Scholar

Jiaheng Wei

Google Scholar ID: gfB8UaIAAAAJ

Assistant Professor at Hong Kong University of Science and Technology (Guang Zhou)

Machine LearningLabel NoiseWeakly Supervised Learning

Citations & Impact

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Citations

867

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Publications

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Publications

20 items

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Resume (English only)

Academic Achievements

- Publications: Multiple papers accepted by top conferences such as KDD 2025, ICML 2025, ICLR 2025, AAAI 2025
- Projects: Involved in the Fortune Analytics Language Model (FALM) project, which powers fortune.com/analytics
- Tools: Developed docta.ai, a library to help users understand and curate their data

Research Experience

- 2024.12 - Present: Assistant Professor at Data Science and Analytics Thrust, Information Hub, Hong Kong University of Science and Technology (HKUST) – Guangzhou Campus
- 2024.04 - 2024.12: Advanced AI Research Scientist (Senior Manager) at Accenture
- 2023.08: Delivered a hands-on tutorial on learning with noisy labels at IJCAI 2023
- 2023.02 - 2023.08: Machine Learning (Research) Intern at ByteDance AI Lab
- 2022.12 - 2023.04: Research Fellow at CROSS

Education

- Ph.D. in Computer Science from the University of California, Santa Cruz, advised by Prof. Yang Liu, recipient of Jack Baskin and Peggy Downes-Baskin Fellowship 2023-2024
- M.S. in Data Science from Brown University
- B.S. in Honors Math & Honors Youth (Gifted Young) from Xi’an Jiaotong University

Background

- Research Interests: Trustworthy machine learning & large language models, including robust learning under real-world constraints (label errors in human-generated data, class-imbalanced learning, group distributional robustness), data alignment in large language models, incentive design for data collection, and generative modeling.
- Position: Tenure-track Assistant Professor at DSA, Hong Kong University of Science and Technology (Guang Zhou)
- Formerly: Advanced AI Research Scientist (Senior Manager) at Accenture
- Personal Summary: Committed to addressing practical issues in machine learning, particularly in data quality and model robustness.

Miscellany