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Muzhe Zeng is an experienced algorithm developer currently employed at Hudson River Trading since August 2021. Prior experience includes serving as a research assistant at the University of Wisconsin-Madison, where Muzhe developed the Vintage Sparse Principle Component model and contributed to tensor latent factor models. Work as an ML/Data Scientist at Facebook involved creating an NLP-based issue prediction system, while a role as a Data Scientist at Amazon focused on building and deploying an end-to-end data pipeline. Additional experience includes a position as a Market Quantitative Analyst at Shenzhen Stock Exchange and research assistant roles at the University of Chicago Booth School of Business. Muzhe Zeng holds a PhD and a Master’s degree in Statistics and a Master’s degree in Computer Science from the University of Wisconsin-Madison, along with a Master of Arts in Statistics from the University of Chicago and a Bachelor of Science in Mathematical Statistics from Peking University. A summer course in Machine Learning was also completed at Carnegie Mellon University.