The STEM-designated MSFE at UIUC fuses stochastic calculus, machine learning, and high-performance computing with market microstructure intuition. Classes meet on Chicago trading floors and in Siebel’s GPU lab where students back-test options strategies on terabytes of tick data. Partnerships with CME, Citadel, and Morningstar feed internships and trading-competition victories that launch quants into hedge funds, banks, and fintech disruptors.
Deep-hedging reinforcement-learning agent for exotic options
Rough volatility calibration using fractional Brownian motion
Explainable multi-factor ESG tilt strategy back-test
Order-book imbalance feature engineering for crypto market making
Stress-testing engine generating correlated fat-tail scenarios
GPU-accelerated Monte Carlo CVA computation with XVA desk inputs
Liquidity-adjusted risk model for sustainable bond portfolios
Zero-knowledge-proof settlement prototype for OTC swaps
LSTM yield-curve forecasting integrating macro sentiment
White paper on central-bank digital currency impact on repo markets
Graph analytics detecting collusive wash trading
Policy brief on AI accountability in retail robo-advisors
Quantum annealing pilot solving portfolio CVaR optimization
Open-source toolkit for carbon risk factor construction
Podcast debating latency races and market fairness
Model, code, and trade the future of finance with UIUC.
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