I’m Xiaolin Hu, pursuing Ph.D. at Gaoling School of Artificial Intelligence , Renmin University of China. I am fortunate to be advised by Prof. Yong Liu. Previously, I received my B.E. and M.E. degrees in communication and information system from Shanghai University in 2018 and 2021, respectively. I worked with Prof. Nicholas E. Buris in the Intelligent Multi-Input Multi-Output Systems(i-MIMOs) research group from 2018 to 2021. I was an Intern Researcher at OPPO Research Institute from October 2020 to February 2021.

I am currently bridging large language models(LLMs) and the next-generation of intelligent personal computing platforms. Toward my vision of building a human-centered application ecosystem based on the fundation models, I am broadly interested in the following topics:

(1) Science Driven Tuning: to pursue scientific principles behind optimization of LLMs and use them to guide human-centerd personal model development.

(2) LLMs for Edge Devices: to study efficient method for deploying LLMs on edge devices and the communication challenges arising from large-scale agents.

I won the Shanghai University President Scholarship (The highest honor among the scholarships at Shanghai University).

🔥 News

📝 Publications

🎙 Generalization

ICLR 2023

Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses
Xiaolin Hu, Shaojie Li, Yong Liu


  • This paper provides a theoretical analysis of generalization error of federated learning.
  • We assume that the heterogeneous clients are sampled from a meta-distribution. In this framework, we characterize the generalization error for unparticipating clients.
  • We further derive convergence bounds for heavy-tail losses.

🧬 AI+Science

APMC 2020

A Deep Learning Framework for Solving Rectangular Waveguide Problems
Xiaolin Hu, Nicholas E. Buri, APMC 2020 (Oral) |


  • We employ Physics Informed Neural Networks (PINNs) to solve rectangular waveguide problems.
  • We successfully apply PINNs to the task of solving electric and magnetic fields, which can be described by partial differential equations (PDEs).
  • We also show the applicability of the framework for predicting the unknown parameters such as wavenumber.

🧑‍🎨 Generative Model

🚍 Others

🎖 Honors and Awards

  • 2022.10 First-class Scholarship, Renmin University of China, Beijing, China
  • 2021.10 Second-class Scholarship, Renmin University of China, Beijing, China
  • 2019.12 Second Prize, China Post-graduate Mathematical Contest in Modeling, China
  • 2019.12 Third Prize in Shanghai, China Graduate Electronics Design Contest, Shanghai, China
  • 2018.07 Provincial Outstanding Graduates, Shanghai, China. (top 5% of graduating students)
  • 2018.07 President Scholarship, Shanghai University. (top 15 of 4900 graduating students)
  • 2017.11 First prize in Shanghai, National Undergraduate Electronics Design Contest, Shanghai, China

📖 Educations

  • 2021.09 - Present, Ph.D. in Artificial Intelligence, Renmin University of China, Beijing.
  • 2018.09 - 2021.07, M.S. in Communication and Information System, Shanghai Univeristy, Shanghai.
  • 2014.09 - 2018.07, B.S. in Communication Engineering, Shanghai Univeristy, Shanghai.

💻 Internships