Title: Optimal Reinsurance Maximising Dividends: A Discretetime Dynamic Problem and Numerical Results

Time: 02:00pm 

Venue: RR301, Run Run Shaw Building

Speaker(s): Prof. Debora Daniela Escobar

Remark(s): 

Abstract

We formulate the optimal reinsurance problem maximising cumulative dividend payments in discrete-time, where our decision is the ceding loss function for each stage. Reinsurance is applied to the aggregated loss of each stage, and the reinsurance premium is given by a distortion risk measure. Considering also dividends as part of the decision variable, we maximise our objective under (a) the surplus, and (b) adding a solvency constraint that controls the ruin probability. Thirdly, we solve a last problem, (c) under both constraints when dividends are given by a pre-specified dividend rule. Problems (a)-(b) reduce to solving unconstrained static problems. We find multi-layered optimal policies by minimising the expected loss of the insurer for each stage, moreover, (b) offers a dividend rule that caps the surplus. In contrast, the optimal policy for (c) with the barrier dividend rule can be found by solving constrained problems for each stage, where the constraint imposes an upper bound to the retained losses. We obtain multilayered policies and propose a Linear Programming (LP) to approximate its deductibles. We show results for the Expected value, Value-at-Risk, Average-Value-at-Risk and Glue Value-atRisk. The deductibles we estimate show a relationship with the distortion, the barrier, and the income of the insurer.

About the speaker

"I joined the Department of Actuarial Mathematics and Statistics in Heriot-Watt University in September 2023. Previously, I was an Assistant Professorial Lecturer in the Department of Statistics in the London School of Economics (LSE), where I was the Program Director of the BSc Actuarial Science. I initially joined LSE as a Fellow in the same department following my PhD. I completed my PhD in the Department of Statistics and Operations Research, at the University of Vienna, under the supervision of Prof Georg Ch. Pflug. I have an MSc in Statistical and Computational Data Analytics from the Complutense University of
Madrid and the Technical University of Madrid. I hold a BSc in Mathematics from the Complutense University of Madrid."

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: 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: 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.