Title: Building Pre-Commercial Challenge Spaces for FinTechs: Innovation Calls, Regulatory Adaptation and Productivity Pathways in Financial Services

Time: 10:30am 

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

Speaker(s): Prof. John Finch

Remark(s): 

Abstract

"Entrepreneurial firms in regulated sectors face a distinctive strategic problem. Their capabilities are still developing; their offerings are not yet stabilised as commercial products; and their market access depends on established sector incumbents, whose bilateral engagement entails procurement, compliance, and reputational obligations that neither party can easily accommodate at the pre-commercial stage. Access to the specialised expertise, sector knowledge, and reputational credibility that would allow the entrepreneurial firm to develop its offering toward commercial viability is precisely what the firm cannot readily obtain — and precisely what the sector incumbent cannot readily provide within a commercial framework. The result is a coordination problem that neither firm-level strategy nor bilateral market engagement resolves cleanly, and that has generated a distinctive institutional response over the last decade: intermediary configurations that convene entrepreneurial firms, sector incumbents, regulators, and universities in structured, time-bounded engagements around jointly-defined pre-commercial challenges.

This paper addresses the specific question of how entrepreneurial firms strategise their engagement with such intermediary configurations, and how the intermediary’s design shapes what firms can accomplish through participation. Our empirical setting is the Financial Regulation Innovation Lab (FRIL), an Innovate UK-funded programme run through a University and Industry Cluster consortium from April 2023 through March 2026, which has run four sequential innovation calls with twenty-two funded fintech firms addressing regulatory challenges ranging from AI-enabled compliance simplification through ESG reporting, Consumer Duty, and operational resilience. Across the twenty-two funded firms, engagement strategies vary substantially. Some firms use their FRIL participation to develop a tailored offering for a specific sector sponsor and exit the programme with a validated proof of concept. Others use the programme as an entry point into wider ecosystem participation, extending their engagement into FSF-led incubators or third-sector distribution channels. A small number participate in multiple FRIL calls, using the programme’s four regulatory domains to develop integrated, multi-domain capabilities over sequential engagements. What accounts for this variation, and what does it tell us about how entrepreneurial firms engage strategically with pre-commercial intermediary configurations?"

About the speaker

"John Finch is Professor of Marketing at the University of Glasgow’s Adam Smith Business School, where he served as Head of School (Dean) from 2016 to 2023, and is currently Head of its Management subject area. His research focuses on markets, innovation, regulation, and policy implementation, with a particular interest in how organisations, regulators, and professional communities shape economic exchange. His recent work examines financial regulation, sustainability governance, ESG, consumer protection, innovation systems and evidence-informed policy.
John has led and contributed to externally funded research with academic, policy, and industry partners, including the Financial Regulation Innovation Lab with FinTech Scotland and the University of Strathclyde, and to policy work as an innovation fellow with the UK Government Department for Business, Innovation, and Science. He is the Editor-in-Chief of Asia Nexus Journal of Management, a new journal that supports Asia-grounded management scholarship with global relevance.
Alongside his research, John has extensive experience in academic leadership, international partnerships, accreditation and sectoral development. He has worked with universities and business schools across Europe, East Asia and beyond, including through EQUIS and AACSB peer review."

Title: Ambient Sensing and Intelligence: Where AI Meets Physics

Time: 10:30am 

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

Speaker(s): Prof. Anthony Lin

Remark(s): 

Abstract

Transformers are revolutionary neural networks architecture, which has been the backbone of our modern Large Language Models (LLMs). Despite the success of transformers in practice, we often do not know why they work (or occasionally also, why they do not work). Recent years have witnessed rapid progress in understanding transformers through the lens of logic and automata (in the community called FLaNN = Formal Languages and Neural Networks). In particular, the toolbox from logic and automata (i.e. connections to linear temporal logic) has helped us understand why PARITY (and in general “state-tracking”) is difficult for transformers. I will recount some of the fundamental results in the field and open problems at the intersection of logic, automata, verification and transformers. I will also discuss new architectures including state-space models ( more generally linear RNNs), and how these compare in expressiveness to transformers.

This talk is based on recent publications (including at ICLR'24, ICLR'26, and ICML'26).

About the speaker

Anthony Lin completed his PhD in 2010 at University of Edinburgh. He is currently a full professor at TU Kaiserslautern (Germany) and a Fellow of Max-Planck Society. Prior to this, he was an assistant professor at Yale-NUS College (Singapore) and an associate professor at Oxford University (UK). His works have received multiple recognitions including ERC Starting Grant, ERC Consolidator Grant, Google Faculty Research Award, Amazon Research Award, and ICLR'26 Best Paper Award.

Title: Consensus and Random Walks on “Higher-order Networks

Time: 10:30am 

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

Speaker(s): Prof. Renaud Lambiotte

Remark(s): 

Abstract

Renaud Lambiotte is a Professor of Networks and Nonlinear Systems at the Mathematical Institute of the University of Oxford and a Tutorial Fellow at Somerville College. As a distinguished network scientist, he is also a Turing Fellow at the Alan Turing Institute in London and an External Faculty member at the Complexity Science Hub Vienna. After receiving his Ph.D. in Physics from the Université Libre de Bruxelles, he held postdoctoral positions at prestigious institutions including ENS Lyon, Université de Liège, UCLouvain, and Imperial College London before becoming a Professor of Mathematics at the University of Namur. His research primarily focuses on the modeling and analysis of large-scale networks, with particular emphasis on social networks, brain networks, network clustering algorithms, empirical hypergraphs, and temporal networks.

Professor Lambiotte has made significant contributions to the field with approximately 150 peer-reviewed publications and is the author of two books: Modularity and Dynamics on Complex Networks (Cambridge University Press, 2022) and A Guide to Temporal Networks (World Scientific,2021). He currently serves as a Senior Associate Editor for Science Advances, further demonstrating his continued influence in the academic community.

About the speaker

Random walks play a central role in modern network science, as a model for diffusion and to extract "multi-scale" information in relational data. In this talk, I will give an overview of recent generalisations to higherorder networks. These generalisations include hypergraphs, accounting for multiway interactions, temporal networks, where edges are dynamical objects, and signed networks, allowing for negative edges to encode conflictual interactions.

Title: Ambient Sensing and Intelligence: Where AI Meets Physics

Time: 03:00pm 

Venue: HW312, Haking Wong Building

Speaker(s): Prof. K. J. Ray Liu

Remark(s): 

Abstract

Utilizing ambient radio waves to monitor human activities has long been a dream of many. Historically treated as unwanted interference, the millions of bouncing radio multipaths in everyday environments can now be observed, thanks to the broader bandwidths of modern Wi-Fi, IoT, and 5G/6G devices.
This talk introduces a paradigm shift: turning such interference into intelligence by treating multipaths as hundreds, if not millions, of virtual sensors. A new physical principle will be presented, showing that the time-reversal focusing spot exhibits a stationary Bessel function power distribution, enabling accurate speed estimation even under severe non-line-of-sight conditions. Defying long-standing scientific belief, this approach thrives indoors where the Doppler Effect fails, proving that indoor environmental complexity is actually a source of precision. Enhanced by AI, this revolutionary ambient intelligence enables a new wave of device-free, non-obtrusive IoT applications. The talk will feature the world’s first centimeter-accuracy wireless indoor positioning system, alongside applications in contactless vital signs detection, sleep monitoring, and fall detection using commodity Wi-Fi. Ultimately, ambient sensing gives future wireless networks a "sixth sense" to decipher the world around us and will forever change the future of wireless systems.

About the speaker

K. J. Ray Liu is the founder of the award-winning Origin AI, acquired by ADT in 2026, that pioneers ambient sensing and intelligence. He was the 2022 IEEE President and CEO and 2012-13 President of IEEE Signal Processing Society. He retired from University of Maryland, College Park, as Distinguished University Professor. He has trained 76 doctoral/postdoctoral students, of which 14 are now IEEE fellows with over 200 doctoral descendants. Prof. Liu is a recipient of many prestigious awards, including two IEEE Technical Field Awards: the 2021 IEEE Fourier Award for Signal Processing and the 2016 IEEE Leon K. Kirchmayer Graduate Teaching Award, 2026 IEEE Haraden Pratt Award, and also IEEE Signal Processing Society 2014 Norbert Wiener Lifetime Achievement Award, 2009 Claude Shannon-Harry Nyquist Technical Achievement Award, and more than a dozen best paper awards. Recognized as a Web of Science Highly Cited Researcher, he is a member of National Academy of Engineering and a Fellow of IEEE, the American Association for the Advancement of Science (AAAS), and the National Academy of Inventors.

Title: Statistical Estimation and Inference in High-Dimensional and Semiparametric Models

Time: 10:30am 

Venue: RR301, Run Run Shaw Building

Speaker(s): Dr. Yuanhang Luo

Remark(s): 

Abstract

High-dimensional parameters arise naturally in many modern statistical problems — from regression with a large number of covariates to ranking models where the number of latent utilities grows with the size of the comparison graph. In this talk, I will present two recent works that address estimation and inference challenges in these high-dimensional and semiparametric settings.

The first part concerns online inference in high-dimensional generalized linear models with streaming data. We develop the Adaptive Debiased Lasso (ADL), which updates coefficient estimates and confidence intervals upon each new data arrival. The method features an adaptive stochastic gradient descent algorithm with a novel online debiasing procedure via Taylor approximation, achieving asymptotic normality with only O(p) space and time complexity instead of O(p²) in previous methods. In the second part, I will present a semiparametric model for ranking data whose underlying graph structure governs both the dimensionality of the problem and the difficulty of estimation. In the comparison hypergraph, each object's strength is modeled as the sum of a utility parameter and a nonparametric covariate effect approximated by a deep neural network. Non-asymptotic error bounds achieving minimax optimality for model components are established. The framework is demonstrated on an ATP tennis dataset that capturing nonlinear contextual effects in player performance.

About the speaker

Yuanhang Luo is currently a PhD student in the Department of Data Science and Artificial Intelligence at the Hong Kong Polytechnic University. He received his B.Sc. in Mathematics & Statistics from Hong Kong Baptist University. His research focuses on high-dimensional statistics, ranking and reinforcement learning.

Title: Addressing Biases, Batches and Hidden Heterogeneities in Microbiome Studies

Time: 11:00am 

Venue: Room 301, Run Run Shaw Building

Speaker(s): Prof. Ni Zhao

Remark(s): 

Abstract

Microbiome data, like other high-throughput omics data, are susceptible to technical artifacts, including batch effects, measurement biases, and latent sources of heterogeneity. These challenges present major barriers to large-scale, multi-site, and integrative microbiome studies, where existing methods often rely on restrictive assumptions and may yield unreliable inference under realistic community-level variation. In this presentation, I will highlight recent methodological advances from our group to address these challenges, including ConQuR, a method for correcting known batch effects; QuanT, a framework for detecting latent or unknown sources of heterogeneity; and CAFT, a statistically principled approach for mitigating bias in differential abundance analysis. These methods are built on flexible nonparametric statistical models that accommodate the irregular, heavy-tailed, and zero-inflated characteristics of microbiome data, enabling more robust and reliable inference across diverse study settings.

About the speaker

Dr. Ni Zhao received her PhD from the University of North Carolina at Chapel Hill and is currently an Associate Professor and PhD Program Director in the Department of Biostatistics at Johns Hopkins University. Her primary research interests lie in statistical genetics and genomics, with a particular focus on developing statistical methods for microbiome studies, including both bulk and spatial microbiome profiling. In recent years, her lab has made significant contributions to understanding and addressing batch effects, biases, and other sources of technical variation in microbiome studies. She has also been actively involved in large-scale epidemiologic studies, where microbiome and multi-omics data integration are central components. Over the past decade, Dr. Zhao has published more than 40 peer-reviewed papers in leading journals across statistics, epidemiology, and biomedical and clinical research.

Title: Evaluating Causes of Effects by Posterior Effects of Causes

Time: 10:30am 

Venue: Room 301, Run Run Shaw Building

Speaker(s): Dr. Zitong Lu

Remark(s): 

Abstract

As highlighted in Dawid (2000) and Pearl & Mackenzie (2018), deducing the causes of given effects is a more challenging problem than evaluating the effects of causes in causal inference. For the case with a single causal variable, the probability of causation and the probability of necessity have been used to assess causes of effects. For a case with multiple causes that may affect each other, we propose the posterior causal effects based on observed evidence, as a measure of causes of effects. Since posterior causal effects involve probabilities of counterfactual variables, their identifiability requires assumptions of no confounding and monotonicity beyond those needed for traditional causal effects; we present these assumptions and provide the corresponding identification equations. We further extend this framework to settings with multiple effect variables. The proposed approach applies broadly to causal attribution, medical diagnosis, and the assessment of blame and responsibility in studies with multiple effect or outcome variables, and we illustrate it through numerical examples.

About the speaker

Dr. Zitong Lu is a Postdoctoral Fellow in the Department of Statistics and Data Science at the Chinese University of Hong Kong. He received his Ph.D. in Systems Engineering from City University of Hong Kong and a B.Sc. in Statistics from Peking University. His research focuses on causal inference, particularly causal attribution and individual treatment effects.

Title: Functional Dynamics in Non-Functional Data via Principle Component Analysis

Time: 10:30am 

Venue: Room 301, Run Run Shaw Building

Speaker(s): Prof. Zhijie Xiao

Remark(s): 

Abstract

Distributions of many economic and financial time series variables contain important information that affects investment and economic policy. The study of distributional relationship has attracted a lot of research attentions recently, and many models are proposed to capture dependence on different aspects of the distributional relationship. The majority of existing literature consider this problem by focusing on some selected representative characteristics of a distribution - such as the variance, dispersion, or a particular quantile - and model dependence based on these selected characteristics. We argue that focusing only on some selected characteristics could potentially miss important information about the dependence relationship, and propose functional quantile regression models to study the distributional dependence relationship in time series data. In the proposed functional quantile regression models, the future economic behavior can be affected by the past distributional information in the economy. The models can capture systematic influences of the past distributional information on the conditional distribution of the response, and therefore constitute a significant extension of traditional time series models in which the effect of conditioning information is confined to only a few selected characteristics of the past distribution. Unlike traditional functional regression models that rely on rich data environments with functional data features, our approach focuses on functional relationships within conventional time series data. We consider a linear functional quantile autoregression model and explore estimation and dimension reduction via Functional Principal Component Analysis (FPCA) in this paper. We propose a threestep estimation procedure, and analyze limiting properties of the proposed estimators. Uniform asymptotic results are developed to facilitate statistical inference based on the functional model. We show that the proposed FPCA-based estimator of the conditional quantile function achieves near root-n convergence rate, improving upon the nonparametric rate of conventional sieve estimators. Monte Carlo experiments are conducted and show improved finite sample performance of the proposed estimator compared to other estimators. Finally, an empirical application to S&P 500 index illustrates the potential of the new method in capturing complex risk dynamics.

About the speaker

Zhijie Xiao currently is a professor at the Department of Economics, Boston College. He obtained his PhD in Economics from Yale University in 1997. His researches cover all kinds of areas in econometrics and statistics, and especially he is a leading figure in quantile regression. Prof Xiao has received many awards from econometric community, including Plura Scripsit Award in Econometric Theory, fellow of Journal of Econometrics, etc., and he has served, is serving, as the editorial board for top journals in econometrics and statistics, such as Journal of Econometric, JASA, etc.