Interests

• Statistical Learning Theory (StatML)
• Mathematical and Computational Finance (Quant)

Automating Black–Litterman Beliefs with Machine Learning

Replacing subjective investor views in Black–Litterman Portfolio Optimization with structured ML‑generated beliefs.

• Links Bayesian portfolio theory with prediction without discarding economic intuition
• Keeps the model transparent so you can see when the data is steering the ship
• Early results show portfolios that beat naïve baselines without hiding risk

Working paper (2024)

Illiquidity, Volatility Smoothing, and Portfolio Risk

Research at Harvard Business School. We study how illiquid assets distort volatility and expected returns, and how classical portfolio rules fail quietly when those frictions show up.

Killer Acquisitions and Innovation in FinTech

Research at Harvard Business School. Synthetic control regressions to predict innovation throughput pre- and post-merger or acquisition for similar firms.

Real Estate Pricing and Presale Option Risk

Research at IIM Bangalore. Structural models of presale housing markets, quantifying risk premia and how information asymmetry shapes urban real estate.