Title: From Digital Human to Humanoids

Time: 10:00am 

Venue: Rn 301, 3/F Run Run Shaw Building

Speaker(s): Prof. He Xiaodong, JD Group

Remark(s): 

Abstract

Thanks to significant technological improvements in artificial intelligence over the last decade, especially the recent development of large language and vision models that enable AI systems to communicate with humans in a very natural manner, we are now able to create high quality “digital humans” that can be deployed for various business tasks. Moreover, by integrating these models into robots and other devices, we are able to build humanoids that can listen, watch, and talk to users, serving us in a broad range of scenarios. In this talk, I will introduce our latest breakthroughs and applications in these areas and demonstrate how AI technology profoundly changes business models and user experiences while creating societal value.

About the speaker

Dr. Xiaodong He is IEEE Fellow, Senior Vice President of JD Group (JD.COM), and managing director of JD AI Research. He is the recipient of the Wu Wen-Jun AI Science & Technology Outstanding Contribution Award. He has published more than 200 papers with over 60,000 citations, and has received awards including the IEEE SPS Best Paper Award, ACL Outstanding Paper Award, and CIKM Test-of-Time Award. Since joined JD.com in 2018, he has led JD's AI effort and developed JD's JoyAI Large Language Model, and has incubated various AI products including JD's intelligent customer service and multimodal digital human Live Streaming, etc. These products and technologies have been deployed across industries including retail, logistics, finance, and healthcare. Before joining JD.com, Dr. Xiaodong He worked with Microsoft Research Redmond as Principal Researcher and Head of the Deep Learning Technology Center (DLTC). Dr. He holds a bachelor's degree from Tsinghua University and Ph.D. from the University of Missouri-Columbia. He serves as Affiliate Professor at the University of Washington (Seattle) and other institutes.

Title: Towards Efficient and Adaptive RL Algorithms in Dynamic and Multi-Agent Environments

Time: 01:30pm 

Venue: CB328

Speaker(s): Prof. Shuai Li, Shanghai Jiao Tong University

Remark(s): 

Abstract

"Reinforcement Learning (RL) in dynamic and multi-agent environments poses critical theoretical and practical challenges. These include (i) the difficulty of decomposing complex structured actions, (ii) robust convergence across diverse and non-stationary scenarios, and (iii) balancing individual learning dynamics with multi-agent game-theoretic interactions. This talk presents a unified framework addressing these challenges through (a) integrating decomposable action structures into agent learning,
(b) leveraging statistical optimization techniques for automatic adaptation to environmental variations, and (c) designing equilibrium-aware algorithms for balancing multi-agent learning and strategic interactions.
I will highlight key theoretical contributions from my group, including optimal regret bounds and robust convergence guarantees under complex function approximations. These results, published in top venues such as SODA, COLT, and ICML, have established several state-of-the-art benchmarks. I will also share successful real-world applications: achieving a bronze medal in the International SAT Competition with Huawei’s EDA parallel solver, reducing A/B testing durations by 10% in Tencent’s WeChat experimentation platform, and lowering privilege restriction rates by 69% (from 6.36 to 1.96 per 1,000) with the same level of risk control capability in Ant Group’s Risk Management Platform.
Looking forward, I will outline new directions for developing scalable RL theories and algorithms under dynamic environments with complex function approximation and multi-agent Markov games."

About the speaker

Shuai Li is an Associate Professor at the AI School of Shanghai Jiao Tong University and Deputy Director of the John Hopcroft Center for Computer Science. Her research focuses on reinforcement learning theory and algorithms for autonomous decision-making in dynamic environments, as well as the analysis of diffusion and large language models. She serves as Area Chair or Senior PC member for leading conferences such as ICML, NeurIPS, ICLR, ACL, AISTATS, IJCAI, AAMAS, and UAI. She has received the AAAI-IAAI Deployed Application Award, Shanghai Rising Talent, Shanghai Xuhui Guangqi Talent, Google PhD Fellowship, Huawei Spark Award, Tencent Outstanding Mentor Award, and international recognition in the SAT Competition. Prof. Li’s algorithms have been successfully deployed in large-scale industrial systems, significantly improving performance and efficiency.

Title: Surgical Data Science for Women's Health

Time: 04:30pm 

Venue: CB308, HKU

Speaker(s): Pierre Jannin and Prof. Krystel Nyangoh Timoh

Remark(s): 

Abstracct

Despite the growing awareness on health disparities, there still remains an important gap in research efforts between men’s and women’s health. A PubMed’s request in December 2024 on the two most common gender specific surgeries returned 22,534 results for “radical prostatectomy”, and only 3,779 for “radical hysterectomy”. There are other important women’s health issues which still have too little consideration, such as endometriosis which affects approximately 10% of women worldwide, and would benefit from research efforts in computer assisted surgery. 

Surgical data science has an important potential for improving surgical management. This may impact the whole perioperative process from diagnosis, strategy decision, planning, performance and post-operative evaluation, as well as initial and continuous learning. In the presentation, we will present the challenges related to women health and how the ones related to surgical management can be addressed by Surgical Data Science and more specifically by studying and understanding surgical skills. We will present our first results in the context of women health as well as results from other surgical specialties (e.g., neurosurgery, orthopedics) showing potentiality. Examples will cover different aspects of surgical skills from technical to non-technical ones.

About the speaker

Prof. Jannin is a INSERM Research Director at the Medical School of the University of Rennes (France). He is the head of the MediCIS research group from both UMR 1099 LTSI, Inserm research institute and University of Rennes. He has more than 30-year experience in designing and developing computer assisted surgery systems.

Prof. Timoh is Professor of Gynecology at the University of Rennes and surgeon at Rennes University Hospital (France). She is Head of the Department of Anatomy, Director of the School of Surgery in Rennes, Scientific Director of the Living Lab "Women's Health", and member of the MediCIS research group from both UMR 1099 LTSI, Inserm research institute and University of Rennes.

Title: Markets as Approximation Algorithms: Their Design and Analysis

Time: 10:00am 

Venue: CB308, HKU

Speaker(s): Yaonan Jin

Remark(s): 

Abstracct

Numerous modern applications in computer science involve self-interested participants, whose incentives are typically misaligned with those of the algorithm designers. Examples abound, such as auctions for online advertising, pricing schemes in e-commerce, and resource allocation in various contexts. The theory of EconCS, an intersection of Computer Science and Economics, addresses challenges arising in these scenarios.

In this talk, I will survey my research in EconCS, with an emphasis on the lens of approximation -- a viewpoint brought by computer scientists that has greatly enriched our understanding of markets. Below are two representative results:

1. First Price Auction, the “most canonical and pivotal” auction format -- the unifying algorithm by Google and other major platforms for online advertising -- guarantees a tight 1-1/e^2≈86.47% approximation to the theoretically optimal (but utopian) welfare.
2. Uniform Pricing, the “most practical and ubiquitous” pricing scheme -- seen across online and offline retail -- ensures a tight 38.17% approximation to the theoretically optimal (but impractical) revenue.

About the speaker

Yaonan Jin is a full-time researcher at Huawei's Taylor Lab. His research interests encompass Theoretical Computer Science, with an emphasis on Algorithmic Economics. Before joining Huawei, he obtained his PhD from Columbia University in 2023 (advised by Xi Chen and Rocco Servedio). Before that, he obtained his MPhil from Hong Kong University of Science and Technology in 2019 (advised by Qi Qi) and his BEng from Shanghai Jiao Tong University in 2017.

Title: Quantum Bayes' rule and Petz transpose map from the minimum change principle

Time: 10:30am 

Venue: CB308, HKU

Speaker(s): Cliff Liu

Remark(s): 

Abstracct

Bayes' rule, which is routinely used to update beliefs based on new evidence, can be derived from a principle of minimum change. This principle states that updated beliefs must be consistent with new data, while deviating minimally from the prior belief. Here, we introduce a quantum analog of the minimum change principle and use it to derive a quantum Bayes' rule by minimizing the change between two quantum input-output processes, not just their marginals. This is analogous to the classical case, where Bayes' rule is obtained by minimizing several distances between the joint input-output distributions. When the change maximizes the fidelity, the quantum minimum change principle has a unique solution, and the resulting quantum Bayes' rule recovers the Petz transpose map in many cases.

About the speaker

Ge Bai is an Assistant Professor at The Hong Kong University of Science and Technology (Guangzhou). His research focuses on quantum causal inference, quantum machine learning and quantum communication network theory. Before his current position, he was a postdoctoral fellow at the National University of Singapore. He received his PhD from the University of Hong Kong, where he was awarded the Hong Kong Young Scientist Award.

Title: Sublinear Algorithms for Graph Optimization

Time: 10:30am 

Venue: CB308, HKU

Speaker(s): Cliff Liu

Remark(s): 

Abstracct

In the era of big data, massive datasets emerge in almost every application domain. Processing data in a traditional way, which takes at least time and space linear in the input size, becomes more-and-more commercially unaffordable. Important topics within sublinear algorithms include parallel computation (sublinear time), streaming algorithms (sublinear space), and communication complexity (sublinear communication), to name a few. In this talk, I will investigate the above topics through the lens of two fundamental problems: graph connectivity and bipartite matching. For graph connectivity, I will start with the simplest possible parallel algorithm with running time O(log n), then introduce the recent breakthroughs of how to break the O(log n) barrier. I will also show how to achieve o(log n) time and linear work, which is optimal. For bipartite matching, I will start with a simple approximate streaming algorithm whose correctness proof is based on combinatorial auctions, then I will introduce our recent work that finds the exact bipartite matching in the semi-streaming model that takes sublinear passes unless the graph is extremely dense.

About the speaker

Cliff Liu is an associate professor at Shanghai Jiaotong University. He was a postdoc in CMU theory group and obtained his PhD from Princeton. His research is around sublinear algorithms, graph algorithms, and data structures. Cliff was a recipient of the Gordon Y.S. Wu Fellowship and NSFC Science Fund Program for Distinguished Young Scholars.

Title: Algorithmic Optimization of Carbon Footprint in Long-Haul Heavy-Duty E-Truck Transportation

Time: 10:00am 

Venue: CB308, HKU

Speaker(s): Minghua Chen

Remark(s): 

Abstracct

The US transportation sector accounted for 37% of the country's total CO2 emissions in 2023. While representing only 0.4% of on-road vehicles, long-haul heavy-duty trucks contribute a disproportionate 12% of transportation carbon emissions, making their decarbonization a critical leverage point for climate change mitigation. Electrifying long-haul heavy-duty trucks represents a vital step toward decarbonizing the trucking sector, yet realizing their full potential requires minimizing the carbon footprint of timely deliveries. This involves optimizing electric truck travel between distant locations across the national highway system under strict deadline constraints. The resulting task, encompassing strategic path, speed, and charging planning, is combinatorial in nature and proven NP-hard. Consequently, traditional methods, including our recent approximation algorithms, struggle to optimize at scale. To this end, we present a novel stage-expanded graph formulation that reduces modeling complexity while revealing exploitable problem structure. Our approach naturally decomposes the problem into tractable subproblems, enabling efficient coordination between routing and charging decisions while maintaining manageable graph sizes. Leveraging these structural insights, we design an efficient algorithm with theoretical performance guarantees. Simulations using real-world data across the US highway system demonstrate that our method achieves an additional 25% carbon reduction beyond the 36% reduction from electrification alone, yielding a total 61% emissions decrease. Furthermore, our carbon-optimized strategy, applicable across various truck types, can achieve comparable carbon reductions nine years sooner than relying solely on zero-emission truck adoption, providing a powerful tool in addressing climate change.

About the speaker

Minghua is a Presidential Chair Professor in School of Data Science, The Chinese University of Hong Kong, Shenzhen. He received the Eli Jury award from UC Berkeley in 2007 and The Chinese University of Hong Kong Young Researcher Award in 2013. His recent research interests include online optimization and algorithms, machine learning for optimization with hard constraints and its application in power system operations, intelligent transportation, distributed optimization, and delay-critical networking. He is an ACM Distinguished Scientist and an IEEE Fellow.

Title: Keynote talk by Zack KASS – Former Head of Go-To-Market for OpenAI

Time: 11:00am 

Venue: KB223, 2/F, Knowles Building, HKU

Speaker(s): Zack Kass

Remark(s): 

Keynote Abstract:
In the age of Artificial Intelligence, everything is changing, from how we work to how we create. How can you take advantage of this change to improve your life and career? As the former Head of Go-To-Market for OpenAI, Zack Kass has been at the forefront of AI's global transformation for over 15 years. In this compelling keynote, Zack counters the conventional dystopian narrative of AI, sharing a positive vision that offers the potential for game-changing innovation, opportunity, and human potential. Audiences will come away understanding how technology will reshape their industries, organizations, workforce, and lives for the better.

About Zack Kass:
Zack Kass is a globally recognized AI advisor and thought leader. With over 16 years of industry experience, he most recently served as Head of Go-To-Market at OpenAI, where he built the company's sales, solutions, and partnerships teams. Zack now advises Fortune 1000 companies and global institutions on long-term AI strategy and transformation. He also serves as Executive-in-Residence at the University of Virginia’s McIntire School of Commerce, where he contributes to curriculum development and discourse on the socioeconomic impact of AI.

Registration: https://shorturl.at/dehPt

Title: JUPAS Information Session 2025

Time: 09:00am 

Venue: Digital Literacy Lab and Student Advisory Services area, 2/F, Chi Wah Learning Commons, Centennial Campus, HKU

Speaker(s): 

Remark(s): 

JUPAS Information Session 2025: Discover the Future of Computing and Data Science

The School of Computing and Data Science is thrilled to invite DSE applicants to the captioned event, which is designed to provide S.6 students with a comprehensive understanding of the exciting opportunities available in the fields of computing, data science, and related disciplines.

Event Highlights

Mini Lectures
Gain insights into key areas shaping the future of technology and data:

Mingling with CDS Professors
Meet and interact with our professors. This is your chance to ask questions, learn about our programmes, and gain valuable advice about your academic and career path.

Alumni Sharing
Hear from our accomplished alumni as they share their experiences, career journeys, and how their education has propelled them to success.

Final Year Project Showcase
Be inspired by innovative projects created by our final-year students, showcasing the practical applications of their knowledge and skills in computing and data science.

Event Details

Date: 9 May, 2025 (Friday)
Time: 09:00 – 12:30
Venue: Digital Literacy Lab and Student Advisory Services area, 2/F, Chi Wah Learning Commons, Centennial Campus, HKU

Who Should Attend?
This event is tailored for JUPAS applicants who are considering pursuing studies in computing, data science, artificial intelligence, actuarial science, statistics, or financial technology.

Why Attend?

• Gain a deeper understanding of the programmes offered by the School of Computing and Data Science.
• Learn about career opportunities and industry trends.
• Connect with professors, alumni, and current students.
• Get inspired by real-world applications of computing and data science.

How to Register
Spaces are limited, so secure your spot today!
Register Now: https://forms.gle/Zs5m8bcx7jBJcfCf6 

Title: ITS Industrial Seminar Series 2025 – Autonomous driving: Embracing the era of large-scale commercialization

Time: 05:00pm 

Venue: Innovation Wing Two, G/F Run Run Shaw Building, HKU

Speaker(s): Dr. Luyi Mo

Remark(s): 

Event Details:  https://www.cds.hku.hk/news/its-industrial-seminar-series-2025-autonomous-driving-embracing-the-era-of-large-scale-commercialization/