Title: Accommodating Comprehensive Foundation Model Services over Heterogeneous Computational Resources

Time: 02:00pm 

Venue: CB 328

Speaker(s): Prof. Binhang Yua

Remark(s): 

Abstract

Deploying foundation model services is crucial to contemporary AI applications. We focus on deploying such services in heterogeneous, potentially decentralized settings to mitigate the substantial costs typically associated with centralized data centers. Our work relies on carefully designed scheduling algorithms and integrated system optimizations to fully unleash the potential of heterogeneous computational power across comprehensive serving paradigms, including data preparation pipelines, large-scale pretraining, reinforcement learning-based alignment, and agentic inference deployment.

About the speaker

Binhang YUAN is an Assistant Professor at the Department of Computer Science and Engineering (CSE), the Hong Kong University of Science and Technology (HKUST) since 2023. He received his Ph.D. and master's degrees from Rice University and his bachelor's degree from Fudan University. Before joining HKUST, he was a Postdoc at the Swiss Federal Institute of Technology Zurich (ETH Zurich). His main research interests are in distributed, decentralized, and heterogeneous machine learning systems for foundation models.

Title: Quantum Recursive Programming: Abstraction, Verification, and Implementation

Time: 10:00am 

Venue: CB 308

Speaker(s): Dr. Zhicheng Zhang

Remark(s): 

Abstract

Recursion is a fundamental concept in computer science, allowing complex algorithms to be expressed as compact, elegant, and modular programs. In quantum computing, recursion is appealing for the same reasons, but it also interacts in subtle ways with uniquely quantum features such as superposition and entanglement. Today, many quantum algorithms are still presented as low-level circuits, making them difficult to analyse, verify, and scale. In this talk, I will introduce quantum recursive programming, a high-level framework that supports modular programming of quantum algorithms. I will discuss two complementary perspectives on quantum recursive programs: a proof system for the formal verification of their correctness, and a framework for their efficient implementation. Together, these results show how quantum recursive programming can bring together modularity, correctness, and efficiency.

About the speaker

Zhicheng Zhang is a postdoctoral research fellow at the University of Technology Sydney. He earned his PhD under the supervision of Mingsheng Ying, Zhengfeng Ji, and Sanjiang Li. His research spans the theory of quantum computing, with a particular focus on the design of efficient quantum algorithms and the quantum software foundations needed for their high-level programming and reliable implementation.

Title: A Billion Medical Devices

Time: 10:30am 

Venue: CB 308

Speaker(s): Prof. Mayank Goel

Remark(s): 

Abstract

As we live longer, we are also living with more diseases. The need to identify illness symptoms and manage them has been increasing rapidly. Our personal devices have a role to play in helping us take care of ourselves outside a doctor’s clinic or hospital. Technology’s role in healthcare is already quite ubiquitous in the form of step counters and heart rate monitors. However, we can go far beyond these coarse measures. I will provide an overview of our efforts at building real-time machine-learning systems that measure depression symptoms, fatigue, sleep quality, and hyperactivity. While useful, these machine learning systems will probably never be perfectly accurate. The sensed information will always be noisy. Moreover, the user won’t know how to interpret and use measured information. I will present our ideas on how to make the inferred information actionable to the patients, caretakers, and doctors. I will also talk about the role of noisy machine learning systems and how a user can counter the system’s inherent error and uncertainty. Ultimately, we aim to build systems that help us manage our health without requiring perfectly accurate inferences.

About the speaker

Mayank is an Associate Professor in the Software and Societal Systems Department (S3D) and Human-Computer Interaction Institute (HCII) in the School of Computer Science at CMU. He leads the Smart Sensing for Humans (SMASH) Lab at CMU. His group develops new, practical, and deployable sensing and machine-learning solutions to solve problems in healthcare and reduce usage barriers for under-resourced populations. He regularly collaborates with mechanical and biomedical engineers, doctors, nurses, community health workers, and patients and their caregivers worldwide. His systems are currently used by hundreds of patients and deployed in several clinics and hospitals around the world.

Title: F.A.C.U.L.: The World’s First FPS AI Companion that Understands Human Language.

Time: 02:00pm 

Venue: HW 312

Speaker(s): Dr. Elvis Liu

Remark(s): 

Abstract

Arena Breakout is the first FPS game that allows players to interact with AI companions through natural language. Traditionally, FPS games restrict communication to simple commands like “attack” or “follow,” due to the limitations of existing input methods such as hotkeys and command wheels. These commands lack specificity, hindering players from giving complex tactical instructions like “clear the second floor” or “take cover behind that tree”.

Arena Breakout enables players to interact with an intelligent companion called F.A.C.U.L. using natural language. F.A.C.U.L. can understand complex instructions, provide feedback, and perform a series of tasks. Moreover, it can identify thousands of in-game objects, including buildings, vehicles, and collectable items, and accurately distinguish different colors and materials.

This revolutionary feature integrates advanced generative AI technologies, including voice input, LLMs, real-time text-to-speech, and image description, creating the most immersive experience for players and allowing them to work with human-like AI. F.A.C.U.L. was first presented at GDC 2025, the most important conference of the gaming industry, and later published at AAAI 2026.

About the speaker

Dr. Elvis Liu is the Head of AI at MoreFun Studios, Tencent, leading R&D in game AI, generative AI, physics simulation, and distributed virtual environments. He spearheaded the deep-reinforcement-learning sparring agents for Naruto Mobile, earning the Tencent Technology Breakthrough Award 2024, and created F.A.C.U.L., the first FPS AI companion enabling natural-language interaction with players.

Dr. Liu has over 20 years of experience spanning parallel and distributed systems, computer simulations, AI, computer graphics, and high‑performance computing. His contributions to these research areas have been acknowledged by invitations to serve on the Programme Committee of the key conferences in these research fields, including ACM SIGSIM-PADS, ACM/IEEE DS-RT, and Winter Simulation Conference. He is currently an Associate Editor of IEEE Transactions on Games and was named an Overseas High-Level Talent by the Chinese government in 2018.

Previously, he was an Assistant Professor at Nanyang Technological University in Singapore and an IRCSET Fellow in Ireland, where his IBM research on parallel simulation for exascale supercomputers was selected for ACM’s 19th Annual Best of Computing list, a collection of the best articles published in computing.

Title: Edge Intelligence: Multi-Modality Sensing to Revolutionize Chronic Diseases Management

Time: 10:30am 

Venue: HW312, Haking Wong Building

Speaker(s): Dr. Qian Zhang

Remark(s): 

Abstract

Rapid aging has led to an increase in the number of chronic disease patients, rising social medical costs, and a decline in the quality of life of patients. Under today's hospital-centered service model, patients are already seriously ill when diagnosed, and their subsequent prognosis is quite uncertain. The research attempts to transform the service model into a lifecycle management model, using IoT sensors and AI algorithm design for early diagnosis of diseases, and will also provide continuous health assessment and rehabilitation guidance to reduce the cost of medical services.

Dr. Qian Zhang will share their recent work on chronic disease assessment and intervention supported by intelligent sensing (multiple sensing modality will be leveraged) and AI algorithm design. She will use cuffless continuous blood pressure monitoring (leveraging mmWave and fiber optics), as well as COPD assessment and training (leveraging audio, IMU, as well as depth camera), as example for the sharing.

About the speaker

Dr. Qian Zhang joined the Hong Kong University of Science and Technology (HKUST) in September 2005. She is currently the Head of the Division of Integrative Systems and Design (ISD), Tencent Professor of Engineering, and Chair Professor of the Department of Computer Science and Engineering (CSE) at HKUST. She is also the Director of the Microsoft Research Asia-HKUST Joint Laboratory, the Co-Director of the Huawei-HKUST Innovation Laboratory. Prior to this, she worked at Microsoft Research Asia from July 1999 as a research manager in the Wireless and Networking Group. Dr. Zhang has published more than 500 refereed papers in leading international journals and major conferences. She is the inventor of more than 50 international patents. Her current research interests include the Intelligent Internet of Things (AIoT), smart health, mobile computing and sensing, wireless networks, and network security.

Dr. Qian Zhang is an IEEE Fellow and a Fellow of the Hong Kong Academy of Engineering (HKAE). Dr. Zhang served as the Editor-in-Chief of IEEE Transactions on Mobile Computing from 2020 to 2022. She is currently a member of the IEEE Infocom Steering Committee.

Title: Guiding with Search: Memory-Aware Abstraction for Scalable Vulnerability Detection

Time: 10:30am 

Venue: CB 308

Speaker(s): Prof. Heqing Huang

Remark(s): 

Abstract

Along with software development, system reliability and robustness have become major concerns. However, due to the complex program semantics, efficiently detecting these vulnerabilities remains challenging. This talk introduces a new perspective, search-space guided analysis with adapted memory organization, to significantly improve the performance of vulnerability detection. Unlike existing efforts that focus solely on the code itself, our approach integrates abstraction with runtime memory as both an oracle and a guidance for better vulnerability detection. The related work has received the ASPLOS Best Paper award and the Google Research Paper award. The artifact has also been successfully integrated into the most widely used compiler infrastructure, LLVM.

About the speaker

Heqing Huang is an assistant professor at the Department of Computer Science at City University of Hong Kong. His research focuses on software security, especially leveraging program analysis techniques to ensure software security rigorously. Specifically, his research takes advantage of both static and dynamic program analysis techniques as complements to address deficiency problems in existing vulnerability detection methods, such as fuzzing, symbolic analysis, and memory sanitization. On the other hand, he also aims to demonstrate the practicalness of the general research methods on specific application scenarios, e.g., Android and Linux kernels. His research has received the ASPLOS Best Paper and Google Research Paper awards. He also served on the CCS program committee and as a reviewer for TDSC, TOSEM, and TSE.

Title: Reasoning Beyond LLM: Code, Design, Strategy, and a Vision for Life After AGI: The Player Era

Time: 02:00pm 

Venue: CB328, HKU (Zoom broadcasting)
Lecture theater 1A, G/F, CDS-1 Build (On-site)

Speaker(s): Prof. Jin Song DONG

Remark(s): 

Abstract

The rise of code-centric Large Language Models (LLMs) has reshaped the software engineering world with tools like Copilot, DeepSeek, GPT-5, and Gemini-3 that can easily generate code. However, there is no guarantee of correctness for the code generated by LLMs, which suffer from the hallucination problem. The first part of this talk demonstrates that the program refinement calculus can be utilized as a formal-chain-of-thought to guide LLMs and verify the correctness of the LLM-generated code [POPL 2025]. The second part of this talk investigates LLM-aided System Design and Validation, where LLM-enhanced model-checking agents are developed [ICML 2025, NeurPIS 2025, ASE 2025]. The third part of this talk highlights the limitations of LLMs in solving complex planning and strategy analysis problems that require formal symbolic reasoning techniques [ICLR 2025, AAAI 2026]. At the end of this presentation, a new vision, "Player Era" for life after AGI, will be discussed. This new vision proposes that humanity will evolve into four distinct yet interconnected roles: the Player, the Explorer, the Co-Creator, and the Gate-Keeper, which serve as the pillars of a civilization redesigned for meaning, creativity, and responsibility.  

About the speaker

Jin-Song Dong is a professor at the National University of Singapore. His research spans a range of fields, including formal methods with LLM agents, safety and security systems, trusted AI, probabilistic reasoning, sports analytics, and verified LLM code synthesis. He co-founded the commercialized PAT verification system, which has garnered thousands of registered users from over 150 countries. Jin Song also co-founded the commercialized trusted machine learning system Silas (www.depintel.com), with 50K+ downloads. He served on the editorial board of ACM Transactions on Software Engineering and Methodology, Formal Aspects of Computing, and Innovations in Systems and Software Engineering, A NASA Journal. He has successfully supervised 34 PhD students, many of whom have become tenured faculty members at leading universities worldwide. He is a Fellow of the Institute of Engineers Australia. Jin Song developed Markov Decision Process (MDP) models for tennis strategy analysis using PAT, assisting professional players with pre-match analysis (beating the world's best). He created a new conference series on Sports Analytics (https://formal-analysis.com/isace/2026/).In his spare time, he serves as a tennis coach, taking pleasure in coaching his students and his three children, all of whom have reached the #1 national junior ranking in Singapore/Australia. Two of his children have earned NCAA Division I full scholarships in the US.  

Title: How to Trust a Quantum Box: Bell’s Theorem for Quantum Engineers

Time: 03:00pm 

Venue: CB 308

Speaker(s): Prof. Stefano Pironio

Remark(s): 

Abstract

How can we be sure that a quantum device really performs as intended? As quantum technologies promise secure communication, certified randomness, and unprecedented computational power, verifying their behavior becomes both essential and surprisingly challenging. One of the deepest results of 20th-century physics, Bell’s theorem, has become a practical tool: it allows us to test quantum behavior by treating devices as black boxes and observing only their input–output statistics. In this talk, I’ll introduce the central ideas behind this approach, known as device-independent quantum information, and show how fundamental physics offers new ways to build and trust quantum technologies.

About the speaker

Stefano Pironio is a theoretical physicist working on quantum information theory. He is a F.R.S.–FNRS Research Director at the Université libre de Bruxelles (ULB). He obtained his PhD from ULB in 2004 and held postdoctoral positions at Caltech, ICFO–The Institute of Photonic Sciences, and the University of Geneva. His work has been recognized with the QIPC Young Investigator Award, the De Donder Prize of the Belgian Academy of Sciences, and the Prix La Recherche.

Title: Weighted Q-Learning for Optimal Dynamic Treatment Regimes with Nonignorable Missing Covariates

Time: 03:30pm 

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

Speaker(s): Prof. Bo Fu

Remark(s): 

Abstract

Dynamic treatment regimes (DTRs) formalize medical decision-making as a sequence of rules for different stages, mapping patient-level information to recommended treatments. In practice,estimating an optimal DTR using observational data from electronic medical record (EMR)databases can be complicated by nonignorable missing covariates resulting from informative monitoring of patients. Since complete case analysis can provide consistent estimation of outcome model parameters under the assumption of outcome-independent missingness, Q-learning is a natural approach to accommodating nonignorable missing covariates. However, the backward induction algorithm used in Q-learning can introduce challenges, as nonignorable missing covariates at later stages can result in nonignorable missing pseudo-outcomes at earlier stages, leading to suboptimal DTRs, even if the longitudinal outcome variables are fully observed. To address this unique missing data problem in DTR settings, we propose 2 weighted Q-learning approaches where inverse probability weights for missingness of the pseudo-outcomes are obtained through estimating equations with valid nonresponse instrumental variables or sensitivity analysis. The asymptotic properties of the weighted Q-learning estimators are derived,and the finite-sample performance of the proposed methods is evaluated and compared with alternative methods through extensive simulation studies. Using EMR data from the Medical Information Mart for Intensive Care database, we apply the proposed methods to investigate the optimal fluid strategy for sepsis patients in intensive care units.

About the speaker

Bo Fu currently is a professor at the School of Data Science, Fudan University, China. He obtained the PhD degree from the department in 2003, and then moved to Cambridge University for a PostDoc fellow. He has worked at the Nanyang Technological University, Manchester University and University College of London. Prof. Fu’s research areas include, but not limited to, statistical theory and application, big medical data, etc. He has published many papers at the top journals in statistics or medicine.

Title: Large Language Models in the Financial Domain

Time: 04:30pm 

Venue: Room 301, Run Run Shaw Building

Speaker(s): Prof. Liwen Zhang

Remark(s): 

Abstract

The report provides a systematic review of the evolution of large language models (LLMs), tracing their development from the emergence of generative AI to the forefront of multimodal and world model advancements. It examines the paradigm shifts induced by AI across scientific research,education, and industrial domains, emphasizing data-driven and collaborative approaches that enhance decision-making processes. The study details the research team's latest innovations in the financial sector, including the Fin-R1 model, constructed through reinforcement learning to augment financial reasoning capabilities; the FinEval benchmark, designed for the rigorous assessment of Chinese financial domain knowledge; the VisFinEval benchmark, a scenario-driven multimodal evaluation framework that encompasses a comprehensive understanding of front-, mid-, and back-office financial operations; and the FinGAIA benchmark, tailored to evaluate AI agents within real-world financial contexts. These advancements underscore the transformative potential of LLMs in financial risk management, customer service, and business transformation, while actively facilitating the intelligent upgrading of the financial industry and contributing to the establishment of an efficient and secure financial ecosystem.

About the speaker

Liwen Zhang is a professor jointly appointed by the School of Statistics and Data Science and Dishui Lake Advanced Institute of Finance, Shanghai University of Finance and Economics. He is also the director of the AI Finance Development and Service Center, the director of the Shanghai Financial Intelligence Engineering Technology Research Center, the deputy director of the Key Laboratory of Mathematical Economics of the Ministry of Education, and the vice dean of the Institute of Data Science and Statistics.