Mathematical sciences student and actuarial analyst building ML pipelines and quantitative risk models, with a standing research interest in tail risk, random matrix theory, and applied econometrics on emerging markets.
I'm Raj Thakur, a final-year Bachelors in Mathematical Sciences student at Tribhuvan University, focused on actuarial science, quantitative modelling, and applied research. I'm a candidate with the Society of Actuaries (SOA), having cleared Exams P, FM, and FAM.
My work sits at the intersection of mathematics, risk, and applied machine learning: from classical probability and extreme value theory to LightGBM pipelines and econometric forecasting. I'm as interested in the open theoretical questions (tail dependence, random matrices, optimal transport) as I am in shipping something that runs in production. Most of what I build starts from the same question: what does a thin, noisy, emerging-market dataset actually let us claim?
Alongside the technical work, I place real value on clear writing, cross-departmental collaboration, and translating dense quantitative results into decisions non-specialists can act on.
I treat mathematics as a toolkit for open empirical questions, particularly where developing-economy data is thin, noisy, and under-studied. Below are papers currently in progress, and the theoretical areas I'm working toward next.
Benchmarking LASSO, Elastic Net, Random Forest, and Gradient Boosting against AR/VAR baselines for Nepal CPI, with SHAP-based explainability to keep the forecasts interpretable.
A logistic regression model in R examining how financial literacy shapes future healthcare and insurance decisions among young adults in Kathmandu, built on original survey data (n = 135).
Built a fully automated forecasting system for 19 NEPSE-listed commercial bank equities, from data acquisition through a deployed, publicly-queryable API, designed and shipped entirely on free-tier infrastructure.
Standard risk models assuming normally-distributed returns systematically underestimate the frequency and severity of extreme market crashes. This project applies Extreme Value Theory to the NEPSE index to produce tail-risk estimates that don't rely on that flawed normality assumption.
genparetoI am open to research collaborations, internships, and opportunities in actuarial science, data science, risk analysis, and AI. Please feel free to get in touch.