Large Language Models for Visual Perception, Generation, Interaction, and Beyond

Speaker: Professor Ping LUO
Associate Director (Innovation and outreach), HKU IDS
Associate Professor, Dept of CS, HKU
Date: June 26, 2024
Time: 3:00pm – 4:00pm 
Venue: IDS Seminar Room, P603, Graduate House / Zoom
Mode: Hybrid. Seats for on-site participants are limited. A confirmation email will be sent to participants who have successfully registered.

Abstract

This presentation is divided into three parts. Firstly, we will go through a series of advancements that have redefined the landscape of image and video generation, such as GenTron (CVPR’24), Video DiT (ICLR’24), and MotionCtrl (SIGGRAPH’24) and the PixArt series—PixArt-alpha (ICLR’24), PixArt-delta (arXiv:2401.05252), and PixArt-sigma (arXiv:2403.04692). Secondly, we introduce how to unify perception and generation capacity in a single multimodal LLM, such as the instance-level Vision Language Models for image understanding and generation tasks (RegionGPT, CVPR’24). These models are distinct from traditional multimodal LLMs fine-tuned with image-text pairs, which often face challenges in achieving detailed instance-level visual concepts. Thirdly, building on the success of large multimodal models in high-level understanding, we design a multimodal code generation framework, RoboCodeX (ICML’24), crafted to convert task plans into precise robotic actions, ensuring adaptability across diverse scenarios. Our approach seeks to seamlessly integrate high-level cognitive processing with practical robotic applications, paving the way for enhanced robotic autonomy and versatility.

Speaker

Professor Ping LUO
Associate Professor @ The University of Hong Kong
Professor Ping Luo’s researches aim at 1) developing Differentiable/ Meta/ Reinforcement Learning algorithms that endow machines and devices to solve complex tasks with larger autonomy, 2) understanding foundations of deep learning algorithms, and 3) enabling applications in Computer Vision and Artificial Intelligence. Professor Ping Luo received his PhD degree in 2014 in Information Engineering, the Chinese University of Hong Kong (CUHK), supervised by Prof. Xiaoou Tang (founder of SenseTime Group Ltd.) and Prof. Xiaogang Wang. He was a Research Director in SenseTime Research. He has published 70+ peer-reviewed articles (including 20 first author papers) in top-tier conferences and journals such as TPAMI, IJCV, ICML, ICLR, NeurIPS and CVPR. He has won a number of competitions and awards such as the first runner up in 2014 ImageNet ILSVRC Challenge, the first place in 2017 DAVIS Challenge on Video Object Segmentation, Gold medal in 2017 Youtube‐8M Video Classification Challenge, the first place in 2018 Drivable Area Segmentation Challenge for Autonomous Driving, 2011 HK PhD Fellow Award, and 2013 Microsoft Research Fellow Award (ten PhDs in Asia).

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

HKU IDS Scholar Prof Chao Huang’s research team have papers listed as the “Most Influential SIGIR 2024-05 Papers”!

HKU IDS Scholar Prof Chao Huang’s research team have papers listed as the “Most Influential WWW and SIGIR Papers in Ver. 2024-05”!

It is a very encouraging piece of news to learn that HKU IDS scholar, Prof Chao Huang, Assistant Professor in our Institute and Computer Science, and his research team, have four of their works listed as the Most Influential Papers in Version 2024-05 in WWW (The Web Conference) & SIGIR (Annual International ACM SIGIR Conference on Research and Development in Information Retrieval)! These are two top-tier data science conferences in the world, and the honours have been presented by Paper Digest which analyzes all published papers in WWW and SIGIR respectively on a yearly basis.

The Web Conference (WWW) is one of the top internet conferences in the world. As for SIGIR (Annual International ACM SIGIR Conference on Research and Development in Information Retrieval), it is one of the top information retrieval conferences in the world.

A special mention has to go to Mr. Xubin Ren, our HKU IDS first year PhD candidate, who is the sole student author of the SIGIR paper “Disentangled Contrastive Collaborative Filtering”, authored with Lianghao Xia; Jiashu Zhao; Dawei Yin, as well as Prof Chao Huang.

Please join us to extend our heartfelt congratulations to Prof Huang, and the other authors of the papers, including Xubin, for their outstanding achievement.

Please read the four papers from the links below:

Paper (1)

Paper (2)

Paper (3)

Paper (4)

Prof Chao Huang’s research interests cover data mining and machine learning. For more information about Prof Huang, please browse: https://datascience.hku.hk/people/chao-huang/

AFAC2024 Competition is welcoming online registrations from now until July 19, 2024!

Organized by Alibaba, and Supported by HKU IDS

AFAC2024 Competition is welcoming online registrations
from now until July 19, 2024!

Please pair up with your team of innovators to win attractive prices.

Official homepage & Registration

Presentation slides about AFAC2024 rules and guidelines

About AFAC2024

Centered on the themes of large-language-model technology and entrepreneurial innovation, the AFAC2024 focus on real industry challenges and provides vast amounts of genuine industry data, and it also offers over 1.3 million (CNY) prize pool. The competition comprises three categories -Challenge Group, Start-up Group, and Enterprise Group – forming an integrated contests of algorithm, creative application and business practice. The three groups target individuals with technical backgrounds from academic institutions and corporations, startup teams, and SMEs, encouraging contestants to delve into continuous exploration and breakthroughs in AI algorithms, tackling complex technological challenges.

Four Tracks in the AFAC2024 Challenge Group

  1. Track 1 – Financial Instrument Learning
  2. Track 2 – Question Answering Based on Insurance Terms
  3. Track 3 – AIAC Multimodal Financial Research Intelligent Generation
  4. Track 4 – Contradiction Identification And Vulnerability Discovery in Long Texts of Financial Rules

Organizing Committee

Advisor:

Science and Technology Commission of Shanghai Municipality

Academic Collaborator: 

Supporting Unit:

China Computer Federation (CCF) Digital Finance 

Member Institutions: 

AFAC2024 Seminar at HKU IDS

Revisit the Seminar Recording at HKU IDS

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

IDS Guest Seminar: Leveraging Generative Models to Understand the Visual Cortex

IDS Guest Seminar - by Mr. Andrew LUO

Title: Leveraging Generative Models to Understand the Visual Cortex

Speaker: Andrew Luo, Ph.D. candidate, Carnegie Mellon University

Date: May 24, 2024
Time: 10:00am – 11:00am
Venue: HKU IDS Office, P307, Graduate House / Zoom

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

Register Now

Abstract

Understanding the functional organization of the higher visual cortex is a fundamental goal in neuroscience. Traditional approaches have focused on mapping the visual and semantic selectivity of neural populations using hand-selected, non-naturalistic stimuli, which require a priori hypotheses about visual cortex selectivity. To address these limitations, we introduce two data-driven methods: Brain Diffusion for Visual Exploration (‘BrainDiVE’) and Semantic Captioning Using Brain Alignments (‘BrainSCUBA’). BrainDiVE synthesizes images predicted to activate specific brain regions, having been trained on a dataset of natural images and paired fMRI recordings, thus bypassing the need for hand-crafted visual stimuli. This approach leverages large-scale diffusion models combined with brain-gradient guided image synthesis. We demonstrate the synthesis of preferred images with high semantic specificity for category-selective regions of interest. BrainSCUBA, on the other hand, generates natural language descriptions for images predicted to maximally activate individual voxels. This approach enables efficient fine-grained labeling of the entire higher visual cortex. Together, these two methods offer well-specified constraints for future hypothesis-driven examinations and demonstrate the potential of data-driven approaches in uncovering brain organization.

Speaker

Mr. Andrew Luo
Ph.D. candidate @ Carnegie Mellon University

Andrew Luo is a Ph.D. candidate in the joint program for Neural Computation and Machine Learning at Carnegie Mellon University, co-advised by Profs Michael Tarr and Leila Wehbe. His research focuses on understanding the functional organization of higher visual cortex using learnable generative models across modalities. Before joining CMU, he earned a B.S. degree in Computer Science from MIT in 2019.

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