معرفی
George H. Chen is an Associate Professor at Carnegie Mellon University, with dual affiliations in the Heinz College of Information Systems and Public Policy and the Machine Learning Department. His research focuses on trustworthy machine learning methods for temporal reasoning, particularly in health applications such as time-to-event prediction (survival analysis) and electronic health records analysis. He has extensive experience in nonparametric methods requiring minimal data assumptions.
Educational Background
- PhD in Electrical Engineering and Computer Science, MIT (2015)
- SM in Electrical Engineering and Computer Science, MIT (2012)
- BS in Electrical Engineering and Computer Sciences & Engineering Mathematics and Statistics, UC Berkeley (2010)
His work spans survival analysis, deep learning, and time series modeling, with applications in neurological prognostication, medical adherence, and health equity. He has developed self-contained educational resources including a 2024 monograph on deep survival analysis and tutorials at CHIL and SIGMETRICS. His 2025 course 95-865: Unstructured Data Analytics focuses on practical unstructured data analysis techniques.
Notable projects include advising the AgriTech startup CoolCrop, which provides cold storage and market forecasts for Indian farmers serving 9,000+ farmers across 7 states. His Google Scholar publications reveal a strong focus on temporal modeling in healthcare, with recent advancements in neural survival analysis and fairness-aware temporal prediction.
