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About Me
I am a Ph.D. student in Statistics at University of Georgia from 08/2024 to 06/2028. Before that, I received my Bachelor's degree in Computer Science from Nankai University in China. My current research interest includes explainable LLM, trustworthy machine learning, recommendation system and symbolic data analysis.
Internship
Machine Learning Engineer @ TikTok, San Jose, CA, United States, 05/2026-08/2026
Publication
Research paper
- X Gong, S Yang, Z Cao, L Billard, D Wang. Faithful-Patchscopes: Understanding and Mitigating Model Bias in Hidden Representations Explanation of Large Language Models.
- X Gong, Y Chen, S Wu, F Wang, P Ma, W Zhong. S2MNet: Speckle-To-Mesh Net for Three-Dimensional Cardiac Morphology Reconstruction via Echocardiogram.
- S Wu, X Gong, Y Chen, Y Liu, W Zhong, P Ma. Fisher Contrastive Learning: A Robust Solution to the Feature Suppression Effect
- J Hu, S Yang, X Gong, H Wang, W Liu, D Wang. MONICA: Real-Time Monitoring and Calibration of Chain-of-Thought Sycophancy in Large Reasoning Models. The Forty-Third International Conference on Machine Learning 2026
- Z Zhang, T Wang, X Gong, Y Shi, H Wang, D Wang, L Hu. When modalities conflict: How unimodal reasoning uncertainty governs preference dynamics in mllms. The Forty-Third International Conference on Machine Learning 2026
- S Yang, J Wu, X Gong, X Wu, D Wong, N Liu, D Wang. Investigating CoT Monitorability in Large Reasoning Models.
- L Hu, T Huang, H Xie, X Gong, C Ren, Z Hu, L Yu, P Ma, D Wang. Semi-supervised concept bottleneck models. International Conference on Computer Vision 2025
- S Wu, B Yang, H Yang, S Coshatt, X Gong, R Parasuraman, J Conrad, W Zhong, J Ye, P Ma, W Song. Online Adaptively Anomaly Detection in Networked Electrical Machines by Enveloped Singular Spectrum Transformation. IEEE Internet of Things Journal 2024
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