About me
I'm an AI researcher focused on building algorithms and tools aiming to advance healthcare through technology. I'm currently a PhD student at the University of Toronto, advised by Dr. Chris McIntosh and Dr. Michael Brudno where my research revolves around developing algorithms that address biases in AI models applied to medical data.
A special shoutout to the Schwartz Reisman Institute, Vector Institute, University Health Network (UHN) and SickKids Hospital for generously supporting my work and placing me in an environment of world-class researchers and clinicians.
I completed my Masters from the National University of Singapore (NUS), specializing in Computational Intelligence. Before joining the PhD program, I worked as an AI Engineer developing IP and building tools for AI augmented healthcare applications.
I love the sea, and I'm a certified scuba diver. P.S: If you ever need company to dive, drop me a message. Thanks to long layovers and commutes, I've been managing to read a bit. Here's my reading list if you are thinking "hmm, what should I read next?"
Research Interests
My primary research focus is on identifying and combating data biases and the phenomenon of shortcut learning in AI/healthcare models. I build algorithms for reducing the reliance of bias and spurious confounders and shortcuts of AI models for healthcare tasks. I'm interested in putting together new healthcare datasets (especially from developing nations) and building systems that improve healthcare pipelines and enable a more equitable utilization of AI tools.
I'm also interested in putting my engineering and research skills to use. If you have tasks which have a large amount of unlabelled data, please reach out. I have some experience with semi-supervised algorithms and building human in loop AI augment annotation pipelines.
On the other end of the spectrum, if you have very little data, I'm interested in utilizing generative models for data augmentation and generation.
PhD Updates
- Excited to announce that I've been awarded the prestigious 2024 Schwartz Reisman Institute Graduate Fellowship. The fellowship supports my work on debiasing AI models in healthcare.
- Our paper "Shortcut Learning in Medical AI Hinders Generalization" has been accepted at Nature Digital Medicine. We have also publicly released our code which allows researchers to estimate the generalization of their AI models without external data.
- I'm appearing as a guest on the ATGO-AI Podcast (Accountability, Trust, Governance, and Oversight of Artificial Intelligence) talking about my research and recent publication on data biases in healthcare data. The podcast is available on Spotify and Apple Podcasts: Episode 1 and Episode 2.
Volunteer Positions & Leadership
- Executive Council Member: Computer Science Graduate Students' Benevolent Society (CSGSBS)
- Distinguished Reviewer: IEEE Transactions in Medical Imaging (TMI)
- Reviewer: Nature Scientific Reports
- Mentor: Graduate Application Assistance Program (GAAP) [2022 - 2024]
- Chairperson: Singapore Computer Society (SCS) Chapter - NUS ISS
- Richard E Merwin Scholar - IEEE Computer Society
- Youth Excellence Award for Most Promising Engineer - Kerala
- Section Student Representative - IEEE Kerala Section
- Chairperson: IEEE Computer Society CET Chapter
- Co-Organizer: NASA Space Apps Challenge