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Bo Wang

Dr. Bo Wang holds a joint tenure-track position as Assistant Professor within the Departments of Laboratory Medicine and Pathobiology and Computer Science at University of Toronto. Dr. Bo Wang is the Chief Artificial Intelligence Scientist at the University Health Network (UHN). He is also a CIFAR AI Chair at Vector Institute, Toronto.

Dr. Bo Wang obtained his PhD from the Department of Computer Science at Stanford University. His PhD work covers statistical methods for solving problems in computational biology with an emphasis on integrative cancer analysis and single-cell analysis.

Dr. Bo Wang’s long-term research goals aim to develop integrative and interpretable machine learning algorithms that can help clinicians with predictive models and decision support to tailor patients’ care to their unique clinical and genomic traits.

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Adamo Young

I am a computer science PhD student, cosupervised by Dr. Wang and Dr. Hannes Rost. I develop new deep learning methods for mass-spectrometry based metabolomic analysis.

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Chloe Wang

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Emmy Fang

I received my BASc. in Engineering Science from the University of Toronto in 2021. I'm currently a MSc. student in Department of Computer Science, University of Toronto. My research interest is on privacy-preserving collaborative machine learning and its application on healthcare.

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Haotian Cui

Haotian received the B.S. and M.S. degree in Biomedical Engineering from the Tsinghua University, China in 2015 and 2019. He is currently pursuing the Ph.D. degree at University of Toronto. His current research interests include computer vision, computational biology and machine learning.

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Hassaan Maan

My research involves the development and application of machine learning and computational biology methods in single-cell genomics, particularly on integrating disparate and multi-modal single-cell datasets.

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Jun Ma

Jun Ma is a Machine Leaning Lead at University Health Network AI Hub. His research interests focus on developing cutting-edge algorithms for accurate and efficient biomedical image parsing, with the goal of enabling precise cancer quantification and personalized patient care. His work has been published in top journals, including Nature Methods, Lancet Digital Health, Nature Communications, and TPAMI. He also has won the top three in over 10 international medical image analysis challenges as the first author. He has organized multiple international workshops and competitions at top tier conferences (e.g., CVPR, NeurIPS, and MICCAI). His contributions have had a significant impact on the field, with over 10,000 citations and an H-index of 31 according to Google Scholar. His open-source projects have garnered more than 10,000 stars on GitHub.

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Kaden McKeen

Kaden McKeen is currently a PhD candidate supervised by Dr. Wang. His research focuses on innovative machine learning techniques surrounding foundation models, multimodal integration, and longitudinal modeling, for clinical applications relating to physiological signals, medical imaging, and clinical text.

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Mica Consens

Mica completed her undergrad in Computer Science, Bioinformatics and Biology at U of T in June 2021. She is currently a Computer Science Ph.D. student at U of T with a Focus in Machine Learning Applications for Healthcare in the Wang Lab. She is interested in developing novel computational methods to investigate biological questions whose answers could provide key insight into understanding human molecular machinery, and consequently into how we are ‘built’.

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Phil Fradkin

Hello, I’m a research student interested in developing deep learning models for investigating impact of genomic variation in humans. After graduating undergrad at UofT in computer science and bioinformatics, I worked for a few years at a Toronto startup, Deep Genomics. This ignited my curiosity in the possibility of utilizing neural networks for connecting genotypic variation to phenotypic outcomes.

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Zeinab Navidi

Zeinab completed her BSc in Computer Engineering at the Sharif University of Technology and recently defended her MSc in Artificial Intelligence field. Currently, she is working as a summer research student in Machine Learning and Computational Biology at Wang's lab and her main focus are on single-cell data analysis. Single-cell is one of the hottest areas in computational biology and she is interested in developing novel practical tools using machine learning applications.

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Rex Ma

Rex received his BASc in Computer Engineering at the University of Toronto in 2020. He is currently a PhD candidate in Computer Science at the University of Toronto. His research interests lie in multi-modality integration in healthcare and biology domain using deep learning.

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Ronald Xie

Ronald received his BSc in Microbiology and Immunology at the University of British Columbia in 2018. He then received his MPhil in Computational Biology at the Department of Applied Mathematics and Theoretical Physics at University of Cambridge in 2019. Ronald is currently a PhD candidate in Computational Biology and Molecular Genetics (CBMG) at the Faculty of Medicine at University of Toronto. His research interests lie in deep learning applications in electron microscopy and single cell omics.

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Vivian Chu

Vivian obtained her BSc at the University of Waterloo in Molecular Genetics and Bioinformatics. She is currently a Medical Biophysics PhD student, co-supervised by Dr. Bo Wang and Dr. Hansen He. She is interested in applying and developing machine learning methods for multi-omic integration and RNA-based therapeutic design in cancer.