Gabriela Morgenshtern

Gabriela is a doctoral candidate at the IVDA lab. She is interested in when and how machine learning models fail to perform predictions that are personally relevant to users. She is keen on building interactive tools that empower recovering athletes, patients, and healthcare practitioners to give feedback to models they use in their care, so that these models may be recalibrated to their insights. She does such research by leveraging interactive visualization strategies, and by using such interactions to provide machine learning models with usable feedback for calibration and retraining

Gabriela received her Hon. B.Sc. in Bioinformatics and Computational Biology, and her M.Sc. in Computer Science at the University of Toronto, under supervision of Prof. Anna Goldenberg and Prof. Fanny Chevalier. Her graduate studies have included research affiliations with the Vector Institute and Toronto's Hospital for Sick Children, a teaching assistanship at the Canadian Bioinformatics Workshop, and a DeepMind Scholar Fellowship. Her research is informed by her past work in industry, and she has a particular soft spot for elegant user experiences, and projects with collaborators from healthcare or athletics.

Currently, Gabriela is especially keen on building human-model teaming applications that leverage data from wearable sensors. As a DSI-Health community member and DSI Excellence Fellow, she is able to study such topics through her ongoing project on interactive visualization of sensor data for remote monitoring of athletic patients 

When she is not at the lab, Gabriela can usually be found on a mountain ⛰️