Founder-engineer building reliable control systems for financial AI.
Co-founder of TheMindOverMarket · Cornell Tech CS '26 · New York City

Website · LinkedIn · TheMindOverMarket

Build systems that make the right action easier than the impulsive one.

The policy layer for AI trading agents. TMOM evaluates structured order intent against explicit strategy contracts, account state, and market state before execution—returning deterministic decisions such as allow, warn, justify, throttle, or block. The goal is not another trading brain; it is reliable control infrastructure for high-stakes financial AI.

TMOM was a finalist in the 2026 Cornell Tech Startup Awards.

• WebApp Study: Evaluating AI-Native Web Application Builders — Deccan AI, June 2025
• Anthar Study: Elevating AI Coding Agents Beyond Benchmarks — Deccan AI, May 2025

Peer reviewer for IEEE, with reviews of AI-focused research for IEEE INDISCON 2025, CVMI 2025, iSSSC 2025, and INSTcon 2026.

• Prefer explicit constraints, measurable behavior, and tight feedback loops.
• Separate intelligence from control: capable agents still need enforceable boundaries.
• Turn decisions into durable artifacts—versions, evidence, reason graphs, and replayable state.
• Ship the smallest real system, learn from users, and compound from there.

Agentic systems · AI safety and evaluation · market microstructure · risk systems · developer tooling · behavioral finance

I am a computer science graduate student at Cornell Tech and a founder working at the intersection of AI agents, financial systems, and operational reliability. I also contribute to research and evaluation work on AI-native software systems.

If you are building serious infrastructure for agents or finance, I would enjoy comparing notes.