About Me
I am a PhD candidate at Uppsala University, working on Computer Vision applied to Social Robotics. My project, "Explainable deep learning methods for human-human and human-robot interaction", aims to use modern end-to-end techniques to detect and analyse emotional alignment during social interactions. The long-term goal is to apply these findings to social robots, so we can improve their conversational skills.
My project is funded by Uppsala University's Centre for Interdisciplinary Mathematics (CIM), and by the Swedish Research Council's ELECTRA project (grant no. 2020-03167). I am a member of the Uppsala Social Robotics Lab (USRL), as well as Methods for Image Data Analysis (MIDA).
Teaching
- Master's thesis supervision: "Multimodal Prediction of rapport level in dyadic child-child interaction." Aditya Harichandar joined the Uppsala Social Robotics Lab as a student intern in Autumn 2023. Under my supervision, he curated an audio feature dataset based on the data collected in my paper "UpStory: the Uppsala Storytelling dataset", and performed rapport prediction using both feature-based methods (using scikit-learn) and audio-based methods (using PyTorch). He also worked on replicating my published video-based results, and exploring modality fusion variants to perform audiovisual prediction. His work culminated in the defense of his masters thesis in Spring 2024, in which I acted as the supervisor. (2024)
- Intelligent Interactive systems (1MD032 / 1MD039): Master's level course with a focus on the hands-on design and implementation of conversational agents. Duties: design of learning materials, teaching lab sessions, grading homework, supervising and grading final projects. Due to ongoing evolution of the field, I was asked to re-design and teach part of the course in three consecutive editions. (2021 - 2023)
Publications
- "Are We Friends? End-to-End Prediction of Child Rapport in Guided Play." To be published in the ECCV 2024 proceedings. (Workshop)
- "UpStory: the Uppsala Storytelling dataset." arXiv:2407.04352. (Submitted to IEEE Access) [DOI] [Code]
- "A Case Study in Designing Trustworthy Interactions: Implications for Socially Assistive Robotics." Frontiers in Computer Science 5: 1152532. (Journal) [DOI]
- "End-to-End Learning and Analysis of Infant Engagement During Guided Play: Prediction and Explainability." Proceedings of the 2022 International Conference on Multimodal Interaction. (Conference) [DOI] [Code]
- "Automatic analysis of infant engagement during play: An end-to-end learning and Explainable AI pilot experiment." Companion Publication of the 2021 International Conference on Multimodal Interaction. (Workshop) [DOI]