“凤回巢”系列学术讲座

榜样引领，薪火相传。新加坡、香港、加拿大三地南航校友回校报告

1)报告题目：Identifying COVID-19 Cases in Singapore During the Early Pandemic Period

报告人：孙殷霄翯

报告时间：2022年6月2日 19:00-20:00

报告地点：腾讯会议 911-7495-8724

报告简介：Rapid identification of COVID-19 cases, which is crucial to outbreak containment efforts, is challenging due to the lack of pathognomonic symptoms and in settings with limited capacity for specialized nucleic acid–based reverse transcription polymerase chain reaction (PCR) testing. Thus, we developed models to identify COVID-19 cases via epidemiological and clinical features. Thereafter, a Web Application with improved algorithms was developed for use in outpatient settings. The Web Application assisted physicians decide those patients who need priority PCR testing in early outbreak.

报告人简介：孙殷霄翯，南京航空航天大学信息与计算科学专业理学学士、新加坡国立大学统计与应用概率专业硕士。现为新加坡国立大学公共卫生学院博士生、助理研究员。主要研究方向为传染病流行病学的传染病建模和贝叶斯方法。在Clinical Infectious Diseases、Risk Management and Healthcare Policy等刊物发表论文11篇，成功申请1项种子研究基金（S$100,000）。

2)报告题目：Intralogistics Synchronization in Robotic Forward-reserve Warehouses for Last-mile Delivery

报告人：蒋敏

报告时间：2022年6月2日 20:00-21:00

报告地点：腾讯会议 911-7495-8724

报告简介：Intralogistics operations (IOs) are defined as the operations inside a warehouse. This talk discusses IOs in a typical robotic forward-reserve e-commerce warehouse involving manual order-picking and robotic order sorting operations. It is critical to synchronize IOs between different areas considering delivery requirements to improve the overall performance. This challenge is formulated as a delivery-driven intralogistics synchronization (DDIS) problem. Our study develops a tailored variable neighborhood search solution method. A series of comprehensive numerical experiments under various scenarios show the superiority and stability of the DDIS as benchmarked with sequential approaches. A substantial reduction can be achieved in both makespan and forward area size, indicating significantly improved intralogistics operational efficiency and space utilization. Managerial insights are also discussed for specific action plans regarding the market size, the labor force, and the trolley configuration.

报告人简介：蒋敏，南京航空航天大学工业工程系学士、香港大学工业工程系博士。现任Barclays Capital（香港）量化研究员。博士期间研究方向为智能仓储、运筹优化与供应链管理。作为第一作者在Transportation Research Part E: Logistics and Transportation Review发表过多篇相关论文，并担任EJOR、TRE、IJPR等期刊审稿人。

3)报告题目：Exploiting Human Trust to Improve Recommender Systems

报告人：Jie Zhang

报告时间：2022年6月9日 16:00-17:00

报告地点：腾讯会议 911-7495-8724

报告简介：Recommender systems based on collaborative filtering suffer from the problems of data sparsity and cold start. Trust-aware recommender systems exploit human trust relationships to improve recommendation performance. In this talk, I will briefly summarize the state-of-the-art methods along this line of research and highlight a few representative works from our group. I will also point out several remaining challenges and interesting future directions.

报告人简介：Jie Zhang，南京航空航天大学计算机学院学士、加拿大滑铁卢大学计算机科学学院博士。现任新加坡南洋理工大学计算机科学与工程学院副教授。研究领域是人工智能的子领域——用户建模，主要研究各种新兴应用领域的信任建模和偏好建模。论文在顶级人工智能会议（如NeurIPS、AAAI和IJCAI）和顶级期刊（如TKDE和AIJ）上发表。多次在会议上获得最佳论文奖，以及斯坦福大学颁发的世界顶尖2%科学家奖和AI 2000最具影响力学者荣誉奖。担任《Electronic Commerce Research and Applications》杂志的高级编辑，并将担任2023年ACM Recommender Systems会议的总主席。