I'm a PhD Student in the Machine Learning Group at the University of Toronto and the Vector Institute. My supervisor is Anna Goldenberg and my research is in machine learning in healthcare. I'm interested in building solutions for barriers of adoption of AI in clinical settings, and my research spans from explainable AI to unsupervised representation learning and anomaly detection for medical time series. I work in close collaboration with clinicians at the Hospital for Sick Children and the Laussen Labs research group.
I received the Apple Scholars in AI/ML PhD fellowship
Our paper Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding was accepted to ICLR 2021!
Our paper What went wrong and when? Instance-wise feature importance for time-series models was accepted to Neurips 2020!
I received the health system impact fellowship from the Canadian Institute of Health Research (CIHR)
The inaugural Pan-Canadian Self-Organizing Conference on Machine Learning (PC-SOCMLx) is an event for Canadian graduate students in machine learning to meet each other and develop a research community (Supported by: Vector Institute, Mila, Amii, CIFAR, and Facebook)
Our paper "Individualized Feature Importance for Time Series Risk Prediction Models" was accepted to the Machine Learning for Health Workshop at NeurIPS, 2019
Our paper What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use was accepted to MLHC 2019!
I gave a talk at Toronto rehabilitation institute research round, with the title: "Prediction models for longitudinal data"