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Xiaotian Han

Large Language Models (LLMs) LLM Agents With Planning And Reasoning Model Architecture Analysis And Alignment Graph Foundation Models AI For Science (AI4Science)
Case Western Reserve University Computer and Data Sciences

About

Xiaotian Han is a faculty member in the Department of Computer and Data Sciences at Case Western Reserve University and leads the Machine Learning Research Group. His research focuses on machine learning, large language models, and AI for science, with an emphasis on foundation models and their applications. His work includes efficient large language models, understanding model architectures and alignment, developing LLM agents with enhanced planning and reasoning capabilities, and AI4Science through graph foundation models.

Research Performance Summary

53
Total Papers
3997
Total Citations
21
H-Index
2018-2026
Active Research Span

First Recorded Paper

Aspect-level deep collaborative filtering via heterogeneous information networks.

Year: 2018

Citations: 113

Venue: IJCAI 18

Latest Recorded Paper

Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers

Year: 2026

Citations: 0

Venue: arXiv preprint arXiv:2601.07036

Last 10 Years Publication Activity

This timeline shows the professor's yearly publication activity.

2026
1
2025
18
2024
10
2023
9
2022
7
2020
3
2019
2
2018
3

Publication Venues and Collaboration

Journal, Conference, and Book Publication Breakdown

Conference 17
Journal 4
Book 0

Top Coauthors

Han Wang Liu

Research Impact by Period

2025-2026

Period Stats
Papers 19
Citations 63
Avg. Citations / Paper 3.3
H-Index 4

2020-2024

Period Stats
Papers 29
Citations 3144
Avg. Citations / Paper 108.4
H-Index 18

2015-2019

Period Stats
Papers 5
Citations 790
Avg. Citations / Paper 158
H-Index 5

Contact and Professional Links

Contact Information

xhan@case.edu

Detected Research Keywords

Fair Graph Message Graph Message Passing Graph Neural Network Anomalous Trajectory Detection Fmp Fair Graph Message Passing Against Passing Against Topology Against Topology Bias Long Context Ability