About

Quan Geng is a highly skilled machine learning engineer and information theory researcher with extensive experience in high-frequency trading. As a Quantitative Researcher at Citadel Securities, he is responsible for developing and implementing quantitative trading strategies using advanced statistical and machine learning techniques. Quan holds a Doctor of Philosophy (Ph.D.) in Electrical and Computer Engineering from the University of Illinois, where he designed the information theoretically optimal mechanism for differential privacy. He also holds two Master of Science (M.S.) degrees, one in Mathematics and the other in Electrical Engineering, both from the University of Illinois. Additionally, he completed a Bachelor's degree in Electronic Engineering from Tsinghua University and an Exchange Student program in Electronic and Computer Engineering from The Hong Kong University of Science and Technology. Prior to joining Citadel Securities, Quan worked as a Software Engineer at Facebook, where he led the Instagram feed recommendation ML team in New York. He built the recommendation system for Instagram home from scratch, which led to the launch of "Suggested Post" in the Instagram feed. This product increased the topline impressions metrics by more than 8%, and it has over 300 million daily active users. Quan has also served as a Guest Lecturer for Course E6885: Reinforcement Learning at Columbia University in the City of New York. Quan's technical expertise includes research science, ML, recommendation systems, software engineering, and reinforcement learning. He has relevant experience of 9.87 years, and he is passionate about developing innovative solutions to complex problems. Quan's personal homepage provides more details about his work, and he welcomes any inquiries about his research and experience.