RGRam Goel
Portland, Oregon, United States · engineerRam Goel is a quantitative researcher and mathematician. He represented the United States at the International Mathematical Olympiad (IMO) 2022 in Oslo, earning a Bronze Medal (25 points) as part of a USA team that finished third overall, competing while a twelfth-grader at Krishna Homeschool in Oregon. His research project 'Products of reflections in smooth Bruhat intervals', mentored by Christian Gaetz through MIT PRIMES, won the 2022 Regeneron ISEF 3rd Place Grand Award in Mathematics and a Regeneron STS 2022 scholarship. He enrolled at MIT in 2022, studying Mathematics and Computer Science, and has since worked on machine learning research - including fast LLM inference (KV cache compression) at Together AI and diffusion models for robot behavior synthesis at MIT CSAIL. As of 2026 he is a Quantitative Research Analyst in Citadel's Global Quantitative Strategies group, following a 2025 internship there.
- Represented the USA at IMO 2022 in Oslo, earning a Bronze Medal with 25 points (rank 230 overall, 61.1%); team placed 3rd. Notably scored full marks on P1, P2 and P4 but zero on P3 and P6.Jul 2022
- Now a Quantitative Research Analyst in Citadel's Global Quantitative Strategies (GQS) group (2026-), after a 2025 GQS internship in Chicago - a direct pipeline from elite STEM research into quantitative finance.2026
- Research project 'Products of reflections in smooth Bruhat intervals' under PRIMES mentor Christian Gaetz earned the 2022 Regeneron ISEF 3rd Place Grand Award in Mathematics and a Regeneron STS 2022 scholarship.2022
- ML research background spans a 2024 Together AI internship on fast LLM inference (KV cache compression) and MIT CSAIL Learning and Intelligent Systems work (2022-2023) on diffusion models for robot behavior synthesis.2024
- Twelfth-grader at Krishna Homeschool, Oregon when he made the 2022 US IMO team, an unusual non-traditional-schooling route to the national team.Jul 2022
Global Quantitative Strategies (GQS).
Statistical Inference, Fundamentals of Programming. Led recitation sections and office hours.
Researched methods for fast LLM inference, primarily KV cache compression.
Learning and Intelligent Systems (LIS) Group at CSAIL. Worked on developing diffusion models for scalable behavior synthesis for robots.
Global Quantitative Strategies (GQS).
Competition record
United States of America · IMO