Jiawei Han

About

Jiawei Han is a professor at the Siebel School of Computing and Data Science within The Grainger College of Engineering at the University of Illinois Urbana-Champaign. He holds a Ph.D. in Computer Sciences from the University of Wisconsin-Madison, obtained in 1985. His research areas include Artificial Intelligence, Bioinformatics and Computational Biology, and Data and Information Systems. Han has contributed to the fields of data mining, information systems, and AI, with notable work recognized through various awards and honors. He is actively involved in teaching courses such as Data Mining Principles and Text Mining with Large Language Models, and has been recognized for his research and mentorship, with his students and colleagues receiving prominent awards at conferences like KDD.

Research topics

• Computer Science
• Artificial Intelligence
• Data Mining
• Machine Learning
• Data science
• Theoretical computer science
• Humanities
• Computer Security
• Biology
• Natural Language Processing

Selected publications

• Heterogeneous Network Representation Learning: A Unified Framework With Survey and Benchmark

  IEEE Transactions on Knowledge and Data Engineering · 2020 · 311 citations

  Senior authorCorresponding

  . from different sources, towards handy and fair evaluations of HNE algorithms. As the third contribution, we carefully refactor and amend the implementations and create friendly interfaces for 13 popular HNE algorithms, and provide all-around comparisons among them over multiple tasks and experimental settings. By putting all existing HNE algorithms under a unified framework, we aim to provide a universal reference and guideline for the understanding and development of HNE algorithms. Meanwhile…

  DOI

• Unsupervised Attributed Multiplex Network Embedding

  Proceedings of the AAAI Conference on Artificial Intelligence · 2020 · 271 citations

  Nodes in a multiplex network are connected by multiple types of relations. However, most existing network embedding methods assume that only a single type of relation exists between nodes. Even for those that consider the multiplexity of a network, they overlook node attributes, resort to node labels for training, and fail to model the global properties of a graph. We present a simple yet effective unsupervised network embedding method for attributed multiplex network called DMGI, inspired by De…

  DOI

• Text Classification Using Label Names Only: A Language Model Self-Training Approach

  2020 · 203 citations

  Senior authorCorresponding

  Current text classification methods typically require a good number of human-labeled documents as training data, which can be costly and difficult to obtain in real applications. Humans can perform classification without seeing any labeled examples but only based on a small set of words describing the categories to be classified. In this paper, we explore the potential of only using the label name of each class to train classification models on unlabeled data, without using any labeled documents…

  DOI

• COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

  2021 · 106 citations

  Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Ranran Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed ELsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human L…

  DOI

• A Data-Driven Graph Generative Model for Temporal Interaction Networks

  2020 · 97 citations

  Deep graph generative models have recently received a surge of attention due to its superiority of modeling realistic graphs in a variety of domains, including biology, chemistry, and social science. Despite the initial success, most, if not all, of the existing works are designed for static networks. Nonetheless, many realistic networks are intrinsically dynamic and presented as a collection of system logs (i.e., timestamped interactions/edges between entities), which pose a new research direct…

  DOI

Recent grants

• CPS: Small: Collaborative Research: Foundations of Cyber-Physical Networks

  NSF · $275k · 2009–2014

• DATA SCIENCE RESEARCH

  NIH · $20.8M · 2021

• III-Core:Small: MoveMine: Mining Sophisticated Patterns and Actionable Knowledge from Massive Moving Object Data

  NSF · $500k · 2010–2016

Frequent coauthors

• Xiang Ren

  98 shared

• Yizhou Sun

  71 shared

• Jingbo Shang

  69 shared

• Meng Yu

  66 shared

• Jiaming Shen

  60 shared

• Xifeng Yan

  Beijing Institute of Technology

  57 shared

• Chao Zhang

  56 shared

• Jian Pei

  Duke University

  54 shared

Labs

• Siebel School of Computing and Data SciencePI

Education

• Ph.D., Computer Science

  University of Wisconsin-Madison

  1986

• M.S., Computer Science

  University of Science and Technology of China

  1982

• B.S., Computer Science

  University of Science and Technology of China

  1980

Awards & honors

• 2025 ACM SIGKDD Rising Star Award
• 2025 ACM SIGKDD Dissertation Award, Runner-Up
• 2025 ACM SIGKDD Dissertation Award, Honorable Mention
• ACM SIGKDD 2024 Dissertation Award

Similar researchers at University of Illinois Urbana-Champaign

• Kaiyu Guanh-86
• ChengXiang Zhaih-83
• Jimeng Sunh-82