上海交通大学闵行校区软件大楼5楼 人工智能研究院 500会议室
The goal of all-inclusive finance is to bring reliable and high quality financial service to everyone everywhere, rich or poor. Machine learning plays a key role in making financial services more accessible affordable secure and sustainable, and hence more inclusive. This requires solving sophisticated technical challenges in the machine learning space involving a diverse range of entities, such as people, small businesses, online platforms, financial institutes and even fraudsters, and over a wide spectrum of domains including graph & structured learning, adversarial learning, privacy-preserving learning, explainable modeling, reinforcement learning, decision making and optimization, etc. In this talk, I will give a brief introduction to the challenges and opportunities on this topic.
Shuang Yang is a Senior Director and the Head of Machine Learning at Ant Financial. Based in Ant's Silicon Valley office, Shuang leads the R&D in Machine Learning and its applications in User Modeling, Precise Marketing, Risk & Security and other financial domains. Before joining Ant, Shuang was a founding partner and the Chief Scientist at Operator Inc. (2015 to 2017) and the Lead Scientist at Twitter Inc. (2012-2015). Shuang earned his Ph.D from Georgia Institute of Technology. He has published actively at leading conferences and journals, and is the recipient of multiple awards including a Best Paper Award from ACM SIGIR (2011), a Best Paper Award from UAI (finalist, 2010), a Best Paper Award from PAKDD (2008), and a Best Paper Award from 1st CCAI (2008).