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About Shreya
Dr. Shreya Ghosh served as a Lecturer and Research Fellow (Level B) in the School of Electrical Engineering, Computing and Mathematical Sciences at Curtin University, Faculty of Science and Engineering, from January 2023 to August 2025. Her core research area is Affective Computing using Computer Vision and Machine Learning techniques, with expertise in human behaviour understanding, human-centred AI, rehabilitation robotics, and multimodal data encompassing images, videos, text, and physiological signals. Ghosh obtained her PhD in Information Technology from Monash University in October 2022, with a thesis on self-supervised, unsupervised, weakly- and semi-supervised learning paradigms for generalized gaze representation learning. She also completed a Masters by Research under a GATE Scholarship from 2016 to 2018. Prior to Curtin, she was a Post-Doctoral Research Fellow at Monash University's Faculty of Information Technology from March 2022 to January 2023, funded by DARPA on a project involving dialogue assistance based on cross-cultural understanding.
At Curtin University, Ghosh coordinated and lectured units including Human Computer Interface and Advanced Human Computer Interface (ICTE 3002/5001) across multiple campuses, serving hundreds of students. She co-organized workshops such as the Multimodal, Generative and Responsible Affective Computing (MRAC) track and contributed to competitions like ABAW at ECCV. Her key publications include 'AVDeepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset' (ACM Multimedia 2024, Best Student Paper Award), 'Emolysis: A Multimodal Open-Source Group Emotion Analysis and Visualization Toolkit' (ACII 2024), 'MARLIN: Masked Autoencoder for facial video Representation LearnINg' (CVPR 2023), 'Automatic Gaze Analysis: A Survey of Deep Learning based Approaches' (IEEE TPAMI 2023), and 'Depression Intensity Estimation via Social Media: A Deep Learning Approach' (IEEE Transactions on Computational Social Systems 2021). Ghosh has earned awards including the DAAD AInet Fellowship for Safety and Security in AI (2024), Best Reviewer Award (top 5%) at ICMI 2021, and Highly Commended team at CUSP London Data Dive (2023). Her research has amassed over 1,585 citations, influencing advancements in affective computing.
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