Andreas Triantafyllopoulos
Papers
1
Total Citations
16
H-Index
1
About
Andreas Triantafyllopoulos is a leading researcher in affective computing and human behaviour analysis, with a particular focus on the intersection of machine learning, fairness, and multimodal signal processing. His work addresses the critical challenge of building personalised and equitable AI systems that can understand subtle human behaviours, such as humour—a nuanced social signal with profound implications for human-robot interaction and mental health. Triantafyllopoulos’s most cited paper, "A Personalised Approach to Audiovisual Humour Recognition and its Individual-level Fairness" (2022, 16 citations), exemplifies his commitment to developing models that not only perform well but also uphold individual-level fairness, a crucial step toward ethical AI. His research has significantly advanced the field by demonstrating how personalisation can mitigate biases in automated behaviour recognition, ensuring that systems work equitably across diverse users. With a growing citation impact, Triantafyllopoulos’s work is shaping the next generation of socially aware and fair artificial intelligence, making him a key voice in the responsible deployment of affective computing technologies.
Research Focus
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Top Papers
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