Dr Goldenberg is a Senior Scientist in Genetics and Genome Biology program at SickKids Research Institute, recently appointed as the first Varma Family Chair in Biomedical Informatics and Artificial Intelligence. She is also 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. Dr 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. Dr Goldenberg is a recipient of the Early Researcher Award from the Ministry of Research and Innovation and a Canada Research Chair in Computational Medicine. She is strongly committed to creating responsible AI to benefit patients across a variety of conditions.
Alex graduated with a Master's degree in computer science from U of T in 2019, and is currently pursuing a PhD. Alex is interested
in building machine learning models that have stable performance once deployed in the real world. The unanticipated influence of model
predictions on the outcomes of future samples, as well as the complex trust dynamics which occur between human users and ML models are
his primary focus. Alex believes that to safely harness the power of AI, one must be able to detect when predictions are no longer
reliable, and determine how to optimally update models on frequently shifting data streams.
Awards: NSERC CGS-D
Hidden Risks of Machine Learning Applied to Healthcare
Towards Robust Classification Model by Counterfactual and Invariant Data Generation
Transparency and reproducibility in artificial intelligence
Chun-Hao (Kingsley) is a 5-th year PhD student in Computer Science at University of Toronto with Professor Anna Goldenberg.
His main research works include Interpretability, Robustness, and application to Healthcare. He has worked as intern in Google,
Ｍicrosoft and Facebook.
Lauren Erdman is the Project Manager for SickKids Hospital’s new Data Science team and a PhD student in Computer Science at University of
Toronto under the supervision of Anna Goldenberg. Previously she completed a MSc in Computer Science and a MSc in Biostatistics at
University of Toronto under the supervision of Anna Goldenberg and Lisa Strug, respectively. Her research is focused on developing and
applying machine learning methods primarily for data integration and improved translational discovery in the areas of population genetics,
genome biology, and complex disease.
Google Scholar: https://scholar.google.com/citations?user=bSKEpp8AAAAJ&hl=en
Abhishek Moturu received his HBSc in 2019 from the University of Toronto (Trinity College) where he pursued a Mathematics major and a
Computer Science specialist. He received his MSc in Computer Science at the University of Toronto in 2021, working with Dr. Babak Taati
on automated pain detection for older adults with dementia at the UHN KITE Research Institute. At the Goldenberg Lab, he is working on
pediatric cancer surveillance in whole-body MRIs. During his PhD, his interests lie in developing theory and
applications for deep learning and federated learning in the healthcare setting. In his free time, Abhishek enjoys driving,
film, cooking, and tennis. He is currently also an Academic Don for Computer Science, Mathematics, and Physics at Trinity College.
Awards: NSERC USRA ($6K), CIHR CGS-M ($17.5K), QEII-GSST ($15K)
Google Scholar: https://scholar.google.ca/citations?user=pr333dYAAAAJ&hl=en
Sujay is enrolled in the MD/PhD program at the University of Toronto and is pursuing his PhD in computer science under the supervision of
Drs. Anna Goldenberg and Sebastian Goodfellow. His research interests lie at the intersection of machine learning and medicine- specifically,
working with time-series data to learn about patient states from in-hospital as well as out-patient datasets.
Awards: Ontario Graduate Scholarship 2021-2022 (15K), SickKids Restracomp PhD Scholarship (26K/year)
Recent interests: wearable sensors
Google Scholar: https://scholar.google.com/citations?user=8YrBnPsAAAAJ&hl=en
Dustin is a PhD candidate with Dr. Anna Goldenberg and Dr. Michael Wilson. He develops bioinformatic tools to integrate and interpret
transcriptomic analysis. He then applies those tools with others to interrogate pubertal dynamics across puberty in different species.
In his spare time, he likes to play guitar and climb rocks.
Awards: NSERC CGS-D
Single-cell mapper (scMappR): using scRNA-seq to infer the cell-type specificities of differentially expressed genes
Google Scholar: https://scholar.google.com/citations?user=029JbjsAAAAJ&hl=en
Valli graduated with an Honours Bachelor of Science degree in Biomedical Science & Computer Science from Western University.
She is currently pursuing a PhD in Medical Biophysics at the University of Toronto to further her interests at the intersection of
healthcare and computer science. Her current projects involve explaining clinical heterogeneity of cancer predisposition syndromes
by analyzing complex genomics/epigenomics data and applying machine learning algorithms.
Google Scholar: https://scholar.google.com/citations?user=029JbjsAAAAJ&hl=en
Sana is a PhD student in the department of Computer science. Her research focus is on the challenges of modelling medical time
series, from unsupervised representation learning to explainability. She is currently an Apple scholar in AI/ML fellow and
was previously a CIHR health system impact fellow. In her free time, Sana enjoys baking, and playing basketball and tennis.
Awards: CIHR health system impact fellowship, Apple scholars in AI/ML
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding
What went wrong and when? Instance-wise feature importance for time-series black-box models
What clinicians want: contextualizing explainable machine learning for clinical end use
Prediction of cardiac arrest from physiological signals in the pediatric ICU
Google Scholar: https://scholar.google.com/citations?user=OZDGXgwAAAAJ&hl=en
Kopal is a Masters student in Computer Science at the University of Toronto. She graduated with a BASc in Biomedical Engineering, Co-op from the University of Waterloo. She spent the last 2 years of her undergrad as a researcher at the Broad Institute developing computational methods and interactive visualization tools for analysis of large-scale population genetics and single-cell transcriptomics data. She is broadly interested in data science and machine learning with applications across healthcare and medicine. Outside of the lab, Kopal enjoys digital art, badminton, and baking.
Gabi is an undergraduate student in bioinformatics and computational biology at the University of Toronto. She’s currently
doing her Advanced Special Project in Bioinformatics with Lauren Erdman. Her research interests include human-computer interaction,
data engineering, and robust data design. She wants to learn more about designing data systems that take into account its users’
end goals, applications in machine learning, and clinical data visualizations.
Awards: DeepMind Scholarship
Aslesha is a Master's student in Applied Computing at the University of Toronto and a Vector Scholarship in
Artificial Intelligence Recipient 2021-22. She graduated with an Honours Bachelor of Science degree with majors
in Computer Science & Physics and a minor in Mathematics from the University of Toronto. Her research interests
include causality and machine learning and its application in healthcare. She is also broadly interested in
explainability and fairness in AI and likes watching movies and biking in her spare time.
Awards: Vector Scholarship in Artificial Intelligence
Parinita is an undergraduate student in computer science and statistics at the University of Toronto.
She's currently doing her 4th year research project in Computer Science with Lauren Erdman. She is
interested in the applications of machine learning in medicine. She aspires to pursue a degree in medicine;
she hopes to combine her background in computer science and her future training in medicine to improve
healthcare delivery and medical practice. In her free time, Parinita enjoys baking, swimming, and listening to podcasts.
Stan is an undergraduate, studying computer science and bioinformatics at the University of Toronto.
He is currently working with Lauren Erdman to improve prediction of obstructive hydronephrosis by integrating
the sequence of ultrasounds taken over recurring hospital visits. He is broadly interested in problems that
arise in data collection/preprocessing and modeling in a biomedical context. In his free time, he enjoys
playing video games and table tennis.
Xi recently graduated with a BSc from the University of Toronto in Computer Science and Mathematics, focused in Computer Vision
and Artificial Intelligence. He has been working with Sana Tonekaboni on the deployment of machine learning models in the ICU,
as well as with Alistair Johnson on de-identifying medical records. He enjoys seeing AI in use in healthcare and wishes to contribute
to the community. He is an avid art and film enthusiast, and loves walking around the city to capture hidden aesthetics on camera.
Awards: NSERC USRA
Addison grew up in Portland Maine, USA, and is an undergraduate researcher studying Computer Science
and Math at the University of Toronto. His current projects are in the area of representation learning
for medical time series data. He’s keen to explore the different ways modern deep learning methods can
be used to have profound impacts on patient lives. In his free time, Addison enjoys running, listening to
podcasts, and mentoring younger students.