Haksun Li
Professor Adjunct at Fudan University
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Biography
Dr. Haksun Li is a founder and the CEO of NM FinTech LTD., a financial analytics company. He has the vision of “Making the World Better using Mathematics”. Under his leadership, the firm serves worldwide brokerage houses and funds, multinational corporations and very high net worth individuals. Haksun is an expert in options trading, asset allocation, portfolio optimization and fixed income product pricing. He coded up two of the company’s products: portfolio management system (for equities) as well as supercurve (for bonds). Prior to this, Haksun was a quantitative trader/quantitative analyst with multiple investment banks. He has worked in New York, London, Tokyo and Singapore.
Dr. Haksun Li is the Vice Dean of the Big Data Finance and Investment Institute of Fudan University, China. He was an adjunct professor with multiple universities. He has taught at the National University of Singapore (Mathematics), Nanyang Technological University (Business School), Fudan University (Economics), as well as Hong Kong University of Science and Technology (Mathematics).
Dr. Haksun Li has a B.S. and M.S. in Pure and Financial Mathematics from the University of Chicago, an M.S. and a Ph.D. in Computer Science & Engineering from the University of Michigan, Ann Arbor.
Academic Duties
Dr. Haksun Li is/was
- a Vice Dean of the Big Data Finance Institute of Fudan University, China;
- an Adjunct Professor of the FuDan Stanford China Financial Technologies and Security Institute, Fu Dan University, China;
- an Adjunct Assistant Professor of the Department of Mathematics, National University of Singapore;
- an Adjunct Associate Professor of Banking & Finance at Nanyang Business School, Nanyang Technological University. He was also an industry fellow.
He also taught at these universities in the past.
- Hong Kong University of Science and Technology, Department of Mathematics
- Fudan University, School of Economics
- Beijing University seminar
Videos
Introduction to Quantitative Trading - Lecture 1/8
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