讲座人简介：

韩晓旭，1992年毕业于bv伟德国际体育数学系。目前是Baylor University工程和计算机学院McCollum讲席教授。研究主要包括数据科学， 人工智能， 金融科技，信息科学， 量子机器学习。 已经发表130多篇论文在这些领域。 在加入Baylor大学之前， 是福特汉姆大学计算机系的终身教授同时也是福特汉姆大学信息安全专业的创始主任。 研究赢得NSF，NIH，NASA和工业界的课题支持。 指导了70多名员工其中包括博士后，博士和硕士以及本科生。

讲座简介：

AI is revolutionizing human society at an unprecedented pace, penetrating various fields with remarkable speed and depth. However, significant challenges remain, particularly the lack of next-generation machine learning regularization theory and the difficulty in achieving reproducibility and explainability for highly complex data in the era of post-deep learning AI. This talk addresses these pressing challenges and focuses on novel techniques for deciphering explainable intelligence in knowledge discovery within the FinTech, science, biomedical, and law domains to gain explainable and reliable insights and knowledge.