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 Department of Computer Science, University of Illinois at Urbana-Champaign, working with the very nice Prof. Hanghang Tong. Before joining UIUC, I received both 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 was also a research intern at MLGroup@Microsoft Research Asia in 2018 and an Applied Scientist Intern at SearchScience@Amazon in 2023 and 2024.
I enjoy doing research and developing open-source softwares for unbiased, efficient, and robust machine learning from skewed data in real-world applications. My recent interest lies in graph data mining (ICML’24), class-imbalanced learning (ICML’24,NeurIPS’20,ICDE’20), and fairness-aware machine learning (KDD’24, FAccT’24).
Contact me via:
📧 Mail: zhining.liu[AT]outlook.com or liu326[AT]illinois.edu
🌈 What's new:
- [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 Items (GoogleScholar)
"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 1 & 3, Civilization VI, Stellaris, GTA 4 & 5, The Witcher 3, DIRT 4, Homeworld 2, Metro 2033, 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.