Karishma Raut
Papers
2
Total Citations
4
H-Index
2
About
Karishma Raut is a rising researcher at the forefront of affective computing and human-computer interaction (HCI). Her work centers on developing advanced multimodal deep learning frameworks to enable machines to perceive and respond to human emotions in real-world settings. Raut’s key contribution is the creation of the Multimodal Diverse Spatio-Temporal Network (MDSTN), a low-complexity architecture designed for video-based affect recognition. This model addresses the critical challenge of integrating audio and visual modalities—such as facial expressions and speech—to improve accuracy in applications ranging from robotic interfaces to mental health assessments, including stress, depression, and Alzheimer’s disease detection. Her 2024 paper on this framework has already garnered 2 citations, while her 2025 follow-up on multimodal perception for enhanced HCI has also received early recognition. By tackling the limitations of unimodal approaches, Raut is paving the way for more empathetic and responsive AI systems. Her work holds significant promise for healthcare, assistive robotics, and beyond, establishing her as a notable emerging voice in the field of real-world affect recognition.
Research Focus
Key Achievements
Top Papers
- 1Multimodal spatio-temporal framework for real-world affect recognition2 citations · 2024
- 2