CV

Education

• Yale University, New Haven, CT, USA
  • Ph.D. in Statistics & Data Science
  • M.A. in Statistics & Data Science
  • Advisors: Jasjeet Sekhon (Co-Chair), Laura Forastiere (Co-Chair)
  • Thesis: Advances in Synthetic Data Generation and Causal Inference
• The University of Chicago, Chicago, IL, USA
  • M.S. in Statistics
• Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico
  • Lic. in Actuarial Science (9.82/10, 2nd best among 410 students)
  • Actuarial Exams (SoA): Construction and Evaluation of Actuarial Models (C), Financial Mathematics (FM), Probability (P)

Experience

• Applied Scientist, Amazon — April 2025 – present
• Applied Scientist Intern, Amazon — Summer 2024
• Quantitative Summer Associate, JPMorgan Chase Applied AI & ML — Summer 2023
• Data Summer Associate, Bitso — Summer 2021
• Financial Researcher, Banco de México — December 2019 – August 2020

Invited Talks

• Causal Inference in the Age of AI: Measuring Persuasion and Heterogeneous Effects
  Banco de México, Mexico City, Mexico, and Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS), Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico. February 2026.
• Uncovering Causal Relationships in Sports Analytics: Methods and Applications
  Connecticut Sports Analytics Symposium (CSAS), Yale University, New Haven, CT. April 2025.
• Enhancing Collaborative Medical Outcomes through Private Synthetic Hypercube Augmentation: PriSHA
  Eastern North American Region, International Biometric Society (ENAR) Spring Meeting, New Orleans, LA. March 2025.
• Privacy with Synthetic Data: Applications and Developments
  Heavy Tails in Machine Learning, Isaac Newton Institute at the Alan Turing Institute, Cambridge University, London, UK. April 2024.
• Estimating the age-conditioned average treatment effects curves: An application for assessing load-management strategies in the NBA
  UConn Sports Analytics Symposium (UCSAS), University of Connecticut, Storrs, CT. April 2024.

Conference Presentations

• AAAI Artificial Intelligence with Causal Technologies (AICT) Workshop, Pennsylvania Convention Center, Philadelphia, PA. March 2025. (Oral)
• AAAI AI for Social Impact: Bridging Innovations in Finance, Social Media, and Crime Prevention (AISI) Workshop, Pennsylvania Convention Center, Philadelphia, PA. March 2025. (Oral)
• Conference on Health, Inference, and Learning (CHIL), Cornell Tech, New York, NY. June 2024.
• AAAI Spring Symposia, Stanford University, Stanford, CA. March 2024. (Spotlight, top 10 submission)
• Machine Learning Summer School (MLSS), Okinawa Institute of Science and Technology (OIST), Okinawa, Japan. March 2024.
• International Conference on AI in Finance (ICAIF), MetroTech Center, Brooklyn, NY. November 2023. (Oral, top 20.5%)
• New England Symposium on Statistics in Sports (NESSIS), Harvard University, Cambridge, MA. September 2023. (Winner of the best poster award)
• Joint Statistical Meetings (JSM), Metro Toronto Convention Centre, Toronto, Ontario, Canada. August 2023. (Winner of the statistical significance award)
• American Causal Inference Conference (ACIC), Austin Marriott Downtown, Austin, TX. May 2023.
• UConn Sports Analytics Symposium (UCSAS), University of Connecticut, Storrs, CT. October 2022. (Winner of the best poster award)

Teaching

Yale University

• Teaching Fellow — Advances in Large Language Models: Theory and Applications (S&DS 617). Instructor: Jas Sekhon. Spring 2025.
• Teaching Fellow — Data Analysis (S&DS 361/661). Instructor: Brian Macdonald. Spring 2023.
• Teaching Fellow — Statistical Case Studies (S&DS 425). Instructor: Brian Macdonald. Fall 2022, Fall 2021.
• Teaching Fellow — Applied Machine Learning and Causal Inference (S&DS 317/517). Instructor: Jas Sekhon. Spring 2022.

Universidad Nacional Autónoma de México (UNAM)

• Adjunct Professor — Advanced Probability. Spring 2020.

The University of Chicago

• Workshop Instructor — Introduction to Deep Learning. Summer 2019.