Title: Statistics-Powered AI

Time: 10:30am 

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

Speaker(s): Prof. Chengchun Shi

Remark(s): 

Abstract

We have definitely entered an era of generative artificial intelligence (AI), where large language models (LLMs) are increasingly reshaping our daily lives. Their impact is everywhere -- from education and academia to professional work and everyday life. In this talk, I will present two recent NeurIPS papers on statistics-powered AI, focusing on how statistical methodologies can enhance AI's performance in (1) aligning LLM's model outputs with human feedback, and (2) detecting LLM-generated content with rigorous guarantees. Open-source Python implementations are available at https://github.com/Mamba413/AdaDetectGPT and https://github.com/DRPO4LLM/DRPO4LLM.

Title: Task-Driven Image Restoration: Why, How, and What?

Time: 03:00pm 

Venue: HW312, Haking Wong Building

Speaker(s): Prof. Kyoung Mu Lee

Remark(s): 

Abstract

Image degradation is common in real-world scenarios due to factors such as transmission loss, limited camera capability, or poor shooting conditions. These issues often remove key high-frequency details, causing major performance drops in high-level vision tasks like classification, segmentation, and detection. Image restoration (IR) seeks to recover lost details in low-quality images using learned natural image priors, offering a potential solution to this problem. However, studies show that simply applying IR as a preprocessing step rarely restores the information most relevant to high-level tasks. This insight has led to Task-driven Image Restoration (TDIR), which focuses on enhancing visual quality in ways that directly benefit downstream vision tasks. In this talk, we will discuss the key challenges in TDIR and highlight several recent, efficient approaches to address them.

About the speaker

Kyoung Mu Lee (Fellow, IEEE) is currently the Editor-in-Chief (EiC) of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI); He is a distinguished professor at Seoul National University (SNU). He was the founding director of the Interdisciplinary Graduate Program in SNU. He is an Advisory Board Member of the Computer Vision Foundation (CVF). He was a Distinguished Lecturer of the Asia-Pacific Signal and Information Processing Association (APSIPA), from 2012 to 2013. He has received several awards, in particular, the Medal of Merit and the Scientist of Engineers of the Month Award from the Korean Government, in 2018 and 2020, respectively; the Most Influential Paper Over the Decade Award by the IAPR Machine Vision Application, in 2009; the ACCV Honorable Mention Award, in 2007; the Okawa Foundation Research Grant Award, in 2006, and the SNU Excellence in Research Award in 2020. He has also served as a General Chair for ICCV2019, ACMMM2018, and ACCV2018; and an Area Chair for CVPR, ICCV, and ECCV many times. He is the founding member and served as the President of the Korean Computer Vision Society (KCVS). Prof. Lee is a Fellow of IEEE, a member of the Korean Academy of Science and Technology (KAST) and the National Academy of Engineering of Korea (NAEK).

Title: Radio Sensing for Human Sensing

Time: 10:30am 

Venue: CB 308

Speaker(s): Prof. Stephan Sigg

Remark(s): 

Abstract

Radio sensing has seen significant advances over the recent decade. I will provide an overview of recent advances, open challenges and new research opportunities. Particularly, as the technology is increasingly integrated with communication platforms, a ubiquitous, connected sensing system is possible. But will such a system indeed be anticipated, given its privacy, ethical and still also technical challenges? On the other hand, new advances towards Quantum RF sensing have been proposed as well as the integration into medical devices and the perception of sentiment, while holographic sensing techniques promise unprecedented imaging capabilities.

About the speaker

Stephan Sigg is a Professor at Aalto University in the Department of Information and Communications Engineering. With a background in the design, analysis and optimisation of algorithms for distributed and ubiquitous systems, he focuses on sensing systems for environmental perception and Usable (perception-based) Security. Especially, his work covers proactive computing, distributed adaptive beamforming, context-based secure key generation and device-free passive activity recognition. Stephan is an editor for the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). He has served on the organizing and technical committees numerous prestigious conferences including IEEE PerCom, ACM Ubicomp, IEEE ICDCS. 

Title: Manifold Fitting

Time: 10:30am 

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

Speaker(s): Prof. Zhigang Yao

Remark(s): 

Title: Exploring Synergies in Wireless Systems and Soft Robots

Time: 11:30am 

Venue: CB 308

Speaker(s): Mr. Yiwen Song

Remark(s): 

Abstract

We present our explorations in how advances in wireless systems can offer new opportunities for soft robots, and vice versa. While soft robots grant applications in narrow fields or under intensive human interaction, the robots are powered by on-board batteries, tethered wires, magnetic fields, or lasers, which restrain their mobility in non-line-of-sight, mobile applications. Our work first brings frequency-selective actuation of liquid crystalline elastomer-based soft robots with radio frequency power, through a combined design of soft materials and wireless frontend. We also explore how soft robots can bring capabilities to wireless communication and power delivery by demonstrating waveguide for tumor-treating-field delivery and soft actuatable antennas that automatically reconfigures operating frequencies for software-defined radios. The talk also covers our wireless infrastructure: sensing and programming wireless environment. We show compressed sensing for probing wireless environments and controlling wireless heating inside a microwave oven with frequency-selective surfaces.

About the speaker

Yiwen Song is a fifth-year Ph.D. student at the Department of Electrical and Computer Engineering, Carnegie Mellon University. His advisor is Swarun Kumar. His research spans broadly in wireless systems, including communication, sensing, and powering, and he is specifically interested in the intersection between wireless systems, soft materials, and robotics. He has published more than 10 papers in top-tier venues including Nature Communications, MobiCom, MobiHoc, ICRA, etc.

Title: Measuring and Mitigating Adversarial Intermediaries on the Global Internet

Time: 10:30am 

Venue: CB 308

Speaker(s): Dr. Diwen Xue

Remark(s): 

Abstract

Over the past decade, significant shifts in the threat landscape have positioned network infrastructure itself as a potential adversary. Rapid advances and commoditization of networking technologies such as Deep Packet Inspection (DPI), combined with loosened regulations like the repeal of net neutrality, have granted the network with unprecedented capability and freedom to inspect, modify, throttle, or even hijacks the traffic it transports at fine granularity and line rate. What were once neutral “dumb pipes” have evolved into capable and sometimes adversarial network intermediaries—ranging from malicious middleboxes and rogue ISPs to compromised routers and untrusted transit networks—all creating new threats that increasingly erode user privacy, autonomy, and overall trust in connectivity.

My research seeks to address this shifting threat landscape by building the next generation of secure and private network ecosystems. This vision is grounded in two complementary efforts: (1) empirically modeling how adversarial intermediaries behave today and how they might evolve in the future, and (2) developing principled countermeasures that are sound in theory and deployable at scale. In this talk, I will present a series of measurements and security protocol designs that illustrate this dual approach, with the goal of safeguarding users’ communication on this increasingly adversarial Internet.

About the speaker

Diwen Xue is currently a Research Fellow at the University of Michigan. His research focuses on areas where the privacy, security and availability implications of networked systems affect users in the real world. He conducts Internet measurements at scale, uses those observations to refine threat models, and builds countermeasures to safeguard users’ communication on this increasingly adversarial Internet. Diwen's work has been recognized with several honors, including the Internet Defense Prize, USENIX Security Distinguished Paper, and first place in the CSAW Applied Research Competition. Previously, he completed his Ph.D. at the University of Michigan and his B.A. at New York University.

Title: Providing Factual Information with Dual Neural Knowledge

Time: 11:00am 

Venue: HW312, Haking Wong Building

Speaker(s): Dr. Xin Luna Dong

Remark(s): 

Abstract

For decades, multiple research communities—including Databases, Information Retrieval, Natural Language Processing, Data Mining, and AI—have pursued the mission of delivering the right information at the right time. These efforts span web search, data integration, knowledge graphs, and question answering. Recent advancements in Large Language Models (LLMs) have brought remarkable progress in language understanding and generation, reshaping approaches across all these fronts. Yet, limitations such as factual inaccuracies and hallucinations restrict their suitability for building knowledgeable and trustworthy assistants.

About the speaker

Xin Luna Dong is a Principal Scientist at Meta Wearables AI, where she leads the Agentic AI efforts for building trustworthy and personalized assistants on wearable devices. Previously, she spent over a decade advancing knowledge graph technology, including the Amazon Product Graph and the Google Knowledge Graph. She is co-author of Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases and Big Data Integration. She is an ACM Fellow and IEEE Fellow, recognized for “significant contributions to knowledge graph construction and data integration.” She was named an ACM Fellow and an IEEE Fellow for "significant contributions to knowledge graph construction and data integration", awarded the VLDB Women in Database Research Award and VLDB Early Career Research Contribution Award, and invited as an ACM Distinguished Speaker. She serves in the PVLDB advisory committee, was a member of the VLDB endowment, a PC co-chair for KDD’2022 ADS track, WSDM’2022, VLDB’2021, and Sigmod’2018.

Title: Dynamic Spectral Clustering with Provable Approximation Guarantee

Time: 10:30am 

Venue: CB308

Speaker(s): Prof. He Sun

Remark(s): 

Abstract

Spectral clustering is one of the most fundamental clustering algorithms in machine learning and has comprehensive applications in many fields of computer science. In this talk I will introduce the basics of spectral clustering, starting with its roots in spectral graph theory and its connection to eigenvalues and eigenvectors of graph Laplacians. I will present a spectral clustering algorithm in dynamic settings and discuss techniques for analyzing its performance. Several open problems will be discussed at the end of the talk. This is based on joint work with Steinar Laenen from Google Zurich, and the work appeared at ICML 2024.

About the speaker

He Sun is a Professor, and Director of Center for Algorithms and Learning Theory, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. He received his PhD from Fudan University in 2010 and worked at the Max Planck Institute for Informatics (2010 - 2015), UC Berkeley (2014, 2023), University of Bristol (2015 - 2017), and University of Edinburgh (2017 – 2025). His research areas include algorithms, machine learning, spectral graph theory, and applied probability. He has written over 60 papers and has solved several long-standing open problems in algorithms. He received the President Medal of Fudan University (2004), Shanghai Outstanding PhD Thesis Award (2010), Simons-Berkeley Research Fellowship (2014), Turing Fellowship (2018), and EPSRC Fellowship (2020). He is a recipient of the Chinese High-Level Talent Recruitment Program for Overseas Experts (2024). He has received research grants of more than 40 million CNY, and has served as an area chair and PC member of several leading conferences in ML and TCS, including ICML 2025 and STOC 2026.

Title: HKU AI & Data Science Workshop
Date : 23 October 2025 (Thu)
Time: 2:30pm - 4:30pm
Venue: CPD 1.22

Time: 02:30pm 

Venue: CPD 1.22

Speaker(s): Prof. Xiangnan HE, Prof. Wenya WANG, Prof. Giulio CHIRIBELLA, Prof. Chao HUANG

Remark(s): 

Title: GPU Trusted Execution Environment

Time: 10:30am 

Venue: CB308

Speaker(s): Prof. Fengwei Zhang

Remark(s): 

Abstract

Trusted Execution Environments (TEEs) have been widely used for protecting endpoints and clouds for the past two decades. However, it primarily focuses on CPU processors and has not carefully considered other processors, such as GPUs. Worse, due to the vulnerable GPU software and non-confidential GPU hardware designs, attacking the GPU is not challenging and can cause severe data leakage. To address this problem, the industry/academy design GPU TEEs. We introduce two GPU TEEs: StrongBox, a GPU TEE designed for Arm endpoints such as smartphones, and CAGE, a GPU TEE tailored for Arm's latest Confidential Computing Architecture. Besides building GPU TEEs, we also discovered a GPU TEE vulnerability (MOLE) on a GPU-embedded Microcontroller Unit (MCU), which enables an attacker to leak sensitive data within the GPU TEE.

About the speaker

Dr. Fengwei Zhang is the Director of the COMPASS (COMPuter And Systems Security) Lab and a tenured Associate Professor at the Department of Computer Science and Engineering at Southern University of Science and Technology, China. Before that, he joined Wayne State University as an assistant professor at the department of computer science from 2015 to 2019. His primary research interests are in the areas of systems security, including trusted execution environments (e.g., Arm TrustZone/CCA), GPU confidential computing, debugging transparency, system introspection, and hardware- assisted security. He has published over 100 conference/journal papers, including IEEE S&P, USENIX Security, ACM CCS, NDSS, IEEE TIFS, and IEEE TDSC. He is a recipient of the Distinguished Paper Award in ACSAC 2017 and the Runner-up Best Paper Award in IEEE/IFIP DSN 2020. His high-quality work received 3 NSF Awards in the USA. He is currently the Principal Investigator of the projects from NSFC and industries. He is a senior member of ACM, a senior member of IEEE, and a distinguished member of CCF.