Financial Engineering Club
The Financial Engineering Club at UIUC, in collaboration with the MS Financial Engineering program, strives to bridge the gap between academic theory and technical practice, equipping students with hands-on experience through real world projects in quantitative finance. By fostering awareness of emerging financial landscapes and emphasizing coding implementation, the club enables members to apply theoretical knowledge to practical scenarios. Through collaborative projects, presentations from leading professionals, and skill-building workshops, the club cultivates an environment where students refine their technical expertise, articulation of complex tools and methods, and commitment to ethical competence. The goal is to empower members with the insights, skills, and networks essential for success in the dynamic world of financial engineering. All students with a quantitative background at UIUC (Engineering, Computer Science, Finance, Physics, etc.) with an interest in financial engineering or quantitative finance are encouraged to join the club.
The Financial Engineering Club was founded by a group of currently enrolled MSFE students in the spring of 2025 and had an outstanding inaugural semester. The club hosted two expert-led presentations by industry professionals, organized two skill-based workshops conducted by MSFE students on Options Pricing and Large Language Models, and supported several member-led group projects throughout the term.
Three types of projects were offered in the first semester: “Binomial De-Americanization of Financial Options”, “Machine Learning for Options Pricing”, and “Large Language Models in Finance”. The “Options Binomial De-Americanization” project focused on pricing European and American options using the binomial framework and the implementation of a De-Americanization procedure model. “Machine Learning for Options Pricing” projects consisted of developing a machine learning-based system for pricing American/European options and estimating implied volatility using open-source data. “Large Language Models in Finance” projects were open ended, where students explored how Large Language Models could be used in Finance.
Throughout the semester, project groups developed their ideas, created detailed posters, and submitted written reports to showcase their work. To celebrate their efforts, the club hosted a competition recognizing the best overall poster in each project category. The winners are as follows:
De-Americanization of Binomial Options: Mengyuan Li, Tomiris Tulegenova, Advay Sapra, Neel Roy
Machine Learning for Options Pricing: Nick Clarisse, Ben Kazinsky
Large Language Models in Finance: Matthew C. Pianfetti, Dean Paganis, Thomas Kang, Elias Badway, Rahil Mittal