I am a central banker and data scientist by passion, building AI-driven decision-support systems for financial supervision, macro-financial risk, and probabilistic forecasting. My work combines Bayesian methods, machine learning, causal inference, and stochastic modeling to support decisions under uncertainty.
An interactive research workbench that turns a plain-English trading idea into an assumption-aware causal diagnostic. Users choose the asset, period, confounders, estimators, and robustness settings; the app then compares the naive backtest with DAG-adjusted estimates, placebo tests, horizon sensitivity, and heterogeneous effects. The final output is a readable evidence report designed to separate a plausible signal from market-regime correlation—without presenting observational evidence as guaranteed alpha.
AI-powered macroeconomic forecasting and market-risk platform centered on a probabilistic Forecast Lab. It combines Monte Carlo simulation, Markov regime detection, historical analogue matching, and Bayesian reasoning to estimate a range of future scenarios rather than one fixed prediction, with supporting macro monitoring, cross-asset risk analytics, and news intelligence.
Probabilistic forecasting platform for Indian financial markets built around a Forecast Lab that estimates future scenarios using Monte Carlo simulation, Markov regime models, and historical analogue analysis. It complements this with Nifty 500 market breadth, liquidity, volatility, and systemic-risk monitoring.
Physics-informed deep learning framework for prostate-cancer risk stratification from MRI. A Variational Autoencoder learns clinically meaningful latent features while jointly reconstructing medical images and predicting PI-RADS scores, improving robustness and interpretability. Built at Uncommon Hacks 2026 and deployed on Hugging Face Spaces.
I am a central banker at the Reserve Bank of India and currently pursuing an MS in Applied Data Science at the University of Chicago. My work sits at the intersection of Bayesian statistics, machine learning, causal inference, and macro-financial risk.
I approach problems through a probabilistic and Bayesian lens, building models that explain uncertainty and support supervisory, policy, and risk-management decisions rather than simply producing point predictions.
My broader interests include stress testing, early-warning systems, time-series forecasting, reinforcement learning, and financial NLP.