Xiaofen Xing

Papers

Total Citations

278

H-Index

About

Xiaofen Xing is a leading researcher in affective computing and financial AI, whose work bridges brain-inspired robotics and quantitative finance. Her most influential contribution, the "SAE+LSTM" framework for emotion recognition from multi-channel EEG (268 citations), introduced a novel linear EEG mixing model combined with long short-term memory networks to decode human emotional states. This breakthrough has significant implications for improving human-robot interaction in brain-inspired systems, enabling more empathetic and responsive autonomous agents. Xing's research extends to explainable financial AI, as demonstrated in her recent work "FinReport" (10 citations), which leverages large language models to democratize stock earnings forecasting for ordinary investors by automatically mining and analyzing news factors. This dual focus on neural signal processing and interpretable machine learning showcases her versatility in applying deep learning to both cognitive science and practical financial decision-making. Her work has been recognized for its interdisciplinary impact, advancing both the theoretical understanding of EEG-based emotion classification and the development of transparent AI tools for real-world applications.