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Pages

About Me

Research Associate at the University of Melbourne specializing in Trustworthy AI, GNNs, and Video Understanding.

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Paper Title Number 4

Published in GitHub Journal of Bugs, 2024

This paper is about fixing template issue #693.

Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
Download Paper

talks

Trustworthy AI for Computer Vision

Published:

Organized by the IEEE Student Branch of University of Kelaniya Abstract: Trustworthy AI in computer vision necessitates models that are not only accurate but also reliable and aware of their limitations. Two critical pillars for achieving this trust are Out-of-Distribution (OOD) detection and model calibration. OOD detection aims to identify inputs that differ significantly from the training data, preventing overconfident and erroneous predictions on unfamiliar or anomalous samples. Calibration ensures that a model’s predicted confidence scores accurately reflect its true probability of being correct, separating predictive performance from reliability assessment. This work explores the synergy and challenges at the intersection of these fields. We review contemporary methods for OOD detection, including those based on density estimation, distance metrics, logit analysis, and energy scores, and techniques for calibration, such as temperature scaling and ensemble-based approaches.  Achieving robust OOD detection and calibration is essential for the safe deployment of vision systems in real-world, open-ended environments, forming the foundation for trustworthy AI that can recognize and express its own uncertainty.

teaching

Numerical Algorithms in Engineering (ENGR30004)

Undergraduate course, University of Melbourne, Department of Mechanical Engineering, 1900

Marker, Tutor for 2023-SM2, 2024-SM2, 2026-SM1 Marked Assignments and conducted tutorials on programming.