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Gelly Fu is currently a Quantitative Researcher and Data Scientist at Optiver since February 2022. Prior to this role, Gelly completed a PhD in Finance at the Rotterdam School of Management, Erasmus University, from September 2016 to February 2022, where research involved handling extensive financial datasets using SAS and SQL, as well as developing a web scraper in Python for mutual fund filings. Gelly was also a Visiting Scholar at the University of Illinois Chicago, focusing on the influence of peer short interest on stock returns, employing machine learning techniques with tools such as Matlab and Stata. Earlier professional experience includes internships at Robeco, where trading strategies were developed with machine learning, and Shell Asset Management Company B.V., applying Bayesian methods for forecasting. Gelly holds an MSc in Quantitative Finance and a BSc in Econometrics and Operations Research from Erasmus School of Economics, along with a Fall Exchange Program at Yonsei University.