Faculty

Hanghang Tong

Professor. Ph.D. in Machine Learning, CMU.
Large scale data mining and network computing.

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Jingrui He

Professor. Ph.D. in Machine Learning, CMU.
Heterogeneous ML, rare category analysis, active learning.

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Postdoc

Jun-Gi Jang

Ph.D in Computer Science and Engineering, Seoul National University.
Tensor and time series analysis.

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Ph.D. Student

Baoyu Jing

Spatial-temporal graphs and time series analysis.

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Lecheng Zheng

On faculty market!
Heterogeneous learning, multi-task learning and multi-view learning.

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Zhe Xu

Graph machine learning, heterogeneous networks, few-shot learning, and graph data augmentation.

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Yuchen Yan

Graph mining, recommendation, KG entity linking, KG reasoning, GNN, network alignment.

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Wenxuan Bao

On industry market!
Transfer learning and trustworthy federated learning.

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Ziwei Wu

On market!
Fairness-aware machine learning, trustworthy machine learning, and graph mining

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Tianxin Wei

Transferability and safety of machine learning algorithms across various modalities and disciplines.

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Yunzhe Qi

Contextual Bandits; Data Mining.

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Eunice Chee-Kay Chan

Fairness in the graph domain, explainable methods utilizing LLMs and graphs.

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Xinrui He

Graph Mining; Recommender System.

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Xinyu He

Graph dynamics.

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Isaac Raheem Joy

Adversarial Learning and Natural Language Processing models within the legal realm.

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Zhining Liu

Graph data mining, class-imbalanced learning, fairness-aware machine learning from skewed data.

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Hyunsik Yoo

Algorithmic fairness, robustness in graph mining/recommender systems, continual learning and temporal aspects.

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Xiao Lin

Graph machine learning, Time series analysis, Robustness/Fairness in machine learning.

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Zihao Li

Learning on Graphs; LLM-based Multi-agent Systems; Multi-modal Generative Models; AI Governance/Policy.

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Ruizhong Qiu

Optimization; Graph ML; Trustworthy ML.

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Zhichen Zeng

Graph machine learning, generative AI, and trustworthy machine learning.

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Mengting Ai

Efficient ML, especially focusing on LLM.

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Sirui Chen

Data-centric AI and interactive systems.

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Oliver Gaotang Li

Large language models and graph machine learning.

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Ting-Wei Li

Graph machine learning, trustworthy machine learning.

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Master Student

Jiaru Zou

NLP and reinforcement learning.

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Xuying Ning

Data mining and recommendation.

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Prospective Members

To help us identify your application, use '[PhD (or Postdoc, Undergrad Intern) Application]' to begin your email subject. Given the high volume of inquiries, we may not always be able to respond promptly. However, if you meet the qualifications described and haven't heard back within a week, feel free to send a follow-up with "[Application Follow-up]". Always remember to attach your CV and explain why we are a good fit for each other. Our Lab welcomes applicants from diverse backgrounds.

Postdoc Openings

Please directly reach out to Prof. Tong or Prof. He if you are interested in postdoc positions. We are always looking for postdocs with strong research backgrounds in machine learning, data mining, and related areas. Ideal candidates will have a strong publication record in top-tier conferences and journals, as well as demonstrated ability for leading research.

Ph.D. Student Openings

Our group typically hires 2-4 highly motivated Ph.D. students each year, depending on available funding. If you are interested in joining us, please apply to either the Computer Science or Information Science Ph.D. program at UIUC, mentioning Prof. Tong or Prof. He in your faculty of interest. Ideal candidates will have strong foundations in mathematics and programming, along with demonstrated potential for leading research. We also encourage applicants to consider research labs led by our alumni and to engage in early collaborations with our group members to ensure a good fit.

Thesis-track Master Student Openings

We welcome admitted master students from CS, ECE or IS programs at UIUC to do thesis research with us. However, there is no guarantee of research assistant funding. It is recommended if your thesis topic is closely related to one of our ongoing projects.

Undergraduate Research Opportunities

Undergraduate interns typically work closely with one Ph.D. student, either contributing to ongoing projects or leading a new research, depending on their career goals. We welcome students from all majors, but a strong background in computer science, mathematics, statistics, or related fields is required. If you're interested in joining us, please send your resume and a brief statement of interest to Prof. Tong or Prof. He. If you wish to work with a specific Ph.D. student, feel free to contact them directly. We will conduct a brief interview to assess your background and interests, and to clarify the expectations for the internship mutually.
Due to the high volume of applications from students at UIUC and other institutions, we would expect you to have a strong academic record (GPA ≥ 3.8/4.0 or other equivalent metrics) and a clear motivation for research. In general, interns who can commit to at least 6 months are preferred to ensure high-quality work. We may offer hourly pay for exceptional interns case by case (~$15 per hour).