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I'm currently working at a startup working on ~intelligence~.
If you're interested in program synthesis, we are hiring!
I'm trying to answer the questions:
1. How are people and natural intelligences so data and compute efficient?
2. How do we use these insights to inform AI research?
3. How do we ensure advances in AI = advances for the average person?
(This is a placeholder for now)
How can we model people's flexible and efficient inductive reasoning ability? In my thesis, I propose two hypotheses: people may operate over an incredibly vast language which is made tractable via a strong bottom-up proposal model, or people may learn task-specific languages to reason over.
Dataset and analysis of crowd-sourced "language-complete" descriptions of 100s of tasks, as well as language-informed program synthesis results.
A very heuristic-heavy chess engine in C.
This website is mostly intended for academic / professional purposes, so here is my resume:
Master of Engineering, Computation and Cognition
May 2024 | GPA: 5.0/5.0
Bachelor of Science in Computer Science and Cognitive Science
May 2023 | GPA: 4.9/5.0
Relevant coursework: Algorithms, Signal Processing, Matrix Methods for Machine Learning, Deep Learning, Advanced Natural Language Processing, Tissue vs. Silicon: Differences in Machine Learning
Extracurriculars: MIT Track and Field and Cross Country (U.S. Track & Field and Cross Country Coaches Association Men's Scholar-Athlete of the Year 2022), MIT Class of 2023 Class Council (Vice President 2019-2020)
September 2024 – Present
Founding Research Engineer
May 2022 – August 2022
February 2022 – May 2022
June 2021 – September 2021
January 2021 – May 2021