I am a seasoned professional in the field of Data and AI,
with extensive research and practical experience in machine learning, deep learning, computer vision, and the Microsoft Azure AI stack, along with a proficiency in developing intelligent applications.
Currently, I am pursuing my Ph.D. in Deep Learning through a collaboration with the Australian Institute of Sport at La Trobe University, Australia.
My dedication to AI has been recognized by Microsoft, which has honored me as a Most Valuable Professional in Artificial Intelligence for seven consecutive years.
As an active contributor to the technical community, I regularly host sessions at technical conferences and user group meetups,
focusing on AI-related technologies. Furthermore, I maintain a blog where I share my experiences and experiments in deep learning and cognitive computing.
Research Interests : Deep Learning, Computer Vision, Artificial Intelligence, Responsible AI
Apart from being an AI researcher, I enjoy most of my time being outdoors. I enjoy hiking, camping, and nature photography. Can explain myself as a travel addict, ready to hit the road at any time with the backpack.
When forced indoors, I do enjoy writing free style poems and short stories, I am an aspiring chef, and I spend a large amount of my free time exploring the wonderful spices in my kitchen.
Group Activity Recognition using Unreliable Tracked Pose
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H Thilakarathne, A Nibali, Z He, S Morgan, arXiv Preprint (2024)
Pose is all you need: The pose only group activity recognition system (POGARS)
[Read]
H Thilakarathne, A Nibali, Z He, S Morgan, Machine Vision and Applications 33, 95(2022)
Predicting Floods in North Central Province of Sri Lanka using Machine Learning and Data Mining Methods.
[Read]
H Thialakarathne, K Premachandra, Proceedings of the 12th Annual Sessions, Sri Lanka Association for Artificial Intelligence 2017.
Improving Assessment on MOOCs through Peer Identification and Aligned Incentives.
[Read]
Gamage D,Whiting ME,Rajapakshe T,Thilakarathne H,Perera I et al., L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale.
Daemo: A Self-Governed Crowdsourcing Marketplace.
[Read]
Stanford HCI Research Group, UIST 2015 - Adjunct Publication of the 28th Annual ACM Symposium on User Interface Software and Technology.