Student Seminar by Mr. Xinhao Yan on Nov 03, 2026, 3:00PM

Title: Secure Safety Monitoring for Unmanned Aerial Systems: Communication Privacy and Physical Safety

Speaker: Mr. Xinhao Yan (Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University)

Date: Nov 03, 2026 (Tuesday)

Time: 3:00 pm - 4:00 pm

Venue: Tam Wing Fan Innovation Wing Two, G/F, Run Run Shaw Building, HKU

Registration Link: https://forms.cloud.microsoft/r/Vq7rZfGqTR

ITS Student Committee will provide snacks and beverages for registered participants.

Abstract: Unmanned aerial systems (UASs) are increasingly used in transportation, logistics, and infrastructure monitoring, where trustworthy operation depends on both communication privacy and physical safety. Sensitive state information must remain protected during transmission, while uncertain motion and potentially compromised measurements complicate the monitoring of non-cooperative UAVs. This talk explores how state estimation can address these challenges by protecting information from eavesdroppers and supporting timely assessment of airspace intrusion risks. It examines privacy-preserving mechanisms that balance confidentiality, estimation accuracy, and computational efficiency, together with monitoring methods that translate uncertain observations into predictive and attack-aware safety warnings. The discussion draws on cryptographic protection, secrecy coding, and temporal logic, with theoretical analysis, simulations, and UAV experiments illustrating their capabilities and practical limitations. The talk also considers how these ideas can inform future monitoring systems that provide the information needed for reliable safety decisions while limiting unnecessary disclosure.

Bios: Xinhao Yan received the B.E. degree in communication engineering and the M.E. degree in control science and engineering from Zhejiang University of Technology, Hangzhou, China, in 2020 and 2023, respectively. He is currently pursuing the Ph.D. degree at the Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong. He was a visiting student at Information Systems Architecture Science Research Division, National Institute of Informatics, Japan. In 2023, he was a recipient of the Outstanding Master's Thesis Award of Chinese Association of Automation. His current research interests include estimation theory, security, privacy, safety, formal methods, and their applications to intelligent vehicles.

Student Seminar by Mr. Muyi Zhu on Oct 22, 2026, 11:00AM

Title: Generative AI and Causal-Spatial Modeling for Understanding Nighttime Pedestrian Risk

Speaker: Mr. Muyi Zhu (Department of Geography, HKU)

Date: Oct 22, 2026 (Thursday)

Time: 11:00 am - 12:00 nn

Venue: CLL, 1010, 10/F, Jockey Club Tower, HKU

Registration Link: https://forms.cloud.microsoft/r/wn7K6cGDPP

ITS Student Committee will provide snacks and beverages for registered participants.

Abstract: Understanding how nighttime urban environments influence pedestrian safety remains challenging due to limited visual data and complex spatial interactions. This study integrates generative AI and causal-spatial machine learning to investigate pedestrian crash risks in Hong Kong. A locally fine-tuned CycleGAN model synthesizes citywide nighttime street-view imagery to characterize illumination, visibility, and visual complexity. These features are analyzed using geographically weighted causal random forests and double machine learning to estimate localized effects on pedestrian safety. Results reveal that visual overload increases crash risks, while clear spatial boundaries and balanced lighting contrast enhance safety. Notably, walking path visibility is more important than overall brightness. The proposed framework provides valuable insights for safer, context-sensitive urban street design.

Bios: Muyi Zhu is currently a Research Assistant in the Department of Geography and an incoming PhD student at ETH Zurich. His research focuses on traffic safety, environmental exposure, and active urban mobility.

Student Seminar by Dr. Qihao Wu on Oct 06, 2026, 3:00PM

Title: Data-driven Prediction and Dynamic Decision Making for Hospital Operations

Speaker: Dr. Qihao Wu (Rotman School of Management, University of Toronto; Department of Data and Systems Engineering, HKU)

Date: Oct 06, 2026 (Tuesday)

Time: 3:00 pm - 4:00 pm

Venue: Tam Wing Fan Innovation Wing Two, G/F, Run Run Shaw Building, HKU 

Registration Link: https://forms.cloud.microsoft/r/1TtEiDWygf

ITS Student Committee will provide snacks and beverages for registered participants.

Abstract:

Modern healthcare operations are becoming increasingly complicated. Improving the efficiency and the quality of healthcare delivery is a significant research agenda that advances public welfare. This work aims to bridge the research gap of deploying both OR and AI solutions in prediction and decision-making tasks for hospital operations, with particular considerations about explainability, robustness, equity, and the curse of dimensionality. At macrolevel, patient boarding across Hong Kong public hospitals during the Omicron surge has been evaluated and interpreted.

Vector autoregression with lagged variables is used to predict patient waiting times and analyze the causality. At mesolevel, a robust patient-redirecting policy for coordinating multiple inpatient units has been proposed to address the imbalance between patient demand and healthcare resource supply. With the structural properties of the formulated constrained Markov decision process (MDP), the distributionally robust optimization can be efficiently solved.

At microlevel, an effective outpatient scheduling scheme that ensures equitable access to care for heterogeneous patients has been developed based on MDP and an optimal stopping mechanism. The contributions can be summarized into two aspects. Methodologically, the novel OR and AI solutions developed for healthcare operations, such as explainable AI frameworks, constrained MDPs, and model-informed RL, can also be applied to other domains, particularly in terms of explainability, robustness, equity, and large-scale optimization. Regarding healthcare applications, this work comprehensively traverses macrolevel, mesolevel, and microlevel. The key findings, such as elderly admission leading to patient boarding, robust inpatient redirect policies, and equitable outpatient scheduling, serve as managerial implications for stakeholders' decision support.

Bios: Dr. Wu is joining the Rotman School of Management as a postdoctoral fellow, with a cross-appointment in the Department of Psychiatry, University of Toronto. He received his Ph.D. in the Department of Data and Systems Engineering from HKU, his M.Sc. from Imperial College, and a dual B.Eng. (honours) from Beijing Jiaotong-Wollongong joint program. Qihao’s research focuses on healthcare management, OR, and AI, particularly data-driven operations and decisions in complex systems.