Mo Minoneshan

I am an M.S. candidate in Financial Engineering at Columbia University, with a B.S. in Mathematics from the University of Texas at Austin.

My interests sit at the intersection of quantitative finance, machine learning, risk analytics, and investment research. I am especially focused on building investment-facing tools that turn complex market data into actionable insights — including portfolio analytics, stress testing, AI-powered research workflows, and systematic investment strategies.

My experience spans product analytics, quantitative research, and data science, including work at USAA, macro/ESG research at UT Austin's Salem Center, fintech data science, and projects involving multi-agent AI systems, copula-based tail risk modeling, Monte Carlo pricing, and inflation forecasting. This summer, I am working on industry projects with RBC Capital Markets and Lockwood Analytics, focusing on conditional stress-scenario generation and AI-based investment strategies.

Longer term, I am interested in hedge fund investing, systematic macro, AI-driven asset management, and entrepreneurial finance.

LinkedIn GitHub Projects Contact

education

M.S., Financial Engineering, Columbia University.
B.S., Mathematics, University of Texas at Austin.

experience

Quantitative Research (Industry Project), RBC Capital Markets, 2026.
AI Investment Strategies (Industry Project), Lockwood Analytics, 2026.
Macro / ESG Research, Salem Center, UT Austin.
Product Management Analyst Intern, USAA.

interests

Quantitative finance, systematic macro, and tail-risk modeling.
Machine learning and multi-agent AI systems for investment research.
Derivatives pricing, portfolio analytics, and stress testing.

contact

linkedin.com/in/minoneshan