个人主页： https://wuyinjun-1993.github.io/
主要研究方向
机器学习中的数据管理问题，人工智能与数据库系统的融合，人工智能中的可解释性问题
博士研究论文“Towards the Efficient Use of Fine-Grained Provenance in Data Science Applications”获得宾夕法尼亚大学计算机系最佳博士论文奖。
在国际计算机会议和期刊上发表20余篇论文，包括：
数据库顶级会议SIGMOD （CCF-A, 2篇）,VLDB (CCF-A, 3篇)，系统顶级会议OOPSLA（CCF-A，1篇），人工智能顶级会议ICML（CCF-A, 2篇），AAAI（CCF-A,2篇）
主要学术任职
在多个CCF-A类期刊和会议上担任审稿人，包括：
ACM SIGMOD (CCF-A)
VLDB Journal (CCF-A)
ICDE (CCF-A)
Neurips (CCF-A)
AAAI (CCF-A)
EDBT (CCF-B)
Selected Publications
TorchQL: A Programming Framework for Integrity Constraints in Machine Learning (OOPSLA 2024)
Do Machine Learning Models Learn Statistical Rules Inferred from Data? (ICML 2023)
Learning to Select Pivotal samples for Meta Re-weighting (AAAI 2023)
Chef: a cheap and fast pipeline for iteratively cleaning label uncertainties (VLDB 2021)
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series (AAAI 2021)
Deltagrad: Rapid retraining of machine learning models (ICML 2020)
PrIU: A provenance-based approach for incrementally updating regression models (SIGMOD 2020)
ProvCite: A Provenance-based Citation System (VLDB 2019)
Data Citation: Giving Credit where Credit is Due (SIGMOD 2018)
Automating data citation in CiteDB (VLDB 2017)
