June 18-21, 2019
Beijing, CN
Jike Chong

Jike Chong
Chief Data Scientist, LinkedIn | Tsinghua University

Dr. Jike Chong is an accomplished executive and professor with experience across industry and academia.

Jike currently heads Data Science, Hiring Marketplace at LinkedIn. He was most recently the chief data scientist at Acorns, the leading micro-investment app in US with over four million verified investors, which uses behavioral economics to help the up-and-coming save and invest for a better financial future. Previously, Jike was the chief data scientist at Yirendai, an online P2P lending platform with more than $7B loans originated and the first of its kind from China to go public on NYSE; established and headed the data science division at Simply Hired, a leading job search engine in Silicon Valley; advised the Obama administration on using AI to reducing unemployment; and led quantitative risk analytics at Silver Lake Kraftwerk, where he was responsible for applying big data techniques to risk analysis of venture investment.

Jike is also an adjunct professor and PhD advisor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, where he established the CUDA Research Center and CUDATeaching Center, which focus on the application of GPUs for machine learning. Recently, he also developed and taught a new graduate level course on machine learning for Internet finance at Tsinghua University in Beijing, China, where he is serving as an adjunct professor.

Jike holds MS and BS degrees in electrical and computer engineering from Carnegie Mellon University and a PhD from the University of California, Berkeley. He holds 11 patents (six granted, five pending).


09:00 - 17:00 Tuesday, June 18 & Wednesday, June 19
Jike Chong (LinkedIn | Tsinghua University), 黄铃 (Tsinghua University), 陈薇 (排列科技)
您想了解金融企业是怎样利用大数据和人工智能技术来画像个人行为并检测欺诈用户的吗?互联网金融幕后的量化分析流程是怎么杨的?个人信用是怎样通过大数据被量化的?在实践过程中,机器学习算法的应用存在着哪些需要关注的方面?怎样通过图谱分析来融合多维数据,为我们区分正常用户和欺诈用户? 这套辅导课基于清华大学交叉信息研究院开设的一门"量化金融信用与风控分析”研究生课。其中会用LendingClub的真实借贷数据做为案例,解说一些具体模型的实现。 Read more.