Yuxiang Fu

I am a MASc student in the ECE department at UBC, and a member in DSL lab, advised by Prof. Lele Wang and Prof. Renjie Liao.

Previously, I received an Honours degree in CS from UBC under the supervision of Prof. Andrew Roth. During my undergraduate studies, I am fortunate to intern at both the BCCRC Roth Lab and Vector Institute.

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• [2025/02] 🎉 One paper gets accepted to CVPR 2025.
• [2025/02] 🎉 One paper gets accepted to Robotics and Automation Letters 2025.
• [2025/01] I received the GSI award from UBC.
• [2024/04] 🎉 One paper gets accepted to CRV 2024 Workshop and it is selected as Oral Presentation.

I am interested in deep learning, generative AI, and self-driving, with a focus on the geometry and structure of diverse data modalities. My research seeks to elucidate underlying data patterns while leveraging their representations to enable robust, reliable, and efficient inference, bridging theoretical insights with practical applications. Some papers are highlighted.

We present a novel Motion prediction conditional Flow matching model, termed MoFlow, to generate K-shot future trajectories for all agents in a given scene. In addition, by leveraging the Implicit Maximum Likelihood Estimation (IMLE), we propose a novel distillation method for flow models that only requires samples from the teacher model.

We introduce TGD, a diffusion-based human trajectory generation model, along with a trainable student model leveraging the IMLE scheme to align with the teacher diffusion model’s distribution at any intermediate diffusion timestamp.

Accurate single-cell segmentation is essential for spatial omics analysis, yet existing methods rely heavily on expert-driven annotations and separate statistical or biological evaluation strategies, highlighting the need for a unified assessment approach. ESQmodel alleviates this limitation.

We aim to elucidate the fundamental nature of rotational impedance and presents a comprehensive, unified framework for formulating rotational impedance using Lie algebra and Noether's theorem. In particular, we utilized quaternions and rotation matrices to represent rotational motion within our proposed framework to ensure theoretical validity.

A slight tweak of the variational autoencoder that controls the primary tissue effect of the bulk RNA sequencing data from PCAWG/ICGC dataset. PCVAE offers the ability to uncover novel connections across heterogeneous cancers in a site-effect-free environment.

This webpage is build based on Jon Barron's website and deployed on Github Pages.

Last updated: Mar 28, 2025 © 2025 Yuxiang (Felix) Fu