When I joined the CQF program, I was working as a quantitative analyst in China, but my academic background was in business management. I already had experience in financial data analysis, yet I felt the need to build a stronger mathematical foundation and a more structured understanding of quantitative finance. The CQF provided exactly that. Through its intensive curriculum, I developed a solid grasp of stochastic calculus, risk and derivatives modeling, and machine learning applications in finance. The program helped me connect practical work with the underlying theory and gave me the confidence to approach financial problems with greater mathematical precision.
Through its intensive curriculum, I developed a solid grasp of stochastic calculus, risk and derivatives modeling, and machine learning applications in finance.
A major influence for me was Dr Riaz Ahmad. His teaching made mathematical finance and programming come alive, helping me see how financial concepts can be expressed and understood through mathematics. I still remember studying late at night after work, coding in Python and applying concepts such as the Black Litterman framework and credit valuation adjustment. Each module added a new layer of intuition and discipline to my thinking.
The CQF experience deepened not only my technical ability but also my commitment to lifelong learning. Today, I am working on research and projects that apply Generative AI and Large Language Models to quantitative analysis, and I continue to find the CQF network and resources invaluable for staying at the frontier of financial innovation. Looking back, it remains one of the most meaningful and rewarding experiences of my professional journey.
