题目 ：Data-driven methods for smart urban transition
报告人 ：韩梦捷（瑞典达拉纳大学）
时间 ：2024年6月24日 15:00-16:00
地点 ：36-507
摘要：Rapid urbanization necessitates transformative approaches to urban development. The pivotal role of data-driven methods in facilitating sustainable and efficient urban transitions has been comprehensively studied. Leveraging extensive datasets sourced from diverse domains such as transportation, energy consumption, socio-economic indicators, and environmental metrics provides robust frameworks for understanding and managing urban growth. This report will highlight key challenges associated with urban transitions, pinpointing inefficiencies and the lack of responsiveness to dynamic urban needs. It will scrutinize how data-driven methodologies, including predictive modeling, intelligent control, mathematical optimization, and AI applications, can address these challenges. European case studies will be examined to demonstrate successful implementations of data-driven strategies, illustrating improvements in areas such as urban mobility, health care, energy efficiency, and sustainability. By adopting a multi-disciplinary approach, this report will discuss the importance of collaboration among urban planners, data scientists, business, and the community to achieve holistic and inclusive urban transitions.
报告人介绍： 韩梦捷是瑞典达拉纳大学微观数据分析副教授、信息与数据管理高级讲师，即将作为特聘教授加盟南通大学。他致力于开发绿色城市转型、智能控制、决策支持系统、统计与数学交叉学科中的数据密集型方法和算法。他开发了利用统计学方法生成大规模能源消耗数据，为城市能源建模和管理提供了实践经验，并补充了基于传感器的数据采集不足。他通过与瑞典当地、欧盟区域内的企业、政府及社会团体合作，领导并参与了十多个科研项目，同时指导了五名博士研究生。他现已发表40多篇同行评审文章，以第一作者或通讯作者发表SCI文章10篇，其中包括三篇top期刊，如Information Sciences，Sustainable Cities and Society，以及多篇一区期刊，如Applied Soft Computing，共被引2100多次。他还担任了《Buildings》期刊的客座编辑，以及比利时Brussels Institute for Research and Innovation项目评委。
