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 very special shoutout to the Vector Institute, University Health Network (UHN) and SickKids Hospital for generously supporting my work and 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'm interested in building debiasing techniques 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 expreience 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.
Publications
Full and updated list available on Google Scholar
| # | Title | Venue/Journal |
|---|---|---|
| 1 | Self-path: Self-supervision for classification of pathology images with limited annotations | IEEE Transactions on Medical Imaging |
| 2 | Semi-supervised classification of diagnostic radiographs with NoTeacher: A teacher that is not mean | MICCAI / Extended Version in Medical Image Analysis |
| 3 | Towards practical unsupervised anomaly detection on retinal images | DART / MICCAI Workshop |
| 4 | Semi-supervised deep learning for abnormality classification in retinal images | ML4Health / NeurIPS Workshop | 5 | Diverse and consistent multi-view networks for semi-supervised regression | ECML PKDD |
| 6 | Semi-supervised and Unsupervised Methods for Heart Sounds Classification in Restricted Data Environments | Project Paper / Arxiv |
| 7 | CareNets: Efficient Homomorphic CNN for High Resolution Images | PriML / NeurIPS Workshop |
| 8 | Learning of multi-dimensional analog circuits through generative adversarial network (GAN) | IEEE SOC |
| 9 | Deep learning models for tuberculosis detection from chest X-ray images | Project / ICT |
Teaching Experience & Talks
A special section just there to make it easier for TA coordinators. If you are a hiring manager, please take a look at my CV (I'm on the lookout for internships and interesting projects!)
| Role | Title | Description / Duties |
|---|---|---|
| Teaching Assistant (Spring 2022, Winter 2022) | CSC 108: Introduction to Computer Programming | Lecture TA (for both online and in-person classes), exam and assignment grading, exam invigilation, and holding office hours to help students. |
| Teaching Assistant (Winter 2022) | CSC 2431: Artificial Intelligence in Medicine | Co-ordinated students and mentors for two Ukrainian Universities (NaUKMA, UCU) and Ph.D. students at UofT for a unique international course where students developed AI algorithms for medicine/health. Created and deployed a website to disseminate project details, teams, and study materials. |
| Teaching Assistant (Winter 2021) | CSC 420: Introduction to Image Understanding | Responsibilities included monitoring and answering student queries via Piazza, marking assignments, and grading exams. |
| Talk (2021) | AI in Healthcare | Gave a talk discussing AI in Healthcare use cases for computer science professionals in Singapore. [Slides] |
| Workshop (2021) | Literature Review Workshop | Led a workshop for undergraduate students where they learned to review scientific papers. Moderated the literature discussion on deep residual learning for image recognition. |
| Talk (2021) | Introduction to Artificial Intelligence & Machine Learning | Showed learning paradigms and AI/ML applications in various fields such as healthcare, finance, etc. Discussed with students in MuLearn (a learning community) ways to learn, upskill,and complete live projects. [Slides] |
| Talk (2020) | Lecture on Logistic Regression | Introduced the concept of logistic regression for students in MuLearn community. Also showcased the math behind back-propagation and differentiation involved in the technique. [Slides] |
Volunteer Positions / Achievements
I spend time volunteering in various capacities. I'm always on the lookout for interesting projects and initiatives. If you have something in mind, please reach out.
| Executive Council Member: Computer Science Graduate Students’ Benevolent Society (CSGSBS) |
| Reviewer: IEEE Transactions in Medical Imaging (TMI) |
| Mentor: Graduate Application Assistance Program (GAAP) |
| 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 |