PreDefense: Defending Underserved AI Students and Researchers from Predatory Conferences. - Thomas Y. Chen (AIDBEI, 2021)

Multi-temporal deep learning-based social media analysis for disaster relief. - Thomas Y. Chen (MobiSys, 2022)

Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery. - Thomas Y. Chen (CoRR, 2022)

MonarchNet: Differentiating Monarch Butterflies from Butterflies Species with Similar Phenotypes. - Thomas Y. Chen (CoRR, 2022)

Data Science and Machine Learning in Education. - Gabriele Benelli; Thomas Y. Chen; Javier M. Duarte; Matthew Feickert; Matthew J. Graham; Lindsey Gray; Dan Hackett; Philip C. Harris; Shih-Chieh Hsu; Gregor Kasieczka; Elham E Khoda; Matthias Komm; et al. (CoRR, 2022)

Interpretable Uncertainty Quantification in AI for HEP. - Thomas Y. Chen; Biprateep Dey; Aishik Ghosh; Michael Kagan; Brian Nord; Nesar Ramachandra (CoRR, 2022)

Deep Learning for Space Weather Prediction: Bridging the Gap between Heliophysics Data and Theory. - John C. Dorelli; Christopher M. Bard; Thomas Y. Chen; Daniel da Silva; Luiz Fernando Guides dos Santos; Jack Ireland; Michael Kirk; Ryan McGranaghan; Ayris Narock; Teresa Nieves-Chinchilla; Marilia Samara; Menelaos Sarantos; et al. (CoRR, 2022)

Science Platforms for Heliophysics Data Analysis. - Monica G. Bobra; Will T. Barnes; Thomas Y. Chen; Mark C. M. Cheung; Laura A. Hayes; Jack Ireland; Miho Janvier; Michael S. F. Kirk; James Mason; Stuart Mumford; Paul J. Wright (CoRR, 2023)