Yuanxi Fu

My research straddles the theory side and the application side of information science. I want to understand how big data and AI change our methods of knowing and the associated risks and benefits. I apply my theoretical contemplation to research quality in science as well as machine learning and data curation education.

Dissertation Title: Unreliability propagation in science: conceptual foundations and mitigation measures

• CASOS Summer Institute & IDeaS Summer Institute Scholarship (2022)
• The Web Conference 2021 scholarship
• JASIST's Top Reviewers for 2021

• Spring 2025 - Concepts of Machine Learning - IS327B

Google Scholar profile

Fu, Y., & Schneider, J. (2025). Engineering the reproducible literature review section for scholarly publications and grant applications. Accepted by Machine Learning and Knowledge Engineering for Trustworthy Multimodal and Generative AI (AAAI-MAKE 2025). To appear in Proceedings of the 2025 AAAI Spring Symposium Series

Zheng, H.*, Fu, Y.*, Sarol, M.J., Sarraf, I., and Schneider, J. “Addressing Unreliability Propagation in Scientific Digital Libraries”. Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2024 (JCDL 2024). https://doi.org/10.1145/3677389.3702526 (*equal contribution)

Fu, Y., & Schneider, J. (2024). An argumentation interface to facilitate human-machine collaboration in scientific research: A preliminary exploration. Proceedings of the 24th Workshop on Computational Models of Natural Argument (CMNA ’24). https://ceur-ws.org/Vol-3769/paper4.pdf

Fu, Y., Clarke, C. V., Van Moer, M., & Schneider, J. (2024). Exploring evidence selection with the inclusion network. Quantitative Science Studies, 1–27. https://doi.org/10.1162/qss_a_00287

Schneider, J., Woods, N. D., Proescholdt, R., & the RISRS Team. (2022). Reducing the Inadvertent Spread of Retracted Science: Recommendations from the RISRS report. Research Integrity and Peer Review, 7(1), 6. https://doi.org/10.1186/s41073-022-00125-x

Fu, Y., Yuan, J., & Schneider, J. (2021). Using Citation Bias to Guide Better Sampling of Scientific Literature. Proceedings of the 18th International Conference on Scientometrics & Informetrics, 419–424. http://jodischneider.com/pubs/issi2021.pdf

Schneider, J., Woods, N. D., Proescholdt, R., Fu, Y., & Team, T. R. (2021). Reducing the inadvertent spread of retracted science: Shaping a research and implementation agenda. F1000Research, 10(211), 211. https://doi.org/10.7490/f1000research.1118522.1

Fu, Y., Schneider, J., & Blake, C. (2021). Finding Keystone Citations for Constructing Validity Chains among Research Papers. Companion Proceedings of the Web Conference 2021, 451–455. https://doi.org/10.1145/3442442.3451368

Hsiao, T.-K., Fu, Y., & Schneider, J. (2020). Visualizing evidence-based disagreement over time: The landscape of a public health controversy 2002–2014. Proceedings of the Association for Information Science and Technology, 57(1), e315. https://doi.org/10.1002/pra2.315

Fu, Y., & Schneider, J. (2020). Towards Knowledge Maintenance in Scientific Digital Libraries with the Keystone Framework. Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, 217–226. https://doi.org/10.1145/3383583.3398514