Mateo Bodon
Yale University
Yale University
Available tomorrow
Starting at 2:00 PM UTC
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I’m a Yale student studying Applied Mathematics, Computer Science, and Economics, with experience across machine learning, quantitative research, software engineering, and academic research. At Yale, I conduct research in the Department of Statistics & Data Science on portfolio risk and covariance estimation. I have also worked in quantitative research at Eclipse Trading in Hong Kong and GBM in Mexico City, and as a Yale STARS research fellow building a large-scale GPU and remote-sensing pipeline. My technical work has required me to read academic literature, design rigorous experiments, build and audit code, validate results, and explain complicated ideas clearly. I earned a 1570 SAT with an 800 in Math, have a 4.00 major GPA at Yale, won first place in the YUHA × Kalshi Market Research & Modeling Competition, and was named a Coca-Cola Scholarship Semifinalist and National Merit Scholar. I work best with ambitious students applying to selective computer science, AI, mathematics, data science, and engineering programs—particularly students who already have substantive research, internships, or technical projects but need help presenting them effectively. I can audit research abstracts and papers, review GitHub repositories and READMEs, sharpen technical resumes and activity descriptions, and shape CS supplements so the student—not merely the project—comes through. My philosophy is simple: technical sophistication matters, but intellectual ownership is the real differentiator. A compelling application makes clear what the student actually contributed, why they made particular decisions, what failed, how their thinking evolved, and what they learned. I help students preserve technical accuracy while communicating their strongest work with clarity, credibility, and genuine substance—without inflating it or making it sound generic.
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Why do I coach?
I coach because I know how easily a strong student can be misunderstood on paper. As a first-generation student from a Title I public high school, I entered the admissions process without legacy connections, specialized counselors, or a built-in roadmap. I had to learn through research, experimentation, candid feedback, and repeated revision how selective applications are actually evaluated. That experience taught me that a successful application is not about manufacturing a perfect persona. It is about identifying the strongest evidence of curiosity, initiative, resilience, and growth—and communicating it in a way that feels specific and unmistakably true to the student. My coaching style is direct, analytical, and collaborative. I do not ghostwrite or turn students into polished but interchangeable applicants. I ask difficult questions, identify vague or unsupported claims, and help students develop the language to explain their own decisions and work. My goal is for each student to leave with stronger materials, a more coherent application, and a deeper sense of ownership over the story they are telling.
Work Experience
Quantitative Research Intern
Eclipse Trading
May 2026 - August 2026
Built research pipelines for more than 40 APAC equity options, studying volatility surfaces, relative-value signals, market microstructure, order flow, liquidity, and transaction costs. Developed Python and kdb+/q tools to test whether promising research signals remained viable under realistic execution conditions. The role strengthened my ability to evaluate technical projects not merely for apparent sophistication, but for methodological discipline, practical constraints, reproducibility, and defensible conclusions.
Quantitative Researcher
Yale University
January 2025 - Present
Conduct quantitative research with Professor Zhou Fan on covariance estimation, portfolio risk, and empirical validation using Python and large-scale financial datasets. Implemented and backtested a corrected covariance estimator that reduced out-of-sample estimation error and improved portfolio risk performance relative to established baselines. This work has strengthened my ability to assess research methodology, experimental design, statistical claims, code quality, and whether conclusions are genuinely supported by the evidence.
Research Fellow
Yale University
January 2025 - August 2025
Conducted machine-learning and remote-sensing research in Yale’s Seto Lab. Built a PyTorch and GPU-based pipeline using 23 years of Landsat and Sentinel satellite imagery to produce high-resolution environmental time series, improving predictive accuracy relative to established fusion methods. Worked across literature review, model development, large-scale data engineering, validation, research communication, and the challenge of turning an ambitious technical idea into a rigorous and reproducible project.
Education
Yale University
(Expected) Bachelor of Science - BS, Computer Science and Applied Math
2024 - 2028
Mateo was also personally admitted to
Princeton University