IDS Guest Seminar: Towards General and Intelligent Autonomous Agent and System

IDS Guest Seminar - by Dr. Hongyang Li

Title: Towards General and Intelligent Autonomous Agent and System

Speaker: Hongyang Li, Research Scientist and PI at OpenDriveLab, Shanghai AI Lab

Date: Feb 19, 2024
Time: 2:30am – 3:30pm
Venue: HKU IDS, 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. 

Abstract

Register Now
End-to-end autonomous driving (E2E AD) has become a popular hit recently. Compared to conventional modular based design, the main advantages of E2E AD descend from the global optimization across sub-modules in the system, and the unanimous objective towards planning and control task. Some leading corporations from industry have also adopted such an end-to-end philosophy and rolled out to customers on the product end. In this talk, we will first walkthrough the key milestones and roadmap of E2E AD, including the first prototypical work UniAD, proposed by OpenDriveLab and recognized as IEEE CVPR 2023 Best Paper. Then the pivotal challenges (generalization, world model, etc.) that the community currently are confronted with are discussed. With the prevalence of foundation models, how to utilize LLM/VLM techniques and distill common knowledge from experienced human experts are covered as well.The talk would be concluded with some future perspectives on building towards a general and intelligent system in a wide span of downstream applications for the next couple years.

Speaker

Dr. Hongyang Li
Research Scientist and PI @ OpenDriveLab, Shanghai AI Lab

Dr. Hongyang Li is a full-time Research Scientist and PI at OpenDriveLab, Shanghai AI Lab. His research focus is on autonomous driving and embodied AI. He obtained the Ph.D. degree from The Chinese University of Hong Kong in 2019 and worked a few years at industry on L2 assistant autonomous driving. In 2021, he built and led the OpenDriveLab team at Shanghai AI Lab. He proposed the bird’s-eye-view perception work, BEVFormer, that won Top 100 AI Papers in 2022 and was recognized by Jensen Huang, CEO of NVIDIA in a Keynote at CompteX 2023. He lead the end-to-end autonomous driving project, UniAD and won the IEEE CVPR 2023 Best Paper Award. UniAD has a large impact both in academia and industry, including the recent rollout to customers by Tesla in FSD V12 version. He is the Notable Area Chair at NeurIPS 2023, the Senior Member of IEEE. For more details, please visit https://opendrivelab.com is a fellow of the IEEE and a distinguished member of the
ACM.

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

1st Conference on Parsimony and Learning (CPAL) was successfully held at HKU on January 3 – 6, 2024!

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1st Conference on Parsimony and Learning (CPAL) was successfully held at HKU on January 3 - 6, 2024!

On January 3-6, 2024, HKU Musketeers Foundation Institute of Data Science (HKU IDS) proudly hosted and sponsored the inaugural Conference on Parsimony and Learning (CPAL) at The University of Hong Kong. This annual research meeting, set foot in the newly established data science research institute for its first gathering, is dedicated to exploring the parsimonious, low dimensional structures prevalent in machine learning, signal processing, optimization, and more.

Revisit the programme of CPAL2024 at the HKU IDS landing page!

 

The Conference has invited internationally renowned data scientists from prestigious universities all over the world. Their vision is to create a scientific forum where faculty, students, industrial partners, and researchers, from various fields can share insights and work towards a unified theoretical and computational framework for understanding intelligence and science from the perspective of parsimonious learning. Over 200 participants attended the conference the event, during which insightful lectures by our 9 Keynote Speakers coming from prestigious institutions such as UCLA, University of Pennsylvania, New York University, KAIST, and more, 30 diversified presentations by researchers with accepted papers as well as 16 Rising Stars Awardees who are early-career scholars, 2 Half-day Tutorials by area experts in machine learning and artificial intelligence, and countless fruitful discussions, were featured.

Day 1 of the event, which included a glamorous Opening Ceremony in the early morning of January 3 ,2024, was graced by Guests of Honours including Prof Xiang Zhang, President and Vice-Chancellor of The University of Hong Kong, Mr Ka-chai Leong, benefactor of the HKU IDS from The Musketeers Education and Culture Charitable Foundation Ltd, Prof Yi Ma, one of the CPAL2024’s General Chair & Director as well Professor, Chair of Artificial Intelligence, HKU IDS & Department of Computer Science, and Prof Harry Shum, another CPAL2024’s General Chair & IAS Professor-at-Large Emeritus, The Hong Kong University of Science and Technology. CPAL is meant to be a new chapter in conferences which emphasizes on true academic values as both the senior and rising researchers are rendered a chance to exchange latest research ideas and interdisciplinary knowledge in data science.

 

Apart from the main programme of the conference at daytime, HKU IDS also shared joy with conference attendees in organizing social and networking events including a dinner banquet at a traditional Chinese-style restaurant on January 4, 2024 evening, and a unique Ding-ding tour on 3 specially designed party trams on January 5, 2024 evening. The attendees, after being immersed in an engaging, yet fairly tight, schedule at the conference on parsimonious learning, got a chance to taste the cultural distinctiveness of the city of Hong Kong through cuisine and entertainment, and also to interact further with other collaborators in a light-hearted manner. Both events were received with positive feedback.

 

In general, CPAL2024 was met with enthusiastic responses. The team at HKU IDS echoes Professor Yi Ma’s conference vision of trying to “make a different conference” which could render better service to the research communities, and looks forward to carrying out other knowledge exchange initiatives at the Institute in an international scale.

Full-time Office Attendant

Careers at IDS

Full-time Office Attendant 全職辦公室服務員

Position  

Applications are invited for the appointment as a Full-time Office Attendant in the HKU Musketeers Foundation Institute of Data Science in the HKU Musketeers Foundation Institute of Data Science (HKU IDS), (to commence as soon as possible for one year with the possibility of renewal, subject to satisfactory performance).

Qualification & Job Duties

Applicants should have completed Form 3 or above. They should be able to speak and write simple English and Chinese, self-motivated, with the ability to work independently and proactively. The appointee is expected to perform duties, including but not limited to general and students research premises cleaning, preparation of meeting logistics and refreshments, receiving and dispatching mails and documents within and outside campus, and rendering office assistance in events and other Institute’s organized activities. Those with experience in tertiary institutions would be an advantage.

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, or until the post is filled, whichever is earlier. 

Apply Now

職位
香港大學同心基金數據科學研究院聘請：全職辦公室服務員 (以一年臨時合約形式聘用；若工作表現滿意，可獲續聘)

學歷要求及工作範疇
應徵者須具備中三或以上程度，能操流利粵語，略懂英語及普通話，並能閱讀及書寫簡單中、英文更佳。受聘者須積極主動、守時盡責及具獨立處理事務能力。受聘者須負責執行一般雜務，包括清潔打掃辦公室及學生研究中心、準備會議及茶水、外勤、文件派遞、協助處理辦公室事務及為研究院的活動提供支援等。具大專院校工作經驗者將獲優先考慮。

申請方法
大學只接受透過網上系統遞交的申請。應徵者請到大學人才招聘網站遞交網上申請及上載最新的個人履歷。大學會盡快展開遴選工作。 

Executive Assistant (at the rank of Clerk II)

Careers at IDS

Executive Assistant (at the rank of Clerk II)

Position  

Applications are invited for the appointment as Executive Assistant (at the rank of Clerk II) in the HKU Musketeers Foundation Institute of Data Science (HKU IDS) (Job Ref.: 524513), (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 render clerical support to the operation of the Institute, including support to events and workshops, research project funding management and handling of grants applications, operation on e-Procurement system, and general programme administration (Research Postgraduate (RPg) & Taught Postgraduate (TPg) courses). He/she will also be responsible for the coordination of research projects initiated by the Institute with mainland partners, and carry out other duties as assigned. 

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 March 31, 2024, or until the post is filled, whichever is earlier.

Apply Now

Clerical Assistant

Careers at IDS

Clerical Assistant

Position  

Applications are invited for the appointment as a Clerical Assistant in the HKU Musketeers Foundation Institute of Data Science (HKU IDS) (Job Ref.: 524511), (to commence as soon as possible, for one-year contract, with the possibility of renewal or change to a fixed-term contract depending on satisfactory working performance).

Qualification  

Applicants should have completed F.5 or above. They should have a fair command of written and spoken English and Chinese; proficiency in Microsoft Office applications; willingness to learn, good interpersonal skills; self-motivation, and be detail-minded with a strong sense of responsibility.

The appointee will provide comprehensive clerical and office support including room booking requests handling, purchase of stationery, mail dispatch, photocopying, office general enquiries handling and documents filing. He/She will be required to assist in reception service during the regular meetings and gatherings of the Institute, as well as serving as the backup for Office Attendant when he/she is on leave. He/She will also provide support to office maintenance and supply, liaise with the Estates Office and Service Providers in the University, assist in the Institute’s event logistics, and perform any other duties as assigned by the Institute. Shortlisted candidates will be invited to attend 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 March 31, 2024, or until the post is filled, whichever is earlier. 

Apply Now

IDS Guest Seminar: AI-driven Software Security

IDS Guest Seminar - by Professor Hao Chen

Title: AI-driven Software Security

Speaker: Hao Chen, Professor, Department of Computer Science, University of California, Davis.

Date: Jan 10, 2024
Time: 10:00am – 11:00am
Venue: Tam Wing Fan Inno wing Two, HKU / Zoom

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

Abstract

As more of our society moves to and relies on computer systems, the software that runs on these systems has become so large and complex that traditional approaches to software security are unable to keep up with it. On the other hand, machine learning prospers on large, diverse data sets. I will discuss our endeavor to apply machine learning to software security. I will show how to transform fuzz testing from a random, ad hoc approach to a principled approach based on machine learning techniques, and demonstrate that this principled approach solves important problems that challenge traditional software testing. I will also discuss how machine learning can benefit from software engineering. I will show how to use fuzz testing to assist code representation learning and to improve program understanding tasks. I will discuss the challenges, opportunities, and open problems in AI-driven software security.

Speaker

Professor Hao Chen
Professor @ University of California, Davis
Hao Chen is a professor at the Department of Computer Science at the University of California, Davis. He received his PhD at the Computer Science Division at the University of California, Berkeley. His research interests are computer security, software engineering, and machine learning. He won the US National Science Foundation CAREER award in 2007, and UC Davis College of Engineering Faculty Award in 2010. He is a fellow of the IEEE and a distinguished member of the ACM.

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

Summer Research Programme 2024 – Welcoming a New Batch of Elite Students Worldwide!

HKU IDS is going to participate in the Summer Research Programme (SRP) 2024!

The University of Hong Kong (HKU) Summer Research Programme (“the Programme”) 2024 is a 10-week intense research training programme with networking and extra-curricular activities for elite students around the world who are interested in pursuing research postgraduate studies at HKU. Application has commenced from now, all through January 26, 2024!

Outstanding undergraduate students are welcome to join the programme from June to August 2024, with our HKU IDS Scholars who are experts in interdisciplinary research fields in data science.

Learn more

IDS Seminar: Geometric Regularizations for 3D Shape Generation

IDS Seminar - by Dr. Qixing Huang from University of Texas at Austin

Title: Geometric Regularizations for 3D Shape Generation

Speaker: Dr. Qixing Huang, Associate Professor with tenure at the Computer Science Department, The University of Texas at Austin
Moderator: Dr. Yanchao Yang, Assistant Professor, HKU IDS / Department of Electrical and Electronic Engineering

Date: Dec 18, 2023
Time: 3pm – 4pm

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

Abstract

Generative models, which map a latent parameter space to instances in an ambient space, enjoy various applications in 3D Vision and related domains. A standard scheme of these models is probabilistic, which aligns the induced ambient distribution of a generative model from a prior distribution of the latent space with the empirical ambient distribution of training instances. While this paradigm has proven to be quite successful on images, its current applications in 3D generation encounter fundamental challenges in the limited training data and generalization behavior. The key difference between image generation and shape generation is that 3D shapes possess various priors in geometry, topology, and physical properties. Existing probabilistic 3D generative approaches do not preserve these desired properties, resulting in synthesized shapes with various types of distortions. In this talk, I will discuss recent work that seeks to establish a novel geometric framework for learning shape generators. The key idea is to model various geometric, physical, and topological priors of 3D shapes as suitable regularization losses by developing computational tools in differential geometry and computational topology. We will discuss the applications in deformable shape generation, latent space design, joint shape matching, and 3D man-made shape generation.

Speaker

Dr. Qixing Huang
Associate Professor @ Computer Science Department, The University of Texas at Austin

Qixing Huang is an associate professor with tenure at the computer science department of the University of Texas at Austin. His research sits at the intersection of graphics, geometry, optimization, vision, and machine learning. He has published more than 100 papers at leading venues across these areas. His research has received several awards, including multiple Best Paper awards, the Best Dataset Award at Symposium on Geometry Processing 2018, the IJCAI 2019 Early Career Spotlight, and the 2021 NSF Career award. He has also served as area chair of CVPR, ECCV, ICCV, and technical papers committees of SIGGRAPH and SIGGRAPH Asia, and co-chaired Symposium on Geometry Processing 2020.  

Moderator

Dr. Yanchao Yang
Assistant Professor @ HKU IDS & Department of Electrical and Electronic Engineering

Dr Yanchao Yang is an Assistant Professor in the Department of Electrical and Electronic Engineering (EEE) and the HKU Musketeers Foundation Institute of Data Science (HKU-IDS). Before joining HKU, he was a Postdoctoral Research Fellow at Stanford University with Prof. Leonidas J. Guibas at the Geometric Computation Group. He received his Ph.D. from the University of California, Los Angeles (UCLA), working with Prof. Stefano Soatto. Earlier, he obtained his Master’s and Bachelor’s degrees from KAUST and USTC, respectively. He researches at the intersection of computer vision, machine learning, and robotics, with a long-term goal in developmental embodied intelligence. 

For his full biograpy, please browse: https://datascience.hku.hk/people/yanchao-yang/

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

IDS Guest Seminar: Deep-Learning-Enabled Probabilistic Models for Knowledge Discovery

IDS Guest Seminar - by  Dr Sulin Liu from MIT

Title: Deep-Learning-Enabled Probabilistic Models for Knowledge Discovery

Speaker: Dr Sulin Liu, Postdoctoral Researcher, Massachusetts Institute of Technology

Date: Dec 14, 2023
Time: 10:00am – 11:00am
Venue: HKU IDS, 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

Probabilistic models provide a powerful framework for modeling diverse data distributions encountered in the real world. These models have found extensive applications in ML-driven knowledge exploration and discovery, including: 1) sequential experiment design and optimization using probabilistic surrogate models like Gaussian processes, and 2) generation of novel designs through generative models. However, a key challenge hindering the application of probabilistic models in real-world scenarios is the lack of scalable solutions that maintain their expressive power. This talk will explore how to overcome this challenge by leveraging the concept of “amortization” through deep neural networks.

First, I will demonstrate how to accelerate the identification of Gaussian process hyperparameters (the major computational bottleneck) by training a single neural network to “amortize” this computationally expensive process across various zero-shot tasks. In the second part, a novel class of generative models will be introduced for flexible and scalable modeling of discrete objects, achieved by learning a neural network to approximate the marginal probability.

Speaker

Dr. Sulin Liu
Postdoctoral Researcher @ Massachusetts Institute of Technology

Dr Sulin Liu is a postdoctoral researcher at MIT working with Rafael Gómez-Bombarelli on machine learning for accelerating science discovery. He received his PhD in Electrical and Computer Engineering from Princeton University, advised by Ryan Adams and Peter Ramadge. His PhD research focuses on developing deep-learning-enabled probabilistic inference and generative models for knowledge discovery. He has also worked as a research intern at Meta Research Adaptive Experimentation team, mentored by Ben Letham and Eytan Bakshy. Prior to his PhD, Sulin received his bachelor’s degree in Electrical Engineering from National University of Singapore.

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

IDS Seminar: Human-AI Interaction in the Age of Large Language Models

Host: HKU Musketeers Foundation Institute of Data Science
Co-host: Department of Computer Science, HKU

IDS Seminar - by Dr. Diyi Yang from Stanford University

Title: Human-AI Interaction in the Age of Large Language Models

Speaker: Dr. Diyi Yang, Assistant Professor, Department of Computer Science, Stanford University
Moderator: Dr. Tao Yu, Assistant Professor, HKU IDS / Department of Computer Science

Date: Dec 11, 2023
Time: 2:30pm – 3:30pm

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 have revolutionized the way humans interact with AI systems, transforming a wide range of applications and disciplines. In this talk, we discuss several approaches to enhancing human-AI and AI-AI interactions using LLMs. The first one explores how large language models transform computational social science, and how human-AI collaboration can reduce costs and improve the efficiency of social science research. We then introduce efficient machine unlearning techniques to enable LLMs to forget sensitive user data if needed, towards secure and responsible interaction. The last part looks at AI-AI interaction via a dynamic LLM agent network for multi-agent collaboration on complicated reasoning and generation tasks. We conclude by discussing how LLMs enable collaborative intelligence by redefining the interactions between humans and AI systems.

Speaker

Dr. Diyi Yang
Assistant Professor @ Department of Computer Science, Stanford University

Dr. Diyi Yang is an assistant professor in the Computer Science Department at Stanford University, also affiliated with the Stanford NLP Group, Stanford HCI Group, and Stanford Human-Centered Artificial Intelligence (HAI). Diyi received her PhD from Carnegie Mellon University, and her bachelor’s degree from Shanghai Jiao Tong University. Her research focuses on natural language processing, machine learning, and computational social science. Her work has received multiple best paper nominations or awards at top NLP and HCI conferences (e.g., ACL, EMNLP, SIGCHI, ICWSM, and CSCW). She is a recipient of IEEE “AI 10 to Watch” (2020), Intel Rising Star Faculty Award (2021), Samsung AI Researcher of the Year (2021), Microsoft Research Faculty Fellowship (2021), NSF CAREER Award (2022), and an ONR Young Investigator Award (2023).

Moderator

Dr. Tao Yu
Assistant Professor @ HKU IDS & Department of Computer Science

Dr. Tao Yu is an Assistant Professor in the HKU IDS and the Computer Science Department of the University of Hong Kong. He is also a Postdoctoral Research Fellow in the Department of Computer Science and Engineering at University of Washington and a co-director of the NLP group at the University of Hong Kong. His research interest is in Natural Language Processing and Deep Learning, with a focus on designing and building conversational natural language interfaces that can help humans explore and reason over data in any application (e.g., relational databases and mobile apps) in a robust and trusted manner. He has published and served in the program committee at ACL, EMNLP, ICLR, NAACL, etc. He co-organized the Interactive and Executable Semantic Parsing workshop at EMNLP 2020.

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