Zhuoyan Xu Ph.D. candidate Department of Statistics, Computer Science University of Wisconsin-Madison, US Contact: zhuoyan.xu [at] wisc [dot] edu Office 5388, Department of Computer Sciences, UW-Madison Google Scholar | Github | LinkedIn | CV | X (Twitter)

About Me

I am a Ph.D. candidate at the University of Wisconsin-Madison co-advised by Prof. Yingyu Liang , Prof. Yin Li and Prof. Yiqiao Zhong . I am also fortunate to work with Prof. Kris Sankaran . I obtained my M.S. degree in Computer Science from UW-Madison. Prior to that, I obtained my B.S. degree in Statistics from Wuhan University in 2019.

My research focuses on the learning and adaptation of Foundation Model, including Large Language Models and Multimodal Models, with the goal of improving their capabilities and deployment efficiency.

I am/was an Applied Scientist Intern in summer 2025/2024 at Amazon AWS AI, Bellevue. I was a Machine Learning Engineer intern in John Deere Intelligent Solutions Group in summer 2022. I also had an internship at China Merchants Bank, Shanghai.

News

• [6/25/2025]: One paper got accepted into ICCV 2025. Thanks to all the collaborators! See you in Hawaii.
• [5/19/2025]: Join Amazon as a applied scientist intern. Work on Agentic AI.
• [12/17/2024]: Passed my prelim exam, PhD candidate now!

Publications

* denotes equal contribution or alphabetical order.

	AdaLLaVA: Learning to Inference Adaptively for Multimodal Large Language Models Zhuoyan Xu*, Khoi Duc Nguyen*, Preeti Mukherjee, Saurabh Bagchi, Somali Chaterji, Yingyu Liang, Yin Li ICCV 2025 [ Website ] [ Paper ] [ Code ] [ HuggingFace ]
	Out-of-distribution generalization via composition: a lens through induction heads in Transformers Jiajun Song, Zhuoyan Xu, Yiqiao Zhong PNAS (Proceedings of the National Academy of Sciences) 2025 [ PNAS ] [ arXiv ] [ Code ] [ Blog ] [ Poster ]
	TabRAG: Efficient Table Retrieval and Understanding with Large Multimodal Models Zhuoyan Xu, Haoyang Fang, Boran Han, Bonan Min, Bernie Wang, Shuai Zhang Work done during internship at AWS [ abstract ]
	Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers Yingyu Liang*, Heshan Liu*, Zhenmei Shi*, Zhao Song*, Zhuoyan Xu*, Junze Yin* arXiv, 2024 [ arXiv ]
	Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability Zhuoyan Xu*, Zhenmei Shi*, Yingyu Liang COLM (Conference on Language Modeling) 2024 [ OpenReview ] [ arXiv ] [ Code ] [ Poster ] [ Workshop ] [ Workshop Poster ] [ Workshop Slides ]
	AdaInf: Adaptive Inference for Resource-Constrained Foundation Models Zhuoyan Xu, Khoi Duc Nguyen, Preeti Mukherjee, Somali Chaterji, Yingyu Liang, Yin Li ICML 2024 Workshop [ OpenReview ] [ Poster ]
	Why Larger Language Models Do In-context Learning Differently? Zhenmei Shi, Junyi Wei, Zhuoyan Xu, Yingyu Liang ICML 2024 [ Openreview ] [ arXiv ] [ Poster ] [ Workshop ] [ Workshop Poster ]
	Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning Zhuoyan Xu, Zhenmei Shi, Junyi Wei, Fangzhou Mu, Yin Li, Yingyu Liang ICLR 2024 [ OpenReview ] [ arXiv ] [ Code ] [ IBM Research Talk Slides ] [ Poster ] [ Video ] [ Workshop ] [ Workshop Poster ] [ Workshop Slides ]
	Spatial Transcriptomics Dimensionality Reduction using Wavelet Bases Zhuoyan Xu, Kris Sankaran F1000, 2022 [ F1000 Research ] [ arXiv ] [ Rpackage ] [ code ]
	Generalized Tensor Regression with Covariates on Multiple Modes Zhuoyan Xu, Jiaxin Hu, Miaoyan Wang arXiv, 2019 [ arXiv ] [ Rpackage ]

Research & Work Experience

	Research Assistant University of Wisconsin-Madison 2022 - Now | Prof. Yingyu Liang and Prof. Yin Li 2023 - Now | Prof. Yiqiao Zhong 2021 - 2022 | Prof. Kris Sankaran
	Applied Scientist Intern Amazon AWS AI in Bellevue, WA Summer 2025 | Debanjan Datta , Guru Nayak and Mukul Prasad Summer 2024 | Shuai Zhang , Boran Han and Haoyang Fang
	Machine Learning Engineer Intern John Deere in Fargo, ND Summer 2022 | Lav Thyagarrajan
	Data Scientist Intern China Merchants Bank in Shanghai, China Summer 2018

Academic Services

Conference Reviewer at ICML 2024-2025, NeurIPS 2024, ICLR 2025, AISTATS 2025, CVPR 2025, ICCV 2025

Teaching

Teaching Assistant:
STAT 371: Introductory Applied Statistics for the Life Sciences (20 Fall)
STAT 301: Introduction to Statistical Methods (21 Spring, 21 Summer)
STAT 303-305: R for Statistics (21 Fall)
STAT 479: Statistical Data Visualization (22 Spring)

Instructor of VISP Non-credit program:
Intro to Big Data (Jan. to Feb. in 2020) [ Slides1 ,  Slides2 ]

Misc

Prelim: [ Document ,  Slides ] This page has been accessed times since Jan 20, 2024
Last updated: Aug 2, 2025