Elham Dolatabadi is an applied AI scientist and academic with eight years of experience translating machine learning research into healthcare impact, currently a Faculty Affiliate at the Vector Institute and an Assistant Professor at York University (with an affiliation at U of T’s Dalla Lana School of Public Health). She combines rigorous PhD-level expertise in Bayesian and deep learning methods with hands-on development of predictive and anomaly-detection systems for high-dimensional time-series and gait analysis, having led projects that influenced clinical practice. Her background spans industry and clinical research—from RBC data science roles to AI postdoctoral work at University Health Network—bringing both production-focused pipelines and novel model development to bear on real-world problems. Known for mentoring interdisciplinary teams and shipping reproducible prototypes (including public GitHub tooling from her doctoral work), she focuses on making AI demonstrably useful and trustworthy in healthcare settings.
8 years of coding experience
10 years of employment as a software developer
Bachelor of Science (BSc), Electrical Engineering, Bachelor of Science (BSc), Electrical Engineering at K. N. Toosi University of Technology
Master of Engineering Science, Electrical and Computer Engineering, Master of Engineering Science, Electrical and Computer Engineering at Western University
Doctor of Philosophy (Ph.D.), Artificial Intelligence, Doctor of Philosophy (Ph.D.), Artificial Intelligence at University of Toronto
Neural Agent Assistant framework includes tools and artifacts rooted in deep learning and foundation language models to improve task-oriented customer service conversations and experiences.
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