Anna Goldenberg

Co-chair of the AI in Medicine Initiative

Chair Positions

Varma Family Chair in Biomedical Informatics and Artificial Intelligence
CIFAR AI Chair

Dr. Anna Goldenberg is a Senior Scientist in Genetics and Genome Biology program at the SickKids Research Institute, and is also the first Varma Family Chair in Biomedical Informatics and Artificial Intelligence. She is an Associate Professor in the Department of Computer Science at the University of Toronto, faculty member and an Associate Research Director, Health at Vector Institute and a fellow at the Canadian Institute for Advanced Research (CIFAR), Child and Brain Development group.

Goldenberg trained in machine learning at Carnegie Mellon University, with a post-doctoral focus in computational biology and medicine. The current focus of her lab is on developing machine learning methods that capture heterogeneity and identify disease mechanisms in complex human diseases as well as developing risk prediction and early warning clinical systems. Goldenberg is a recipient of the Early Researcher Award from the Ministry of Research and Innovation. She is strongly committed to creating responsible AI to benefit patients across a variety of conditions.

Research

The great promise of AI in healthcare is taking time to materialize. Besides difficulties with access to the data and unrealistic expectations of the AI due to the hype fueled by the media, there are many fundamental machine learning advances that need to be made to achieve the widespread use of AI in healthcare.

Some of the current shortcomings include, but are not limited to:

• high error rates in predicting rare critical events (such as cardiac arrest)
• lack of the model explainability that makes it hard for clinicians to act upon predictions that diverge from their intuition
• the effects of adopting imperfect machine learning models in practice and the feedback loop of error such adoption creates.

Goldenberg’s lab is working on addressing these fundamental issues to build the foundation for responsible AI and healthcare, ensuring realization of the AI potential in healthcare.

Education and experience

• 2018–present: Varma Family Chair in Biomedical Informatics and Artificial Intelligence, The Hospital for Sick Children, Toronto, ON
• 2018–present: Associate Research Director, Health, Vector Institute, Toronto, ON
• 2018–present: Senior Scientist, Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON
• 2018–present: Associate Professor, Department of Computer Science, University of Toronto, Toronto, ON
• 2018–present: Associate Professor, Department of Computer Science, University of Toronto, Toronto, ON
• 2017–present: Co-chair of AI in Medicine Initiative, The Hospital for Sick Children, Toronto, ON
• 2017–present: Faculty Member and Associate Research Director Health, Vector Institute, Toronto, ON
• 2011–2017: Scientist, Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON
• 2012–2018: Assistant Professor, Department of Computer Science, U of T, Toronto, ON

Achievements

• 2019: AI Chair, CIFAR
• 2019: International Advisory Board, The Lancet Digital Health
• 2019: AI task force for Healthcare (AI4H) organized by CIFAR
• 2019: Member of the AI Task Force at the Royal College of Physicians and Surgeons
• 2018: Area Chair, ISMB
• 2018: Education Program Committee Member, 2018 AACR Annual Meeting
• 2017: Canadian Research Chair in Computational Medicine Tier II, Government of Canada
• 2017: Faculty Member, F1000prime, Bioinformatics, Biomedical Informatics & Computational Biology Faculty
• 2017: Organizer of the PI Prep School, The Hospital for Sick Children
• 2017: Co-chair, Analytical Framework Working Group, UCAN CAN-DU committee
• 2017: Program committee, ISMB
• 2016: Fellow of the Candian Institute for Advanced Research in Child & Brain Development program, CIFAR
• 2016: Child and Brain Development Research Support, CIFAR
• 2016: Early Researcher Award, Ministry of Research and Innovation
• 2016: Award for exceptional mentoring and outstanding commitment to graduate student recruitment, University of Toronto, Computer Science

1. GA Adam, L Rampášek, Z Safikhani, P Smirnov, B Haibe-Kains, A Goldenberg. (2020) Machine Learning Approaches to Drug Response Prediction: Challenges and Recent Progress. Nature Precision Oncology 4 (9).
2. J Wiens, S Saria, M Sendak, M Ghassemi, VX Liu, F Doshi-Velez, K Jung, K Heller, D Kale, M Saeed, PN Ossorio, S Thadaney-Israni, A Goldenberg (2019). Do no harm: a roadmap for responsible machine learning for health care. Nature Medicine. doi: 10.1038/s41591-019-0548-6 free version: https://rdcu.be/bO6ed
3. W Nelson, M Zitnik, B Wang, J Leskovec, A Goldenberg*, R Sharan*. (2019) To embed or not: network embedding as a paradigm in computational biology. Frontiers in Genetics, section Systems Biology. 1;10:381
4. Rampasek, D.Hidru, P. Smirnov, B. Haibe-Kains, A. Goldenberg. (2019). Dr. VAE: Improving drug response prediction via modeling of drug perturbation effects. Bioinformatics. Oxford University Press. btz158, https://doi.org/10.1093/bioinformatics/btz158
5. Aristizabal, IAnreiter, T Halldorsdottir, CL. Odgers, TW. McDade, A Goldenberg, S Mostafavi, MS. Kobor, EB. Binder, MB. Sokolowski, KJ. O’Donnell (2019). Biological Embedding of Experience: a primer on epigenetics, PNAS. https://doi.org/10.1073/pnas.1820838116 

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