Aboue me

I am a final-year Masters student at Brown University. My research interests are computer graphics, 3D computer vision, and deep learning. In particular, I aspire to make 3D generative models controllable, interpretable, and powerful. I am currently doing research with Brown Visual Computing group, co-supervised by Prof. Daniel Ritchie and Prof. Srinath Sridhar.

Previously, I obtained my bachelor’s degree from Tufts University. During my undergraduate years, I have been fortunate to work with Prof. Michael Hughes on generalized category discovery and Prof. Liping Liu on graph neural networks. I also had the opportunity to intern at Redwall-taihe Fund Management, Fidelity Investments, Platinum AI, and Foundation LLM.

Last Update: 12/30/2024

Publications

  • Specializing Small Language Models towards Complex Style Transfer via Latent Attribute Pre-Training
    Ruiqi Xu*, Yongfeng Huang*, Xin Chen, Lin Zhang. In Proceedings of ECAI 2023 [paper] [code]

    I worked on this project during my internship at Platinum AI, while I was taking a year off from undergraduate. TLDR: We propose a method to specialize small language models towards text style transfer tasks by training a latent attribute predictor (based on small T5 models) via contrastive learning.

Implementations

Every now and then, we come across papers without code. Here is a record of codeless papers I implemented for personal interest / research needs. Hopefully you can notice how my coding skills grow over time from these repos:)

  1. On Distillation of Guided Diffusion Models (43 stars) [paper] [code]
  2. Nuvo: Neural UV Mapping for Unruly 3D Representations (23 stars) [paper] [code]