Title: Subsystem Quantum Error Correction for Noisy Quantum Metrology

Time: 10:30am 

Venue: CB308, 3/F, Chow Yei Ching Building, HKU

Speaker(s): Dr. Qiushi Liu

Remark(s): 

Abstract

Quantum error correction has been successfully applied to enhance the precision of parameter estimation in the presence of noise. Nonetheless, existing methods require a number of noiseless, controllable ancillae and lack efficient encoding and decoding procedures. In this Letter, we demonstrate that subsystem error correction provides a new direction that can substantially simplify the metrological protocol. We derive general conditions under which subsystem stabilizer codes achieve the Heisenberg limit and show that, for broad classes of noise, this can be realized by syndrome-free protocols using at most a single ancilla qubit. Furthermore, we extend this framework to dynamical error correction and show that Floquet codes can protect time-dependent metrological signals in reaching the Heisenberg limit.

About the speaker

Qiushi Liu is a postdoctoral researcher at Perimeter Institute for Theoretical Physics. He earned his PhD in computer science from the University of Hong Kong, supervised by Prof. Yuxiang Yang and Prof. Giulio Chiribella. Prior to that, he obtained a master in physics from ETH Zurich, and bachelor in physics from Peking University. His research interests include quantum metrology, quantum error correction and quantum foundations.

Title: Finite-Sample Likelihood Ratios for Logistic Regression

Time: 03:00pm 

Venue: CB328, 3/F, Chow Yei Ching Building, HKU

Speaker(s): Prof. Nikita Zhivotovskiy

Remark(s): 

Abstract

Likelihood ratio methods are a central tool in statistical inference, but their classical justification is largely asymptotic and local. In regular parametric models, Wilks’ theorem predicts a universal chi-square behavior, suggesting that likelihood ratio confidence sets should behave as if they had a simple dimension-dependent number of degrees of freedom. I will discuss a nonasymptotic theory for the likelihood ratio in logistic regression. The main result shows that, under arbitrary fixed designs, the worst-case finite-sample behavior can be larger than the classical Wilks prediction by a logarithmic factor, and that this loss is unavoidable. The bound holds uniformly over all design matrices and all true parameters, and does not require the maximum likelihood estimator to exist.

About the speaker

Prof. Nikita Zhivotovskiy is an Assistant Professor in the Department of Statistics at the University of California, Berkeley. His research interests lie at the intersection of mathematical statistics, probability, and learning theory. His work focuses on understanding what can be learned from data under minimal assumptions, with an emphasis on finite-sample, non-asymptotic, and distribution-free guarantees for statistical and machine learning problems.

Title: Modeling, Understanding, and Interacting with the 3D World

Time: 02:30pm 

Venue: CB328, 3/F, Chow Yei Ching Building, HKU

Speaker(s): Prof. Mengyu Wang

Remark(s): 

Abstract

The rapid rise of large language models has brought AI into people’s daily lives and is reshaping many aspects of society. It is increasingly recognized that AI’s success in the digital domain must be extended to the real 3D world, ultimately enabling robotic AI systems to live and work in physical environments. Achieving this goal requires models that can effectively model, understand, and interact with the 3D world. In this talk, I will present our recent research spanning 3D object generation, dynamic scene understanding, geometric and spatial reasoning, world models, and active vision systems. In particular, I will introduce Stream3D, a scalable framework for streaming and consistent 3D generation from sparse observations; PAGE-4D, a dynamic-aware 4D reconstruction model that jointly estimates geometry and camera motion in dynamic scenes; GeoWorld, a geometry-grounded world modeling framework that improves spatial reasoning and physical consistency in vision-language models; GEM, a geometry-enhanced world model that aligns generative dynamics with structured geometric representations for robotic manipulation; and an active vision system that enables robots to actively perceive the world, improve scene understanding, and increase manipulation success through closed-loop interaction. Together, these works highlight a pathway toward robotic AI systems that can robustly perceive, predict, and act in the real world.

About the speaker

Prof. Mengyu Wang is an Associate Professor with appointments at Harvard Medical School, Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, Harvard Data Science Initiative, and Broad Institute of MIT and Harvard. Prof. Mengyu Wang has interests spanning generative AI for computer vision, multimodal large language model behaviors and agents, AI for robotics, AI for genomics, and various other AI applications in medicine.

Title: Causal Generalist Medical AI

Time: 11:00am 

Venue: HW312, Haking Wong Building or Lecture theater 1A, G/F, CDS-1 Building, HKU-CDS Shanghai Teaching and Research Site

Speaker(s): Dr. Hongtu Zhu

Remark(s): 

Abstract

"The rapid evolution of flexible, reusable foundation models is transforming medical science. This lecture introduces Causal Generalist Medical AI (Causal GMAI)—a paradigm that integrates causal inference into generalist AI architectures to enhance
interpretability, robustness, and generalizability in clinical decision-making. Causal GMAI leverages advanced self-supervised, semi-supervised, and supervised learning across highly diverse, multimodal datasets, including medical imaging, electronic health
records (EHR), clinical trials, genomics, knowledge graphs, and clinical narratives, to perform complex downstream tasks with minimal task-specific supervision.

By embedding structural causal reasoning, these models move beyond traditional correlation-based prediction to infer underlying disease mechanisms and counterfactual outcomes, thereby advancing diagnostic precision and personalized medicine. This
lecture will outline the mathematical and technical foundations of Causal GMAI—specifically focusing on causal discovery, counterfactual reasoning, and domain adaptation under covariate shift—alongside its real-world clinical applications. Finally, the lecture will address critical open challenges in regulatory compliance, statistical validation, and multi-center dataset curation required to ensure clinical reliability. Ultimately, this presentation provides a foundational framework for statisticians, data scientists, and AI practitioners to advance the next generation of trustworthy and interpretable medical AI."

About the speaker

"Dr. Hongtu Zhu is the Kenan Distinguished Professor of Biostatistics, Statistics, Radiology, Computer Science and Genetics at the University of North Carolina at Chapel Hill. He is the Fellow of ASA, IMS, AIMBE, and IEEE. He was a DiDi Fellow and Chief Scientist of Statistics at DiDi Chuxing between 2018 and 2020 and held the Endowed Bao-Shan Jing Professorship in Diagnostic Imaging at MD Anderson Cancer Center between 2016 and 2018. He is an internationally recognized expert in statistical
learning, medical image analysis, precision medicine, biostatistics, artificial intelligence, and big data analytics. He received an established investigator award from the Cancer Prevention Research Institute of Texas in 2016, the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice in 2019, the ICSA 2025 Distinguished Achievement Award, the IMS
2027 Medallion award and Lecture, and the COPSS 2025 Snedecor Award. He has published more than 359 papers in top journals, including Nature, Science, Cell, Nature Genetics, Nature Communication, PNAS, AOS, JASA, Biometrika,
and JRSSB, as well as presenting 71+ conference papers at top conferences, including meetings for Neurips, ICLR, ICML, AAAI, IPMI, MICCAI, and KDD. He is the coordinating editor of JASA and the editor of JASA ACS."

Title: 3D Visual Character Motion Generation, Reconstruction, and Embodied Agents

Time: 10:00am 

Venue: CB328, 3/F, Chow Yei Ching Building, HKU

Speaker(s): Prof. Li Cheng

Remark(s): 

Abstract

"Recent advancements in sensing and deep learning have unlocked exciting possibilities for the visual analysis of human and animal motions in the physical 3D space. These innovations hold great potential for applications across diverse domains, including for example natural user interfaces, AR/VR, robotics, and gaming. In this talk, I will present the latest research progress in this rapidly evolving field including especially 3D human motion generation, pose tracking and shape reconstruction, and related tasks - highlighting key developments from the past few years as well as contributions from our own work."

About the speaker

Li CHENG is a full professor with the Department of Electrical and Computer Engineering, University of Alberta. He is currently an associate editor of IEEE Transactions on Image Processing. Prior to joining University of Alberta, he worked at A*STAR, Singapore, TTI-Chicago, USA, and NICTA, Australia. His current research interests include computer vision, multimedia data analytics, and applications. He has over 200 papers in peer-reviewed journals and conferences. His papers have been nominated for Best Paper Award at CVPR 2021. More recent details can be found at his lab website

Title: A General Framework For Multiple Testing Via E-Value Aggregation And Data-Dependent Weighting

Time: 02:00pm 

Venue: Room 301, Run Run Shaw Building

Speaker(s): Dr. Guanxun Li

Remark(s): 

Abstract

"Motivated by recent findings in Li and Zhang (2025), which establish an equivalence between certain $p$-value–based multiple testing procedures and the e–Benjamini–Hochberg procedure, we develop a general framework for constructing new multiple testing methods via aggregation and combination of e-values. A direct aggregation or combination can yield negligible power in
practice; therefore, we introduce data-dependent weighting for e-value aggregation and combination, which significantly improves the power of the resulting e–Benjamini–Hochberg procedures. Designing these weights is nontrivial and is inspired by leave-one-out analyses, a technique widely used to prove false discovery rate control in $p$-value–based methods. We theoretically show that the proposed e–Benjamini–Hochberg procedure, when equipped with data-dependent weights, achieve finite-sample FDR control. Building on these weights, we propose new procedures for three distinct scenarios: (i) assembling e-values obtained from
different data subsets, with simultaneous control of group-wise and overall FDRs; (ii) aggregating e-values produced by different procedures; and (iii) adaptive multiple testing methods that incorporate external structural information to increase power. Numerical studies demonstrate the effectiveness and advantages of the proposed methods in each application scenario."

About the speaker

Dr. Guanxun Li is an Assistant Professor in the Department of Statistics at Beijing Normal University, Zhuhai Campus. He earned his Ph.D. in Statistics from Texas A&M University in 2022. His research focuses on multiple testing, watermarking in large language models, Bayesian computation, and biostatistics.

Title: Non-Asymptotic Bounds for Forward Processes in Denoising Diffusions: Ornstein-Uhlenbeck is Hard to Beat

Time: 11:00am 

Venue: HW312, Haking Wong Building, HKU

Speaker(s): Prof. Aleksandar Mijatović

Remark(s): 

Abstract

"Denoising diffusion probabilistic models (DDPMs) represent a recent advance in generative modelling that has delivered state-of-the-art results across many application domains. Despite their success, a rigorous theoretical understanding of the error within DDPMs, particularly the non-asymptotic bounds required for the comparison of their efficiency, remain scarce. Making minimal assumptions on the initial data distribution, allowing, for example, the manifold hypothesis, this talk presents explicit non-asymptotic bounds on the forward diffusion error in total variation (TV), expressed as a function of the terminal time T.

The talk parametrises multi-modal data distributions in terms of the distance R to their furthest modes and consider forward diffusions with additive and multiplicative noise. The analysis rigorously proves that, under mild assumptions, the canonical choice of the Ornstein–Uhlenbeck (OU) process cannot be significantly improved in terms of reducing the terminal time T as a function of R and error tolerance. Motivated by data distributions arising in generative modelling, the talk also establishes a cut-off like phenomenon (as R →∞) for the convergence to its invariant measure in TV of an OU process, initialized at a multi-modal distribution with maximal mode distance R.

Joint work with M. Bresar."

About the speaker

Prof. Aleksandar Mijatović is a Professor of Probability at the Department of Statistics at the University of Warwick and a Fellow of The Alan Turing Institute in London. Prof. Mijatović was previously a Chair in Probability at the Department of Mathematics of King’s College London, and before that a Reader in Probability at the Mathematics Department of Imperial College London. Prof. Mijatović obtained his Ph.D. in low-dimensional topology at the University of Cambridge, before working in the City of London as a front-office quantitative analyst in Foreign Exchange derivative markets. His research interests are in Probability and its applications, including Stability of Stochastic Systems, Simulation and Monte Carlo Methods, Mathematical Finance, Numerical Stochastics, Data Science & Foundations of Machine Learning. He is also interested in the interactions of Probability with Analysis and Geometry.

Title: Towards Dependable Systems for Privacy-Enhancing Technologies

Time: 10:00am 

Venue: CB308, 3/F, Chow Yei Ching Building, HKU

Speaker(s): Dr. Dongwei Xiao

Remark(s): 

Abstract

"Privacy-Enhancing Technologies (PETs) are foundational for a future where data can be used without compromising privacy. While the community has largely focused on advancing the cryptographic foundations of PETs, real-world security of PETs is threatened by the very software systems designed to make them accessible, including PET-oriented compilers and frameworks.

The goal of my research is to ensure that the practical systems supporting PETs are dependable. In this talk, I will present my work on developing novel, automated techniques to systematically uncover critical vulnerabilities in the software systems of PETs. I will show two thrusts of my research: (1) automatically discovering severe logic bugs in domain-specific compilers for PETs, and (2) identifying and mitigating new, subtle security risks in PET-enhanced machine learning frameworks. The tools from this research have uncovered dozens of bugs (some with high security impact) in high-stakes PET systems and have been adopted by leading PET industry users. I will conclude by discussing my future research vision towards building provably dependable PET ecosystems."

About the speaker

Dongwei Xiao is currently a Postdoctoral Fellow at the Hong Kong University of Science and Technology, working with Prof. Shuai Wang. He earned his PhD degree from the same institution. During his PhD study, he conducted research as a visiting student at ETH Zürich with Prof. Zhendong Su. He has published papers at venues like NDSS, PLDI, and ICSE, and received an ACM SIGSOFT Distinguished Paper Award in 2023. He will join the University of Birmingham as an Assistant Professor in the Fall of 2026.

Title: Optimal Time To Sell A Stock In The Presence Of Gap, Default And Volatility Risks

Time: 03:00pm 

Venue: Room 301, Run Run Shaw Building

Speaker(s): Prof. Aleksandar Mijatovic

Remark(s): 

Abstract

Consider a small investor who holds a stock that is subject to default risk and seeks to identify the optimal time to sell the asset in the sense of minimizing the prophet's drawdown, which is the ratio of the ultimate maximum of the stock price at the time of default and the value of the stock price at the moment of sale. Assuming that default occurs at a constant rate and that at the moment of default there is a random recovery value, we solve this stochastic optimisation problem explicitly in the case the log-price of the stock prior to default is modelled by a general spectrally negative Levy process. Our results reveal a decomposition of the critical drift levels of the log-stock (at which the optimal strategy changes) into gap-risk, default-risk and volatility-risk components. Moreover, we provide an algorithm for the computation of the optimal exercise policy in terms of the Levy measure, volatility and drift parameters of the Levy process and apply this algorithm to a number of widely used models in the literature.

About the speaker

Title: Semiparametric Distribution Learning Via Quantile Regression

Time: 04:00pm 

Venue: Room 301, Run Run Shaw Building

Speaker(s): Prof. Huixia Judy Wang

Remark(s): 

Abstract

"Modern data analysis increasingly requires learning not only average trends, but also heterogeneity, uncertainty, tail behavior, and how information can be fused across heterogeneous data sources. In this talk, I will discuss how the quantile regression process
provides a flexible semiparametric approach to these problems by learning conditional distributions without imposing strong parametric assumptions on their shape.

I will highlight its role in several modern statistical problems, including multiple imputation, Bayesian inference, extreme quantile analysis, and conformal prediction, where quantile processes can help construct density-based nonconformity scores and prediction regions under complex error distributions. I will also discuss rank-based data integration motivated by the fusion
of multiple epigenetic clocks for assessing biological aging. Together, these examples illustate how quantile-based thinking can move beyond mean-centered modeling toward a richer and more robust understanding of variation, uncertainty, and individualized prediction."

About the speaker

"Huixia (Judy) Wang is the William Marsh Trustee Professor in Data Science and Chair of the Statistics Department at Rice University. She previously held faculty positions at The George Washington University and North Carolina State University and served as a Program Director at the National Science Foundation from 2018 to 2022. Her research spans statistical learning, uncertainty quantification, high-dimensional inference, quantile regression, extreme value theory and applications, spatial data analysis. She is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, an elected member of
the International Statistical Institute, and currently serves as Co-Editor of Statistica Sinica"