Rate My Professor Prithwi Chakraborty

Dr. Prithwi Chakraborty serves as a Lecturer in Information Technology within the Faculty of Science and Engineering at Southern Cross University. He earned a Bachelor of Computer Science and Engineering (BCompSci&Eng) and a PhD from Queensland University of Technology (QUT), with his doctoral research centered on affective computing, emotion analysis, and machine learning. Chakraborty commenced his academic career as a research fellow at QUT, where he engaged in collaborations with the University of Sydney, Griffith University, Queensland Health, and Queensland Children's Hospital (QCH). These partnerships yielded multiple highly impactful journal papers. Subsequently, he transferred to Southern Cross University, continuing his contributions to research clusters such as Reefs and Oceans, with his work aligned to the United Nations Sustainable Development Goals.

Chakraborty's research specializations include affective computing, emotion analysis, machine learning, and cognitive load projects. His key publications demonstrate influence in human-computer interaction, health applications, and computer vision. Notable works encompass 'The rise of office design in high-performance, open-plan environments' (Candido, Chakraborty, & Tjondronegoro, 2019), 'Continuous Learning without Forgetting for Person Re-Identification' (Sugianto, Tjondronegoro, Sorwar, Chakraborty, & Yuwono, 2019), 'myPainPal: Co-creation of a mHealth app for the management of chronic pain in young people' (Cooke, Richards, Tjondronegoro, Chakraborty, et al., 2021), 'Using viewer's facial expression and heart rate for sports video highlights detection' (Chakraborty, Zhang, Tjondronegoro, & Chandran, 2015), 'Automatic identification of sports video highlights using viewer interest features' (Chakraborty, Tjondronegoro, Zhang, & Chandran, 2016), 'Automatic classification of physical exercises from wearable sensors using small dataset from non-laboratory settings' (Chowdhury, Farseev, Chakraborty, Tjondronegoro, et al., 2017), 'A machine learning approach to identify fall risk for older adults' (Chakraborty & Sorwar, 2022), and 'Towards generic modelling of viewer interest using facial expression and heart rate features' (Chakraborty, Tjondronegoro, Zhang, & Chandran, 2018). These publications underscore his ongoing impact in the field.