Speaker: Dr. WANG Zhuang Senior Applied Scientist, Amazon Annapurna Labs Title: Scaling LLM Pre-training through Optimizing Data- and Management-Plane Communications

Speaker: Professor ZHANG Hongce Assistant Professor, Microelectronics Thrust, The Hong Kong University of Science and Technology (Guangzhou) Title: AI for Hardware Formal Verification

Speaker: Patrick Groeneveld, Senior Fellow at AMD and adjunct lecturer in Stanford University’s Department of Electrical Engineering, Title: Place, Route, and Evolve: Design Lessons Shared by Silicon and Biology

Speaker: Professor Ansuman BANERJEE Advanced Computing and Microelectronics Unit Indian Statistical Institute, Kolkata Title: Agentic AI and Formal Verification Joining Hands

Speaker: Dr. Yiyu Shi currently a professor in the Department of Computer Science and Engineering at the University of Notre Dame Title: When Small Variations Become Big Failures: Reliability Challenges in Computing-in-Memory Neural Accelerators

Speaker: Dr. ZHANG Xujun Postdoctoral Researcher, Zhejiang University Title: Methodological Study on Machine Learning-Based Molecular Docking

Speaker: Mr. MAO Xinyu, PhD in Theoretical Computer Science, University of Southern California, Title: Black-Box Separation between Multi-Collision Resistance and Collision Resistance

HOW TO PROVE POST-QUANTUM SECURITY FOR SUCCINCT NON-INTERACTIVE REDUCTIONS, ms Zheng yuxi, Phd in computer & comminication sciences epfl, switzerland

multimodal llms as social media analysis engines prof luo jiebo albert arendt hopeman professor of engineering and computer science university of rochester

dr. liu qiang, postdoctoral fellow hexhive, epfl, switzerland (advised by prof. mathias payer), enforcing trust at runtime

Speaker: Professor LI Yu Assistant Professor, Department of Computer Science and Engineering The Chinese University of Hong Kong Title:Go Beyond Building Virtual Cell with Artificial Intelligence

Speaker:Mr. GU Yufeng, Ph.D. candidate (advised by Prof. Reetuparna Das), University of Michigan ; Title:Architecture and System Co-Design for Scalable Large Language Model Inference

Speaker: Professor Alastair DONALDSON, Professor & Director of Research, Department of Computing, Imperial College London, Title: Randomised testing and test case reduction for GPU compilers

Speaker: Dr. CHEN Guangyi, Postdoctoral Research Fellow, Carnegie Mellon University (CMU), Research Scientist, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Title: Causal Representation Learning

Speaker:Dr. CHEN Xiusi; Postdoctoral Research Associate, University of Illinois Urbana-Champaign ; Title: Towards Embodied Reasoning and Alignment of Large Language Models

Speaker: Dr. Johann KNECHTEL, Senior Research Scientist, New York University Abu Dhabi (NYUAD)Title: LLMs for Secure Hardware Design: Opportunities and Challenges

Dr arnaud huaulme research engineer medicis research group of umr 1099 ltsi, inserm research institute and univeristy of rennes (france)
ASSESSMENT OF THE TECHNICAL AND NON-TECHNICAL SKILLS OF THE SURGICAL TEAM

prof. krystel nyangoh timoh professor of gynecology university of rennes and prof. pierre janning INSERM Research Director Medical School, University of Rennes
SURGICAL DATA SCIENCE FOR WOMEN’S HEALTH

dr. qin tao, dr. liu haiguang, dr. yu chao, mr. guan haoxiang
PIONEERING AI FOR SCIENTIFIC DISCOVERY AND SOCIETAL IMPACT – ZHONGGUANCUN INNOVATIONS

Dr Sharib ALI Assistant Professor, School of Computer Science, University of Leeds, United Kingdom
Computer Vision for Endoscopic Image Analysis – From Diagnostics to Intraoperative Applications

Prof. Xu Hong, Department of Computer Science & Engineering, The Chinese University of Hong Kong
Hyper-Scale AI Infrastructure: Efficiency, Reliability, And Beyond

Mr. Alper Çakan PhD in Computer Science, Carnegie Mellon University
Public-Key Quantum Fire and Key-Fire From Classical Oracles

Prof. Fei Yunsi
Professor
Electrical and Computer Engineering Northeastern University
Side-Channel For AI Security: For or Friend

Prof. Miao Zhengjie
Assistant Professor
School of Computing Science Simon Fraser University
Test Database Generation For Text-To-SQL Evaluation and Beyond

Mr. CHEN Yanbo
PhD Candidate in Computer Science, University of Ottawa
Advised by Professor Andrej BOGDANOV
Recent Advances in Adaptively Secure Threshold Signatures

Dr. Rohit CHATTERJEE Research Fellow, School of Computer Science, National University of Singapore Are Uncloneable Proof and Advice States Strictly Necessary?

Professor Sethu Vijayakumar FRSE
Professor of Robotics, University of Edinburgh Programme Director, The Alan Turing Institute
From Automation to Autonomy: Machine Learning For Next-Generation Robotics

Professor Andrej Bogdanov
School of Electrical Engineering and Computer Science, University of Ottawa
Adaptive Bobustness of Hypergrid Johnson-Lindenstrauss

Professor Stephen T.C. Wong
Houston Methodist Hospital and Cornell University, USA
Spatial Computing For Biomedical Innovation: Transforming HealthCare Delivery And Drug Discovery

Professor Ansuman BANERJEE
Professor, Advanced Computing and Microelectronics Unit, Indian Statistical Institute, Kolkata
Harnessing Multiple BMC Engines Together For Efficient Formal Verification
 

Professor ZHAO Han
Assistant Professor, Department of Computer Science, University of Illinois Urbana-Champaign (UIUC)
Model Merging with Sparsity: Theory, Algorithms And Applications

Professor Jasper LEE,
Assistant Professor, Computer Science, University of California, Davis (UC Davis),
Towards a new toolbox of optimal statistical primitives

Professor MENG Qingjie
Assistant Professor, School of Computer Science, University of Birmingham
Smart Heart: AI-Powered Cardiac Shape Reconstruction, Motion Tracking And Data Generation

Dr. JIN Yaonan
Researcher, Huawei Theoretical Computer Science Lab
Tight Regret Bounds For Fixed-Price Bilateral Trade

Dr. XU Mengya
Postdoctoral Fellow, Multi-Scale Medical Robotics Center
From Recitation to Reasoning: Multimodal Large Language Models For Advanced Medical Intelligence

Mr. CHENG Chaoran
The Grainger College of Engineering, University of Illinois Urbana-Champaign
Unified Framework For Continuous-State Discrete Flow Matching

Professor ZENG Wei
Assistant Professor, The Hong Kong University of Science and Technology (Guangzhou)
Human-AI Interaction: From Passive Observation To Interactive Interpretation And Steering

Professor TIAN Yang
Associate Professor, Guangxi University
Designing Highly Accessible XR Interfaces

Mr. YOU Haoran
Ph.D. Candidate in Computer Science, Georgia Institute of Technology
Hardware-Aware Algorithms and Holistic Systems for Ubiquitous Artificial Intelligence Across Edge and Cloud

Dr. YUE Xiang
Postdoctoral Fellow, Carnegie Mellon University
Learning to Reason With LLMS

Professor HU Ruizhen
Distinguished Professor, College of Computer Science & Software Engineering, Shenzhen University
Modeling and Generating Interactions in 3D World

Mr. LI Chaojian
School of Computer Science, Georgia Institute of Technology
Enabling Ubiquitous 3D Intelligence via Multi-Granular Algorithm-Hardware Synergy

Professor Christoph TREUDE
Associate Professor, Singapore Management University
Generative AI and Empirical Software Engineering: A Paradigm Shift

Dr. LIU Shengchao
postdoc, University of California, Berkeley
Foundation Model for Scientific Discovery – With Applications in Chemistry, Material, and Biology

Professor Nicolas Padoy
Computer Science, University of Strasbourg
Harnessing the Power of Vision and Language to Improve Surgical Safety

Dr. LUO Jianlan
Postdoctoral Scholar, Department of Electrical Engineering and Computer Sciences, UC Berkeley
Intelligent Physical Agents: High-Performance Learning for Generalist Robots

Dr. ZHANG Zhuo
Postdoctoral Researcher, Purdue University
Advancing Security Red-Teaming through Probabilistic Binary Analysis

Professor Wayne LUK
Department of Computing, Imperial College London, United Kingdom
Verifiable Optimisation for Parametric Hardware Designs

Professor Nicholas AYACHE
Research Director, National Institute for Research in Digital Science and Technology (Inria), France
AI for Medical Imaging, Digital Twins & Medicine

Professor LIANG Zhiding, Assistant Professor
Department of Computer Science, Rensselaer Polytechnic Institute (RPI)
Co-Design of Quantum Software and Hardware: From Digital to Analog

Dr. Stefano V. Albrecht
Associate Professor in Artificial Intelligence School of Informatics, University of Edinburgh
From Deep Reinforcement Learning to LLM-based Agents: Perspectives on Current Research

Professor ZHANG Jialu, Assistant Professor
Department of Electrical and Computer Engineering, University of Waterloo, Canada
Automated Prevention, Detection, and Repair of High-Impact Program Errors

Professor Nikos MAMOULIS
Department of Computer Science and Engineering, University of Ioannina (UoI)
Handling Ranges in Main Memory

Mr. DING Youlong
Hebrew University of Jerusalem
Constructing Low-depth Pseudorandom Functions from LPN

Dr. BIAN Yatao
Research Scientist, Machine Learning Center, Tencent AI Lab
Learning Molecular Graphs under Label Scarcity and Distribution Shift

Dr. CHEN Li
Associate Researcher, Beijing Zhongguancun Laboratory
Building High-Performance Digital Twins of Large Model Training Systems

Professor CHEN Chen
Lecturer, Department of Computer Science, University of Sheffield
Unlocking the Value of Single Modality through Multi-Modal Knowledge Transfer for Healthcare

Professor Lydia Y. CHEN
University of Neuchatel, Switzerland
Watermarking Generative AI Models

Professor CAI Jing
Professor and Associate Dean, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University
Fellow, American Association of Physicists in Medicine (AAPM)
Advances and Challenges of AI and Radiomics in Precision Radiotherapy

Professor CHEN Ho
Chair Professor, The University of Hong Kong
AI-driven fuzzing across the software stack

Professor ZENG Wei
Assistant Professor, The Hong Kong University of Science and Technology (Guangzhou).
Enhancing Creative Control over GenAI for Design

Professor Giovanni DE MICHELI
Electrical Engineering and Computer Science, EPFL Lausanne, Switzerland
Digital System Design Automation

Professor DOU Qi
Assistant Professor, Department of Computer Science and Engineering,
The Chinese University of Hong Kong
A Step for AI CoPilot in Medical Diagnosis and Surgery

Mr. MU Changrui
Research Assistant (National University of Singapore)
Instance-Hiding Interactive Proofs

Professor FU Xiao
Oregon State University
Towards Provable Unaligned Multimodal Learning: A Model Identification Perspective

Professor CAI Leizhen
Professor Emeritus, Department of Computer Science and Engineering,
The Chinese University of Hong Kong
Harness Indirect Certificates to Design Algorithms

Mr. LIN Tao
PhD student in Computer Science,
Harvard University
How to Avoid Polarization in Recommender Systems with Dual Influence?

Professor LI Qingbiao
Assistant Professor, Faculty of Science and Technology,
University of Macau
Machine Learning for Embodied Artificial Intelligence: from Surgical Robotics to Multi-robot Coordination

Professor CHENG James
Associate Professor, Department of Computer Science and Engineering,
The Chinese University of Hong Kong
High-Performance Systems for Graph Analytics

Professor YU Bei
Associate Professor, Department of Computer Science and Engineering,
The Chinese University of Hong Kong
Machine Learning in EDA: When and How

Dr. GAN Junhao
Senior Lecturer, School of Computing and Information Systems, The University of Melbourne
Exact and Optimal Dynamic Parameterized Subset Sampling on Bounded Precision Machines

Dr. C. Mohan
Distinguished Professor of Science (Hong Kong Baptist University, China)
Distinguished Visiting Professor (Tsinghua University, China)
Retired IBM Fellow (IBM Research, USA)
Artificial Intelligence (AI): Past, Present and Future

Speaker:

Professor WEI Lan
Associate Professor
University of Waterloo

Abstract:
Given its high mobility, high breakdown voltage and decent thermal conductivity, GaN technologies have shown great promise for high-power high-frequency (HP-HF), rapidly rising as a front runner for mm-wave to THz analog/RF circuits for IoT and 5G/6G wireless communication. Meanwhile, it is also heavily explored for power electronic applications for fast charging, data center, and electric vehicles. As GaN technology continues to improve, challenges of high design cost and sub-optimal system performance emerge as bottlenecks preventing the technology from wide scale deployment. Accurate, scalable and efficient compact model is key to overcome such challenges.

This presentation will provide a brief overview of the family of MVSG GaN compact model, including models for GaN HEMT, GaN multi-channel diodes and GaN transmission-line resistors.  The model formulation and various features will be introduced. Application examples will also be demonstrated, showing the potentials of this group of physics-based compact models.

Biography:

Prof. Lan Wei received her B.S. in Microelectronics from Peking University, China (2001), M.S and Ph. D. in Electrical Engineering from Stanford University, USA (2007 and 2010, respectively). She is currently an Associate Professor at the University of Waterloo, Canada. She has intensive experience in device physics-based compact modeling including silicon and GaN technologies, device-circuit interactive design and optimization, integrated nanoelectronic systems with low-dimensional materials, cryogenic CMOS device modeling and circuit design for quantum computing.  She has authored/co-authored more than 90 peered reviewed publications and served on the technical program committees including IEDM, ICCAD, DATE, ISQED, BCICTS, etc.

Enquiries:

Professor YU Bei (byu@cse.cuhk.edu.hk)

Mr. WONG O Bong (obong@cse.cuhk.edu.hk)

** ALL ARE WELCOME **

Speaker:

Professor Emir Demirovic

Assistant professor

TU Delft (Netherlands)

Abstract:
Decision trees are an effective and concise way of conveying information, easily understood by virtually everyone regardless of the topic. Given the recent interest in explainable AI and related fields, decision trees stand out as a popular choice. From the algorithmic side, the unique structure of decision trees is interesting since it may be exploited to obtain much more efficient algorithms than structure-oblivious approaches.

In this talk, I will give an overview of the research we have been doing on leveraging the decision tree structure from three disjoint angles: 1) machine learning with constraints, where the goal is construct the optimal regression/decision tree representing tabular data whilst potentially respecting different types of constraints such as fairness, 2) controller/policy verification, where the aim is to verify that a decision tree controller satisfies desired properties in continuous time, and 3) explaining the unsatisfiability of a combinatorial optimisation problem on a graph, by representing proofs of unsatisfiability as a tree using graph-specific concepts. We show that for each of these problems, exploiting the decision tree structure is important in obtain orders of magnitude runtime improvements and/or interpretability.

The talk summarises about half a dozen of our papers (AAAI’21/24, JMLR’22, NeurIPS’22/23, ICML’23/24) and is meant to be accessible to all backgrounds, with plenty of time for discussion!

Biography:

Emir Demirovic is an assistant professor at TU Delft (Netherlands). He leads the Constraint Solving (“ConSol”) research group, which advances combinatorial optimisation algorithms for a wide range of (real-world) problems, and co-directs the explainable AI in transportation lab (“XAIT”) as part of the Delft AI Labs. Prior to his appointment at TU Delft, Emir worked at the University of Melbourne, Vienna University of Technology, National Institute of Informatics (Tokyo), and at a production planning and scheduling company.

The focus point of Emir’s current work is solving techniques based on constraint programming, optimising decision trees, and explainable methods for combinatorial optimisation. He is also interested in industrial applications, robust/resilient optimisation, and the integration of optimisation and machine learning. He publishes in leading AI conferences (e.g., AAAI, NeurIPS) and specialised venues (e.g., CP, CPAIOR), attends scientific events such as Dagstuhl seminars, Lorentz workshops, and the Simons-Berkeley programme, and frequently organises incoming and outgoing visits, e.g., EPFL, ANITI/CNRS, CUHK, Monash University, TU Wien.

Enquiries:

Professor LEE Ho Man Jimmy (jlee@cse.cuhk.edu.hk)

Mr. WONG O Bong (obong@cse.cuhk.edu.hk)

** ALL ARE WELCOME **

Speaker:

Professor Frank Liu
Professor and Inaugural Director, School of Data Science
Old Dominion University

Abstract:

Old Dominion University is a large public university located in the southwest coast of Virginia in the US. First established as a branch of College of William and Mary, its root can be traced to the 17th century England. School of Data Science is a newly established academic unit in Old Dominion University to encourage interdisciplinary research and education across the campus, as well as the region. I will give a brief introduction to the data science program, followed by open floor for Q&A and discussions.

Biography:

Frank Liu is a Professor of Computer Science and ECE at Old Dominion University. He is the founding director of the School of Data Science, with research experience spans academia, national laboratories, and corporate research labs. He is a Fellow of IEEE.

Enquiries:

Professor YOUNG Fung Yu (fyyoung@cse.cuhk.edu.hk)

Mr. WONG O Bong (obong@cse.cuhk.edu.hk)

** ALL ARE WELCOME **

Speaker:

Professor HSIEH Chang-Yu

Professor, College of Pharmaceutical Sciences

Zhejiang University

Abstract:
In recent years, generative AI has gained significant traction as a tool for designing novel molecules for therapeutic purposes. Advanced deep learning techniques have been increasingly adapted for drug design, yielding varying levels of success. In this seminar, I will provide an overview of this emerging field, highlighting the key challenges in applying generative AI to drug design and presenting our proposed solutions. Specifically, we combine principles from physics and chemistry with deep learning methods to discover more realistic drug candidates within the vast chemical space. Our results are supported by benchmark studies and validated through experimental wet lab testing.

Biography:

Dr. Chang-Yu (Kim) Hsieh is the QiuShi Engineering Professor at the College of Pharmaceutical Sciences, Zhejiang University. Before joining Zhejiang University, he led the Theory Division at Tencent Quantum Lab in Shenzhen, focusing on AI and quantum simulation for drug and material discovery. Prior to that, he was a postdoctoral researcher in the Department of Chemistry at MIT. His primary research interests lie in leveraging advanced computing technologies, including AI and quantum computing, to simulate and model material and molecular properties.

Enquiries:

Professor HENG Pheng Ann (pheng@cse.cuhk.edu.hk)

Ms. NG Man Nga Vivien (vivien@cse.cuhk.edu.hk)

Mr. WONG O Bong (obong@cse.cuhk.edu.hk)

** ALL ARE WELCOME **

Speaker:

Professor ZHANG Qirun
Assistant Professor, School of Computer Science
Georgia Institute of Technology

Abstract:
SMT formulas are first-order formulas extended with various theories. SMT solvers are fundamental tools for many program analysis and software engineering problems. The effectiveness and scalability of SMT solvers influence the performance of the underlying client analyzers. The most popular approach to improving SMT solving is by developing new constraint-solving algorithms. In this talk, we will discuss a new perspective on improving SMT solving via compiler optimization. Our basic idea involves translating SMT formulas to LLVM IR and leveraging LLVM optimization passes to simplify the IR. Then, we translate the simplified IR back to SMT formulas. In addition, this strategy can be extended to enhance the solving of unbounded SMT theories by utilizing their bounded counterparts.

Biography:

Qirun Zhang is an Assistant Professor in the School of Computer Science at Georgia Tech. His general research areas are programming languages and software engineering, focusing on developing new program analysis frameworks to improve software reliability. He received a PLDI 2020 Distinguished Paper Award, an FSE 2023 Distinguished Paper Award, an NSF CAREER Award, and an Amazon Research Award in Automated Reasoning.  He served on the program committees of FSE, ICSE, ISSTA, OOPSLA, PLDI, and POPL.

Enquiries:

Professor LYU Rung Tsong Michael (lyu@cse.cuhk.edu.hk)

Ms. NG Man Nga Vivien (vivien@cse.cuhk.edu.hk)

Mr. WONG O Bong (obong@cse.cuhk.edu.hk)

** ALL ARE WELCOME **

Speaker:

Professor ZHANG Yizhe

Associate Professor, School of Computer Science and Engineering,

Nanjing University of Science and Technology

Abstract:
With advancements in deep learning and AI techniques, medical image segmentation has experienced rapid development over the past decade. Modern DL-based models, utilizing large labeled datasets, often produce impressive benchmark results. However, practical issues, such as reliability and trustworthiness, persist when these models are implemented in hospitals and medical facilities.

This talk addresses two related aspects of medical image segmentation for improving model deployment: model evaluation and test-time methods. First, we will discuss our recent work on deployment-centric model evaluation, evaluation of foundation models and related techniques. Next, we will cover a series of test-time methods that we have developed to improve video segmentation consistency, enhance the quality of medical image segmentation, and more recently, advance segmenting anything in medical images.

Finally, we will briefly highlight several other projects from my group and discuss directions in medical image segmentation research that we find promising and important.

Biography:

Yizhe Zhang, Ph.D., is an associate professor at Nanjing University of Science and Technology. He received his Ph.D. from the University of Notre Dame in the United States. Before returning to Nanjing, he was a senior research engineer at Qualcomm AI Research, San Diego, where he worked on efficient video segmentation and the spatiotemporal consistency of segmentation. He has conducted research on topics such as active learning, semi-supervised learning, model design, training and evaluation in medical image segmentation. As the first author, he has published papers in conferences and journals including MICCAI, Medical Image Analysis, IEEE TMI, BIBM, ICCV, AAAI, and WACV. As a key contributor, he was involved in biomedical image modeling and analysis work that won the 2017 Cozzarelli Prize awarded by the National Academy of Sciences.

Enquiries:

Professor HENG Pheng Ann (pheng@cse.cuhk.edu.hk)

Ms. NG Man Nga Vivien (vivien@cse.cuhk.edu.hk)

Mr. WONG O Bong (obong@cse.cuhk.edu.hk)

** ALL ARE WELCOME **