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Portfolio item number 1

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Portfolio item number 2

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Paper Title Number 1

Published in Journal 1, 2009

This paper is about the number 1. The number 2 is left for future work.

Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf

Paper Title Number 2

Published in Journal 1, 2010

This paper is about the number 2. The number 3 is left for future work.

Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf

Paper Title Number 3

Published in Journal 1, 2015

This paper is about the number 3. The number 4 is left for future work.

Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf

LayoutVLM: Differentiable Optimization of 3D Layout via Vision-Language Models

Published in CVPR, 2025

LayoutVLM combines vision-language model spatial planning with differentiable optimization to generate physically plausible 3D scenes from language instructions.

Recommended citation: Sun, F.-Y., Liu, W., Gu, S., Lim, D., Bhat, G., Tombari, F., Li, M., Haber, N., & Wu, J. (2025). "LayoutVLM: Differentiable Optimization of 3D Layout via Vision-Language Models." CVPR. https://arxiv.org/abs/2412.02193

Rethinking Reward Supervision: Rubric-Conditioned Self-Distillation

Published in COLM, 2026

Rubric-Conditioned Self-Distillation uses criterion-level rubrics to provide token-level guidance on a student model’s own sampled trajectories.

Recommended citation: Gu, S., Chen, J., Zhou, S., Cohan, A., & Ying, R. (2026). "Rethinking Reward Supervision: Rubric-Conditioned Self-Distillation." COLM. https://arxiv.org/abs/2606.19327

Ungraduate TA

Undergraduate course, Emory University, Department of Computer Science, 2021

TA for CS 253, Data Structure and Algorithm

Graduate TA

Graduate course, Stanford University, Department of Computer Science, 2024

Graduate TA for CS 224N: Natural Language Processing with Deep Learning