2月16日下午，由香港科技大学与香港科技大学（广州）联合举办的国际招生在线展会暨宣讲会顺利召开，面向全球直播讲解港科大和港科大（广州）的研究型硕博项目。香港科技大学霍英东研究生院院长、香港科技大学（广州）副校长（研究生事务）吴宏伟教授出席并致辞。

The majority of the global population dwells in urban areas. Fast urbanization has led to numerous externalities in the transportation system, such as excess carbon emissions, air pollution, and mobility injustice, which are urgent to be resolved in a climate changing world. This research seminar demonstrates my interdisciplinary work on advancing a sustainable transport system. It starts from establishing a quantitative modeling framework for carbon and air pollution emissions from Canada’s largest metropolitan transportation system, the Greater Toronto and Hamilton Area. Personal carbon footprint is combined with granular socio-economic, land use, and travel activity data to investigate the existing environmental and energy justice concerns, revealing the association between high social disadvantage and low mobility-related emissions.

2月9日下午，生命科学与生物医学工程（BSBE）学域举办了2022-2023春季学期暨元宵庆祝活动。BSBE的全体教职工和学生（MPhil & PhD）欢聚一堂，享用丰盛的下午茶、猜灯谜、相互畅谈对学业、研究及工作的心得与收获。

Legged robots have attracted considerable attention from both academia and industry in recent years, thanks to their superiorities of traversing complex terrains and accomplishing complex tasks as compared to traditional wheeled mobile robots. These advantages make them well suited for various applications such as industrial surveillance, search and rescue, last-mile delivery, among others.

With the rapid advances in new-generation information technologies such as the Internet of Things, 5G, and mobile internet, Spatio-Temporal (ST) data are growing explosively. In contrast to image, text, and voice data, ST data often present unique spatio-temporal characteristics, including spatial distance and hierarchy, as well as temporal closeness, periodicity, and trend. Spatio-Temporal AI is a proprietary AI technology for ST data, where AI meets conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology, in the context of urban spaces. This talk first introduces the concept of Spatio-Temporal AI, discussing its general framework and key challenges from the perspective of computer sciences. Secondly, we classify the applications of Spatio-Temporal AI into four categories, consisting of modeling ST point data, ST grid data, ST graphs, and ST sequences. We also present representative scenarios in each category. Thirdly, we delineate our recent progress in the methodologies of the above four categories. Finally, we outlook the future of spatio-temporal AI, suggesting a few research topics that are somehow missing in the community.

The ride-hailing service platforms have grown tremendously around the world and attracted a wide range of research interests. This talk will introduce some recent progress we have made on two key issues of ride-hailing service: demand forecasting and order matching. A key to ride-hailing service platforms is how to realize accurate and reliable demand prediction. However, most of the existing studies focus on region-level demand prediction while only a few attempts have been made to address the problem of origin-destination (OD) demand prediction. In our recent studies, we constructed dynamic OD graphs to describe the ride-hailing demand data from a graph perspective. We proposed two novel neural architectures named the Dynamic Node-Edge Attention Network (DNEAT) and the Dynamic Auto-structuring Graph Neural Network (DAGNN) to address the unique challenges of OD demand prediction from the demand generation and attraction perspectives. Online matching between idle drivers and waiting passengers is another key component in a ride-sourcing system. We proposed two ideas to improve the matching efficiency: early driver matching and delayed passenger matching. The first idea considers the vehicles whose destinations are close to the passenger’s origin so that the passenger’s waiting time may be shorter and the vehicle’s pick-up distance and fuel consumption can be saved. The second one assumes that a specific passenger request can benefit from a delayed matching since he/she may be matched with closer idle drivers after waiting for a few seconds. The problems are solved by reinforcement learning methods. Through extensive empirical experiments with well-designed simulators, we show that the proposed frameworks can remarkably improve system performance.

Proteogenomic identification of translated small open reading frames in human has revealed thousands of microproteins, or polypeptides of fewer than 100 amino acids, that were previously invisible to geneticists. Microproteins have now been linked to important cellular and physiological processes as well as disease in diverse organisms. We developed a high-throughput technology for global mapping of microproteins to specific subcellular organelles by proximity labeling techniques and mass spectrometry-based proteomics. We demonstrated that this technology enables detection of hundreds of microproteins within specific subnuclear regions in cultured human cell lines and in mouse model. Meanwhile, we characterized the biological function of an intrinsically disordered microprotein, NBDY, and showed that NBDY is a master regulator of the substrate specificity of the cytoplasmic RNA decapping complex. We identified that phosphorylation of NBDY from phosphoproteomics downstream of signaling pathways that control cell division and response to growth factors is sufficient to drive the membraneless organelle (Processing bodies) disappearance in human cells.

Metal additive manufacturing (AM) is a family of rising technologies that are well-known for their flexibility in fabricating geometrically and/or material-wisely complex metal parts that are difficult for traditional manufacturing processes. However, metal AM processes are often plagued with a lack of reproducibility and reliability. Computational modeling has become an appealing approach in understanding the Process-Structure-Property (PSP) relationship in AM processes, critical for process control and optimization.

Transportation has been and will continue to beunder rapidtransformation.Onesuchtransformationistransportation systems'increasing reliance on massive datasets, includingthose from vehicles (especially CAVs) and othertransportationusers, from the (intelligent)infrastructure,the data communicated betweenvehicles and the infrastructure, and other sources.However, the growing reliance on data poses criticalcybersecurity issues to transportation systems,among which is the so-called “data poisoning”attacks (DPA). DPAs aim to compromise a system’ sperformance (e.g., deviate traffic state estimation)by adding malicious noises or stealthy perturbationsto the dataset used by the system. Althoughpioneering and practically important, recent studieson DPAs against vehicular data and V2X data havesome limitations, especially in traffic stateestimation and prediction (TSEP) applications,which are often associated with physical constraintsand lack differentiability' in their mathematicalmodels. This work formulates DPAs against TSEPmodels as a general sensitivity analysis ofoptimization problems over data perturbations(attacks) and studies the Lipschitz continuity

Uncertainty widely exists in many engineering problems, such as measurement uncertainty, model uncertainty, parameter uncertainty, and manufacturing uncertainty. These uncertainties will affect the design and manufacture of engineering products. An important issue is how to properly deal with these uncertainties and get more reliable and robust engineering products. In this talk, I will discuss using Bayesian statistics to deal with uncertainty and help provide a better model prediction, especially when the physical model is not accurate (a low-fidelity model), combing experimental measurement data. In addition, I will also discuss using Bayesian statistics for reliability sensitivity analysis, i.e., measuring the effects of random parameters on the failure of structures, which will be helpful for failure control and reliability-based design. Some results will be shown with composite material coupon structures.