Greetings! I'm Zhi-ning LIU (刘芷宁) 🍻

你好! / Hello! / 안녕하세요! / こんにちは! / Здравствуй! / Bonjour! / Guten Tag! / Hola! / Ciao! / السلام عليكم!

I’m a Ph.D. candidate at University of Illinois Urbana-Champaign (UIUC), working with the very nice Prof. Hanghang Tong. Before joining UIUC, I received my B.S. and M.Eng. in Computer Science from Jilin University in 2019 and 2022, where I was fortunate to have Prof. Yi Chang as my advisor. I also worked as a research/applied scientist intern at Amazon and Microsoft Research.

I enjoy doing research (GoogleScholar) and developing open-source softwares (GitHub) for Data-centric Trustworthy Machine Learning, my interests include but not limited to:

Large Language Model Factuality (ACL’25)
Graph & Time Series Data Mining (ICML’24, ICML’25)
Fairness-aware ML (KDD’24, FAccT’24)
Class-imbalanced Learning (ICML’24, ICDE’20)
Meta Ensemble Learning (ICML’25, NeurIPS’20)

📧 Contact me via:
Mail: zhining.liu[AT]outlook.com or liu326[AT]illinois.edu

🌈 What's new:

[05/2025] 🏆 Award: Honored to receive the C.L. and Jane Liu Award!
[05/2025] ACL'25: How to prevent LLM from overlooking the evidence in your RAG? [Code/PDF]
[05/2025] ICML'25: Sample-level adaptive model fusion for time series forecasting. [Code/PDF]
[05/2025] 🧑‍💻 Intern@Amazon: Back to Bay Area again🌴!
[01/2025] ICLR'25: One co-author paper on TTA for Graph Structural Shift accepted :) [PDF]
[09/2024] NeurIPS'24 Spotlight: One co-author paper on Time Series Backdoor Attack accepted. [Code/PDF]
[05/2024] KDD'24: AIM: Attributing, Interpreting, Mitigating Data-encoded Unfairness. [Code/PDF]
[05/2024] 🧑‍💻 Intern@Amazon: Starting my Applied Scientist Internship at BayArea!
[04/2024] ICML'24: Class-Imbalanced Graph Learning without Class Rebalancing? [Code/PDF]
[03/2024] FAccT'24: Group Fairness via Group Consensus (with Eunice Chan). [PDF]
[05/2023] 🍻 KDD'23: Web-based Long-term Spine Treatment Outcome Forecasting (with Hangting Ye). [PDF]
[05/2023] 🧑‍💻 Intern@Amazon: Starting my Applied Scientist Internship at Seattle!
[02/2023] 💾 Open-source: Major release of IMBENS - imbalanced learning toolbox [Github/Docs/PyPI]
[02/2023] 🍻ICDE'23: UADB: Unsupervised Anomaly Detection Booster (with Hangting Ye). [Code/PDF]
[03/2022] 🎓 Starting Ph.D.@UIUC: I will join Prof. Hanghang Tong's group at UIUC in 2022 Fall!
[01/2022] 💾 Open-source: Awesome2ML - a curated list across all Machine Learning topics. [ENG/中文]
[11/2021] 📜 Preprint: IMBENS: Ensemble Class-imbalanced Learning in Python [Code/PDF]
[06/2021] 💾 Open-source: IMBENS - imbalanced learning toolbox [Github/Docs/PyPI]
[10/2020] NeurIPS'20: MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler [Code/PDF]
[04/2020] 💾 Open-source: Awesome-Imbalanced-Learning - a curated list of imbalanced learning resources.
[10/2019] 🍻 ICDE'20: Self-paced Ensemble for Highly Imbalanced Massive Data Classification [Code/PDF]
[07/2019] 🎓 Graduation@JilinU: Recieved my B.Sc. from Tang Aoqing Honors Program in Science, Jilin University.
[09/2018] 🧑‍💻 Intern@Microsoft: Starting my internship at Microsoft Research Asia! Supervisor: Dr. Jiang BIAN, Dr. Wei CAO.

🎓 Featured Research

Please check up-to-date list on GoogleScholar.

	"SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence" Zhining Liu, et al. In ACL'25. [PDF] [Github] [GoogleSlides] [Poster] [Video] [Zhihu/知乎]
	"Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting" Zhining Liu, et al. In ICML'25. [PDF] [Github] [GoogleSlides] [Poster]
	"Class-Imbalanced Graph Learning without Class Rebalancing" Zhining Liu, et al. In ICML'24. [PDF] [arXiv] [Zhihu/知乎] [Github]
	"IMBENS: Ensemble Class-imbalanced Learning in Python" Zhining Liu, et al. Open-source Python Package. [PDF] [arXiv] [Zhihu/知乎] [Github] [Documentation] [Example Gallery] [PyPI]
	"MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler" Zhining Liu, et al. In NeurIPS'20. [PDF] [arXiv] [Video] [Zhihu/知乎] [Github]
	"Self-paced Ensemble for Highly Imbalanced Massive Data Classification" Zhining Liu, et al. In IEEE ICDE'20. [PDF] [arXiv] [Video(bilibili)] [Slides] [Zhihu/知乎] [Github] [PyPI]

💾 Featured Open-source Projects (Github)

	IMBENS: class-imbalanced ensemble learning in Python [Python Library] [PDF] [Documentation] [Gallery] [PyPI] [Changelog] [Zhihu/知乎]
	Imbalanced Learning: paper, code, frameworks, and libraries [Awesome] [English] [Chinese/中文] [Zhihu/知乎]
	Awesome2ML: a curated list across all machine learning topics [Awesome] [English] [Chinese/中文] [Zhihu/知乎]
	MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler [NeurIPS'20] [PDF] [arXiv] [Video] [Zhihu/知乎]
	Self-paced Ensemble for Highly Imbalanced Massive Data Classification [ICDE'20] [PDF] [arXiv] [Video] [Slides] [Zhihu/知乎]

📈 My Github Stats

🕺 Fun Facts

💃 In the Chinese context, my name “Zhi Ning” (芷: a type of vanilla; 宁: peace & tranquil) has some feminine overtones. As a result, many of my friends thought I was a cute girl before meeting me (and were a little disappointed when we finally met).
🎮 I enjoy (but am not necessarily good at) playing almost all kinds of video games, shooters, strategy, 4X, RPG, roguelike, etc. To name some of my favorites: Battlefield, Civilization, Stellaris, GTA, The Witcher, DIRT, Homeworld, Metro, Bioshock, Borderlands, etc.
🎨 Making things that look nice and satisfying (like this page and illustrations in my papers) makes me happy. Drawing was also one of my favorite thing to do before my 20s. I’m probably better suited to being a designer than doing computer science LOL.

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Zhining Liu