Rate My Professor Thusithanjana Thilakarathna

Thusithanjana Thilakarathna is a PhD candidate at Auckland University of Technology, New Zealand. He earned a Master of Science from the Sri Lanka Institute of Information Technology (SLIIT) in the Department of Computer Science and Software Engineering. His research specializes in human-computer interaction, mixed reality, computer vision, mobile computing, the Internet of Things, and software engineering. Thilakarathna emphasizes user-centered design, usability evaluation, mobile application development, IoT security, and improvements in software engineering processes. He maintains a Google Scholar profile with 86 citations, reflecting contributions to practical applications in health monitoring, environmental sustainability, education, and assistive technologies.

Thilakarathna has co-authored numerous papers presented at the International Conference on Advancements in Computing (ICAC) and other venues. Key publications include "Computer Vision Enabled Drowning Detection System" (2021, cited 29 times), which develops a computer vision-based system to detect drowning incidents. "Computer Vision Based Privacy Protected Fall Detection and Behavior Monitoring System for the Care of the Elderly" (2021, cited 10 times) introduces privacy-preserving monitoring for elderly care. "Computer-Vision Enabled Waste Management System for Green Environment" (2021, cited 9 times) proposes an automated waste sorting solution. More recent works encompass "Personalized and Interactive Learning Platform for Students with Autism Spectrum Disorder" (2023, cited 8 times), an interactive tool for special needs education; "AI Powered Virtual Stress Management Assistant for IT Professionals" (2023, cited 4 times); and "Ganitha Piyasa: Effective Lesson Delivery Method for Graphical Dyscalculia Students" (2023, cited 4 times). In 2024, he contributed to papers on facial diagnosis for skin care and makeup recommendations using deep learning (cited 2 times), GlowUp for AI-driven salon insights, deep learning frameworks for customized CNNs in salons, personalized hair care apps, and machine learning for context-aware mobile input validation. Earlier contributions include SMART Garbage Bin Kit (2021, cited 3 times) and Assist rendering software (2020, cited 1 time). At AUT, he collaborates on projects like Cybersickness Assessment in Virtual Reality funded by the Computer and Information Sciences Research Centre.