孙浩，中国人民大学高瓴人工智能学经理聘副教授、博士生导师（2021至今），麻省理工学院兼职研究员、美国东北  大学兼职教授，国家高层次人才计划青年专家，中国人民大学杰出学者A岗。2014年在美国哥伦比亚大学取得工程力学博士学位，随后在麻省理工学院从事博士后研究（2014-2017），曾任美国匹兹堡大学（2017-2018）、美国东北大学终身序列助理教授、博导（2018-2021）。主要从事人工智能数理基础与理工交叉研究，包含可诠释性深度学习、物理驱动深度学习、符号强化学习与推理、复杂系统数据驱动建模与识别、控制方程找型、基础设施健康监测与智能化管理等方向。主持（或共同主持）美国科学基金等研究项目共计330余万美元；在国际一流期刊（如《自然-通讯》）和计算机顶会等各类刊物上共发表论文50余篇；受邀到麻省理工学院、加州理工学院、加州大学伯克利分校等世界名校做学术报告；在过去几年内，研究成果受到了几十家国际知名媒体的广泛报导（例如《福克斯新闻》、《麻省理工新闻》、《科学日报》等著名媒体）；担任国际综合期刊 PLOS ONE 学科主编。 2018 年入选“福布斯美国 30位 30 岁以下精英榜（科学类）”，2019 年 5 月当选“美国十大华人杰出青年”。

内容简介：Harnessing data to model and discover complex physical systems has become a critical scientific problem in many science and engineering areas. The state-of-the-art advances of AI (in particular deep learning, thanks to its rich representations for learning complex nonlinear functions) have great potential to tackle this challenge, but in general (i) rely on a large amount of rich data to train a robust model, (ii) have generalization and extrapolation issues, and (iii) lack of interpretability and explainability, with little physical meaning. To bridge the knowledge gaps between AI and complex physical systems in the sparse/small data regime, this talk will introduce the integration of bottom-up (data-driven) and top-down (physics-based) processes through a Physics-informed Learning and Reasoning paradigm for discovery of discrete and continuous dynamical systems. This talk will discuss several methods that fuse deep learning and symbolic reasoning for data-driven discovery of mathematical equations (e.g., nonlinear ODEs/PDEs) that govern the behavior of complex physical systems, e.g., chaotic systems, reaction-diffusion processes, wave propagation, fluid flows, etc.