Elham Dolatabadi
I'm an applied Machine Learning Scientist at Vector Institute. I also have an Assistant Professor appointment (status only) at the Institute of Health Policy, Management, and Evaluation (IHPME​) at the University of Toronto.
My work portfolio and research agenda are mainly focused on the adoption of Machine Learning (ML) and Deep Learning technologies for real-world needs. My mission is to bring the power of ML and Data Science to health in order to improve human health and address the big challenges facing our healthcare system.
elham<dot>dolatabadi<at>mail<dot>utoronto<dot>ca
About Me
I have over 10 years of experience in leading various applied AI projects in collaboration with public and private health sectors. I have well-established experiences in developing and teaching health AI training programs and graduate-level courses on ML and knowledge representation in the health domain.
Prior to joining Vector Institute, I was a postdoctoral researcher at KITE research institute - University Health Network where my research focused on applying ML techniques in the development and evaluation of intelligent health monitoring technologies. I finished my PhD in 2015, at the University of Toronto and was a member of the intelligent assistive technology and systems lab under the supervision of Dr. Alex Mihailidis.
News & Highlights
March 2022: Excited to present at Vector's Endless Summer School: Healthcare Roundup about Deep Learning meets Public Health Surveillance.
Feb. 2022: Our work on Using AI to Help Solve The Long Covid Puzzle is now available as a blog post on Vector.
Dec. 2021: Our Tick Identification paper is now published in IEEE Journal of Translational Engineering in Health and Medicine.
April 2021: Excited to be a panelist on the topic of Prediction, Machine Learning and Causal Inference at Data Science Interdisciplinary Research Cluster, Dalla Lana School of Public Health.
Dec. 2020: Our NLP Project Technical Report is now available on Vector's BLOG & NEWS.
Nov. 2020: Our paper on Question-Answering pair generation in response to COVID-19 is published at Scholarly Document Processing Workshop at EMNLP 2020.
Oct. 2020: Our work on using Knowledge Transfer for medical imaging diagnostic is accepted to MED-NEURIPS 2020.
Sep. 2020: The preprint of our work on computer vision and Lyme disease with Public Health Ontario is now on arXiv.
Education
2011 - 2015, University of Toronto, Biomedical Engineering with focus on Machine Learning
2009 - 2011, University of Western Ontario, Electrical and Computer Engineering
2002 - 2009, K.N.Toosi University of Technology, Electrical Engineering
Teaching
Graduate Course: MHI2002H, Emergent Topics in Health Informatics: Intelligent Medicine, Machine Learning and Knowledge Representation, IHPME, University of Toronto.
Invited Talks
Deep Learning meets Public Health Surveillance. Vector's Endless Summer School: Healthcare Roundup. March 2022.
Validating Data Quality in a Public Health Crisis. Workshop Presenter. A New Normal: 14th Annual Dalla Lana School of Public Health Student-Led Conference. Dalla Lana School of Public Health, Toronto. November 2021.
From sensing to surveillance, a wide spectrum of health AI applications. Evaluative Clinical Science Rounds 2019, ICES, Toronto, Canada.
Automating information extraction from EHR, part of Innovative approaches in acquiring, processing, and analyzing EMR and EHR data. ICES Fall Forum 2019, Toronto, Canada.
Generalization and Transfer Learning in pre-trained Natural Language Processing Models. Endless Summer School (ESS) program 2019, Vector Institute, Toronto, Canada.
The Promise and Potential of ML in Health. ICES Data Science Education Webinar Series 2019, Toronto, Canada.
Deep generative models and reviewing Auto-Encoding Variational Bayes. Aggregate Intellect (A.I), Toronto, Canada.