Papers
10
Total Citations
328
H-Index
7
About
Xiaoping Wang is a multidisciplinary researcher working at the intersection of neuromorphic computing, affective intelligence, and conversational AI. Their most significant contributions span two interconnected domains: memristive circuit design for brain-inspired emotional and cognitive modeling, and deep learning approaches for emotion recognition in conversation. Wang's pioneering work in memristive circuits has produced biologically plausible hardware implementations of emotional generation, affective associative learning, and decision-making processes, drawing inspiration from neurological mechanisms such as the brain emotional learning theory, classical and operant conditioning, and hippocampal spatial cognition. These circuits simulate how humans form, store, and retrieve emotions at the hardware level — a frontier with profound implications for neuromorphic computing and robotics. This body of work has collectively accumulated over 200 citations, with individual papers garnering up to 69 citations. On the software side, Wang developed GA2MIF, a graph and attention-based multi-source information fusion framework for multimodal emotion recognition in conversation, which has attracted 91 citations and represents a meaningful advance in human-computer interaction. Across their portfolio, Wang demonstrates a consistent commitment to bridging biological intelligence and machine systems, making their research particularly valuable for students exploring affective computing, neuromorphic engineering, and intelligent human-machine interaction.
Research Focus
Key Achievements
Top Papers
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- 3Memristive Circuit Design of Brain-Like Emotional Learning and Generation61 citations · 2021
- 4The Design of Memristive Circuit for Affective Multi-Associative Learning53 citations · 2020
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