Sitan Yang

Sitan is a Principal Applied Scientist at Keystone Core AI. Before joining Keystone, he spent nearly six years at Amazon, where he was a senior applied scientist in the Forecasting team of Supply Chain Optimization group, and he led the deep learning team working on cutting edge AI research and deployment of large scale deep neural networks models for product demand forecasting. His research has been published in top AI/ML conferences such as KDD and NIPS. He started his career at Bloomberg as a Quantitative Analyst developing various Cross-Asset Derivative Pricing functions and later he worked as a Front-office Quantitative Strategist for Equity Option Trading desk at Morgan Stanley. He got his Ph.D. in Statistics from Johns Hopkins University.

Outside of work, Sitan likes playing soccer and enjoys various outdoor activities including hiking and fishing.

Education

• Ph.D. in Statistics from Johns Hopkins University