Scholar
Nate Gillman
Google Scholar ID: twg9zD0AAAAJ
PhD student, ML @ Brown University
Citations & Impact
Citations
85
H-index
4
i10-index
2
Publications
9
Co-authors
18
list available
Publications
Resume (English only)
Academic Achievements
- Published multiple papers in AI/ML, including 'Force Prompting: Video Generation Models Can Learn and Generalize Physics-based Control Signals' (NeurIPS 2025) and 'Fourier Head: Helping Large Language Models Learn Complex Probability Distributions' (ICLR 2025); holds a patent titled 'Methods and systems for automatically generating and executing computer code using a natural language description of a data manipulation to be performed on a data set' (U.S. Patent Application No. WO 2024/073098 A1); has publications in mathematics such as 'Large sets with small injective projections' (Annales Fennici Mathematici, 2021).
Research Experience
- Engaged in research on machine learning, computer vision, and AI during PhD studies; took a professional leave of absence after obtaining a master’s degree to gain exposure to ML in industry, interned at American Express AI Labs, Akkio (a no-code AI startup), and Captions (an AI video editing startup); did two summers of research with Ken Ono at Emory University's Research Experience for Undergraduates program as an undergraduate.
Education
- PhD student at Brown University's Department of Mathematics, advised by Chen Sun; previously conducted research in analytic number theory and cryptography under Jeff Hoffstein in the math department; received a master's degree in mathematics in spring 2022; undergraduate from Wesleyan University, participated in Math in Moscow and Budapest Semesters in Mathematics programs; undergraduate math research advisor was Ken Ono.
Background
- Research interests include machine learning, computer vision, and artificial intelligence. Current research focuses on video generative modeling and world modeling, enjoys training deep generative models that approximate real-world physics.
Miscellany
- Inspired by the life of Walter Pitts, who proposed the first mathematical model of the neural network.
