Hi! I am Thomas (Hanwen) Zhu. I am currently a first-year MS in Machine Learning student at CMU, working on applying language models to solving mathematical problems. I am honored to be advised by Prof. Sean Welleck and Prof. Jeremy Avigad. My research interests more broadly include advancing reasoning abilities in machine learning models in a robust and scalable way.

I received BA Mathematics and Computer Science from Oxford, where I graduated top first and received the Gibbs Prize. I had the priviledge to work with Ruining Li and Tomas Jakab at VGG in applying diffusion models to 3D generation of human-object interactions. I also worked with a team at OxAI in developing a benchmark for gender bias in large vision-language models.

I am always excited to hear about potential collaborations or ideas. Please contact me at [email protected].

Publications

miniCTX: Neural Theorem Proving with (Long-)Contexts
Jiewen Hu, Thomas Zhu, Sean Welleck
Paper Dataset
ICLR 2025 (Oral)

DreamHOI: Subject-Driven Generation of 3D Human-Object Interactions with Diffusion Priors
Thomas Zhu, Ruining Li*, Tomas Jakab*
Website Paper GitHub
In preprint

VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution
Siobhan Mackenzie Hall, Fernanda Gonçalves Abrantes, Thomas Hanwen Zhu, Grace Sodunke, Aleksandar Shtedritski, Hannah Rose Kirk
Paper GitHub
NeurIPS Datasets and Benchmarks 2023