Anderson Augusma
Papers
1
Total Citations
5
H-Index
1
About
Anderson Augusma is a researcher at the forefront of affective computing, specializing in multimodal group emotion recognition and privacy-preserving machine learning. His most-cited work, "Multimodal Group Emotion Recognition In-the-wild Using Privacy-Compliant Features" (2023, 5 citations), tackles the critical challenge of understanding collective emotional states in unconstrained, real-world settings—a task with profound implications for social robotics, conversational agents, e-coaching, and learning analytics. Augusma’s key contribution lies in demonstrating that accurate group-level emotion recognition is achievable even when using privacy-compliant features, addressing a major ethical and technical hurdle in deploying such systems outside the lab. By participating in the EmotiW Challenge 2023, his research not only advances the state-of-the-art but also sets a new standard for responsible AI development. This work underscores his commitment to building emotionally intelligent systems that respect user privacy, making his research highly relevant for students and practitioners aiming to create socially aware, trustworthy technologies.
Research Focus
Key Achievements
Top Papers
- 1