Hi! I'm Yangxin Fan. Currently, I am a PhD candidate in Computer Science at the Case Western Reserve University, advised by Professor. Yinghui Wu (https://yinghwu.github.io/)
My main research interests include Data Mining, Machine Learning, Causal Inference, and Network Analysis with their applications in Computational Social Science, Health & Biomedical Informatics, and Material Science.
Besides research, I enjoy Photography and Spaceflight.
Publications
1. Yangxin Fan, Xuanji Yu, Raymond Wieser, David Meakin, Avishai Shaton, Jean-Nicolas Jaubert, Robert Flottemesch, Michael Howell, Jennifer Braid, Laura S.Bruckman, Roger H.French, Yinghui Wu. Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Timeseries Data Imputation, ACM SIGMOD Conference on Management of Data (SIGMOD), 2023.
2. Yangxin Fan, Hanjia Lyu, Jin Xiao, Jiebo Luo. American Twitter Users Revealed Social Determinants-related Oral Health Disparities amid the COVID-19 Pandemic, Quintessence International, 2022
3. Hanjia Lyu, Yangxin Fan, Ziyu Xiong, Mayya Komisarchik, Jiebo Luo. Understanding Public Opinion Toward the #StopAsianHate Movement and the Relation With Racially Motivated Hate Crimes in the US, IEEE Transactions on Computational Social Systems.
4. Yangxin Fan*, Yuan Wang* (*Equal contribution). Analysis and Prediction of U.S. Fatal Police Shooting, Joint Conference of the Upstate Chapters of American Statistical Association, 2021 (Oral presentation)
Selected Projects
1. Fraud detection models and trust level validation in Mobile Money Transactions (MMT)
Goal: To develop a service module train a series of fraud detection classifiers and choose top k best models and a client module which can validate the functionality of selected models and assign trust levels for mobile money transaction fraud detection.
Detail: https://github.com/Yangxin666/Fraud-detection-models-and-trust-level-validation-in-MMT
Project from AI Startup RIG (https://rigroup.co/) when I was a Machine Learning Engineer Intern there.
Architecture diagram
2. Predict Clinical Insomnia Among Cancer Survivors using YOCAS RCT data
Goal: To develop Machine Learning models to predict ISI (Insomnia Severity Index), analyze the patient-symptom bipartite network, and deliver clinically meaningful analysis.
