Xuan Shen

By the Sea of New England

I am a Ph.D. student in ECE Department of Northeastern University at Boston, advised by Yanzhi Wang. Previously, I received my M.S. degree at Northeastern University in 2020 and my B.S. degree at Nanjing University of Science and Technology in 2018.

My research interest is Efficient AI including pruning, quantization, NAS, and distillation with Software Hardware Co-Design on Mobile, FGPA, and ASIC.

I work closely with Jiuxiang Gu, Prof. Pu Zhao and Prof. Wei Niu. I was fortunate to work with Ming Lin.

As a final-year Ph.D. candidate, I am actively pursuing post-doctoral and full-time research opportunities. I would welcome the chance to connect and discuss potential collaborations if our research interests align.

News

Mar 06, 2025	Our paper accepted in ICLR 2025 SCI-FM Workshop.
Feb 26, 2025	Got one paper accepted in CVPR 2025.
Feb 02, 2025	Release efficient reasoning work with paper and code.
Jan 23, 2025	Release the code of LazyDiT.
Jan 22, 2025	Got one paper accepted in ICLR 2025.
Dec 09, 2024	Got Adobe Reward: 2024 Key Innovations (Tech Transfer Small LLM on Acrobat).
Dec 09, 2024	Got three papers accepted in AAAI 2025.
Nov 19, 2024	Multimodal Opioid Benchmark released on HuggingFace: opioidarchive/oida-qa.
Oct 30, 2024	Our paper about PTQ of LLMs on Mobile and FPGA has been accepted to TCAD.
Sep 25, 2024	Got two papers accepted in NeurIPS 2024.

Selected Publications

CVPR

QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on the Edge

Xuan Shen, Weize Ma, Jing Liu, Changdi Yang, Rui Ding, Quanyi Wang, Henghui Ding, Wei Niu, Yanzhi Wang, Pu Zhao^†, Jun Lin^†, and Jiuxiang Gu^†

Conference on Computer Vision and Pattern Recognition, 2025

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ICLR

Sparse Learning for State Space Models on Mobile

Xuan Shen^*, Hangyu Zheng^*, Yifan Gong, Zhenglun Kong, Changdi Yang, Zheng Zhan, Yushu Wu, Xue Lin, Yanzhi Wang, Pu Zhao, and Wei Niu^†

International Conference on Learning Representations, 2025

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AAAI

Numerical Pruning for Efficient Autoregressive Models

Xuan Shen, Zhao Song, Yufa Zhou, Bo Chen, Jing Liu, Ruiyi Zhang, Ryan A Rossi, Hao Tan, Tong Yu, Xiang Chen, Yufan Zhou, Tong Sun, Pu Zhao, Yanzhi Wang^†, and Jiuxiang Gu^†

Association for the Advancement of Artificial Intelligence, 2025

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AAAI

LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers

Xuan Shen, Zhao Song, Yufa Zhou, Bo Chen, Yanyu Li, Yifan Gong, Kai Zhang, Hao Tan, Jason Kuen, Henghui Ding, Zhihao Shu, Wei Niu, Pu Zhao, Yanzhi Wang^†, and Jiuxiang Gu^†

Association for the Advancement of Artificial Intelligence, 2025

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NeurIPS

Search for Efficient Large Language Models

Xuan Shen, Pu Zhao, Yifan Gong, Zhenglun Kong, Zheng Zhan, Yushu Wu, Ming Lin, Chao Wu, Xue Lin, and Yanzhi Wang^†

Conference on Neural Information Processing Systems, 2024

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TCAD

HotaQ: Hardware Oriented Token Adaptive Quantization for Large Language Models

Xuan Shen, Zhaoyang Han, Lei Lu, Zhenglun Kong, Peiyan Dong, Zhengang Li, Yanyue Xie, Chao Wu, Miriam Leeser, Pu Zhao, Xue Lin, and Yanzhi Wang^†

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024

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AAAI

Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge

Xuan Shen^*, Peiyan Dong^*, Lei Lu, Zhenglun Kong, Zhengang Li, Ming Lin, Chao Wu, and Yanzhi Wang^†

Association for the Advancement of Artificial Intelligence, 2024

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CVPR

DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network

Xuan Shen^*, Yaohua Wang^*, Ming Lin, Yilun Huang, Hao Tang, Xiuyu Sun, and Yanzhi Wang^†

Conference on Computer Vision and Pattern Recognition, 2023

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IJCAI

Data Level Lottery Ticket Hypothesis for Vision Transformers

Xuan Shen, Zhenglun Kong, Minghai Qin, Peiyan Dong, Geng Yuan, Xin Meng, Hao Tang, Xiaolong Ma, and Yanzhi Wang^†

International Joint Conference on Artificial Intelligence, 2023

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