报告题目：Deep learning in science and engineering

报  告 人： 潘宝祥 研究员

邀  请 人： 刘德地 教授

时      间： 2022年7月11日（星期一）下午2:30-4:00

地      点： 水电科技大楼A区202会议室

会议链接： https://meeting.tencent.com/dm/SiKx5HO4az3W

腾讯视频会议（ID: 448 688 164）   B站直播 （ID: 23115892)

报告人简介：

潘宝祥，美国劳伦斯利弗摩尔国家实验室研究员，主要研究兴趣包括概率信息理论、结合机器学 习与动力模式的天气-气候尺度预报、动力系统可预报性。2012年本科毕业于武汉大学，2015年 在清华大学获得工学硕士学位，2019年于加州大学欧文分校(University of California, Irvine) 获得工学博士学位，师从Dr. Soroosh Sorooshian, Dr. Kuolin Hsu, Dr. Amir AghaKouchak。

报告简介：

Deep neural networks, operate with large, high quality data, which together with proper computation resources, motivate an ongoing paradigm shift in scientific discovery and engineering practices. This talk is for domain experts who are interested in deep learning, and would like to apply deep learning to make predictions, explanations, or quickly explore research ideas. I will briefly review the technical history of deep learning, discuss six mindsets underpinning the data-driven modeling paradigm, use several application studies to illustrate the potential pitfalls and benefits for applying deep neural networks in specific problems. I encourage an open discussion of research frontiers, given that individuals could hardly follow the fast progress in this field. Finally, I will close the talk by providing useful resources for learning and tracking advances in this field.

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