Summary
Kexuan Ma is a quantitative researcher and Operations Research master’s candidate at Columbia University with a strong track record of applying machine learning and parallel computing to financial problems. He built large-scale data pipelines and predictive models—from generating over one million candlestick images feeding CNNs to assembling a 100-factor technical database and ensemble models that pinpoint market inflection points. His toolkit spans Python, C++, SQL and PyTorch, and he has proven experience optimizing performance on Linux clusters and using BERT and random forests for messy real-world matching tasks. With internships at Charlton Capital, Futu Holdings, and Everbright Securities, he combines academic rigor (3.96 GPA, Columbia; First Class Honors in Financial Engineering) with production-focused research that delivered measurable returns. Based in New York, he’s as comfortable tuning model performance as he is unwinding on the badminton court or the ski slope.
10 years of coding experience
1 year of employment as a software developer
Bachelor's degree, Financial Engineering, First Class Honor (Dated July 31, 2024), Bachelor's degree, Financial Engineering, First Class Honor (Dated July 31, 2024) at The Chinese University of Hong Kong, Shenzhen 香港中文大学(深圳)
High School Diploma, High School Diploma at Shenzhen Experimental High School
Master's degree, Operations Research, 3.96/4.00, Master's degree, Operations Research, 3.96/4.00 at Columbia University
English, Chinese