Vikalp Thukral. Quantitative researcher working at the intersection of derivatives, stochastic modeling, and real‑time market data.

I came to quantitative finance through an unusual door — six years of large‑scale data and product engineering at American Express, where I owned the Enterprise Data Warehouse and rebuilt legacy PySpark pipelines that cut overnight runtimes from 24 hours to under four. That foundation in systems and scale shaped how I think about models: they only matter if they're fast, reproducible, and usable by someone other than the author.

At UCLA Anderson MFE I focused on the rigorous side of that equation — stochastic calculus, derivatives pricing, computational methods, and econometrics — and I'm now a Quant Research Analyst at UCLA Anderson, developing Python‑based quant research tooling and risk analytics modules with agentic AI (Claude Code) to accelerate calibration and backtesting of derivative pricing models. I'm also running systematic alpha research on the WorldQuant BRAIN platform. In 2025 my team placed Top 4 of 30 in the IAQF International Student Competition, modelling bubble dynamics with GARCH‑DCC and LPPLS.

Outside of coursework I ship things: an in‑house options pricing library (binomial, trinomial, Crank‑Nicolson PDE solvers, variance‑reduced Monte Carlo), a Fama‑French five‑factor replication suite, a cash‑flow waterfall engine for securitized loans, and a live 3D volatility surface you can visit below. I care about the craft of making quantitative research legible.