Hello there. I study how we can predict human disagreements during human annotation using machine learning. This work is helpful when we want to model human disagreements, which is conventionally considered annotation noise. Following recent breakthroughs in machine learning research has shown instances where the algorithms being biased towards specific groups. I’m PhD student at the Lab for Population Intelligence at RIT led by Professor Christopher Homan.
Currently in the job market. I’ve interned at Amazon Ads as an Applied Scientist Intern (2023), Meta (Facebook) in Summer 2022 and RPI (IBM Watson Project) in Summer 2019.
In parallel, I’m also working with University of Kelaniya in Sri Lanka to build an electronic medical record system for the entity of Sri Lanka.
My previous research also comes from sociolinguistics, studying the evolution of Sri Lankan English across multiple generations.
I enjoy DevOPS side of systems and building systems that are end to end.
When I’m not at my desk, I envy traveling.
PhD in Computer Science, Current
Rochester Institute of Technology
BSc in Computer Science, 2017
University of Kelaniya
Offensive speech detection is a key component of content moderation. However, what is offensive can be highly subjective. This paper investigates how machine and human moderators disagree on what is offensive when it comes to real-world social web political discourse. We show that (1) there is extensive disagreement among the moderators (humans and machines); and (2) human and large-language-model classifiers are unable to predict how other human raters will respond, based on their political leanings. For (1), we conduct a noise audit at an unprecedented scale that combines both machine and human responses. For (2), we introduce a first-of-its-kind dataset of vicarious offense. Our noise audit reveals that moderation outcomes vary wildly across different machine moderators. Our experiments with human moderators suggest that political leanings combined with sensitive issues affect both first-person and vicarious offense.
Worked at the Facebook Creators Wellbeing Team on Public Conversations. Overlooked models for improving the comments recommendation and ranking models on Facebook Pages with varying populations of followers from around the globe.
Project - Introduction of a Multi-Label Multi-Task model for assisting page administrations for comment management.
Collaborative project with the Faculty of Medicine, and Colombo North Teaching Hospital, Sri Lanka.