Additional Relevant Coursework: Quantitative Risk Management; Stochastic Models; Market Models;

Foundational Artificial Intelligence & Cybernetics; Linear Algebra; Multivariable Calculus; Differential Equations

• Grew revenue from $0 to $40K ARR, with software adopted by 75 enterprise users (40 daily active).

• Raised a six-figure funding round.

• Led a team of 4 spanning engineering and go-to-market as CTO.

• Owned and managed enterprise-grade infrastructure across AWS Bedrock and Azure.

• Founded and built Radar entirely solo, an AI-powered market intelligence platform for VC and PE, now deployed at enterprise scale. Built on Next.js, Python, Exa, PostgreSQL, Qdrant.

• Created a rich database of millions of companies and people, continuously updated through automated scraping pipelines.

• Engineered a cutting-edge search layer using LLMs and vector search to surface the most relevant results for any investment mandate.

• Built an automated monitoring engine that surfaces relevant developments to customers using rich firm context (investment thesis, portfolio, preferred stages, etc.).

• Built a Python OCR application that converts PDFs into a standardized, database-ready format, eliminating manual data entry.

• Built a multi-label classification model that predicts company sectors from scraped website text, improving filtering and fixing misclassifications in their database.

• Built a t-SNE-based similarity engine that ranks all database companies against an input company, accelerating analysts' search for acquisition targets.

• Agentic fundamental analysis through a spreadsheet workspace, where agents use verified SEC data on any publicly listed company to automate financial modeling. Built a web-based sheets engine from scratch. Rapidly unlocks intrinsic value and guides fundamental investors.

• AI-powered web interface manipulation platform enabling real-time DOM editing and natural language CSS/HTML modifications. Allows users to design their frontend without having to code, agents will do it for them.

• Adaptive reinforcement learning environment for medieval kingdom management, featuring a dynamic reward system that evolves based on how the player plays the game.

• 3D IV Surface generator that takes a stock ticker as input. The graph has strike price on the x-axis, DTE on the y-axis, and IV on the z-axis. Allows for configuration of the option type, a minimum & maximum strike, and if strike or moneyness is on the x-axis.

Technical Skills: Experienced with Python, Next.js, TypeScript, PostgreSQL, Docker;

Intermediate in R, Java, Go, Excel; Limited experience with C, C++, Ruby

Languages: Fluent in English, Dutch; Intermediate in Italian, Japanese

Hobbies & Interests: Chess, rock climbing, reading, hiking