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. He leads the AI team for Peter Munk Cardiac Centre (PMCC) at University Health Network (UHN). He is also a CIFAR AI Chair at Vector Institute, Toronto.
Bo obtained his PhD from the Department of Computer Science at Stanford University, and has extensive industrial research experience at many leading companies such as Illumina and Genentech. His PhD work covers statistical methods for solving problems in computational biology with an emphasis on integrative cancer analysis and single-cell analysis.
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.
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.
At a high-level, my research focuses on machine reading of biomedical literature and clinical notes. More specifically, this involves developing methods for the major components of text-mining and information extraction (IE) namely: named entity recognition (NER), named entity linking (NEL), and relation/event extraction (RE). The end goal is to develop a neural end-to-end system for machine reading of biomedical literature and clinical notes and to make the system freely available as an open-source tool.”
I’m Jesse! I’m currently a summer undergraduate research student that will be entering my second year in Computer Science at the University of Waterloo. This summer, I’ve received the incredible opportunity to work with Dr. Wang’s lab - more specifically on the medical imaging team. My research is centred around diagnostic and segmentation models for medical images such as MRI/CT scans. Furthermore, given the fact that most data in the real world is unlabeled, we are very much interested in developing semi-supervised learning methods for these models. There is a surprisingly high demand for computer aided devices for diagnosing cardiac diseases and segmenting classes of interest, and I couldn’t be more excited to be working on this project during my short stint this summer term!
Currently a Research Associate with the Wang Lab, I am a recent MSc graduate of the University of Toronto’s Health Services Research program, with a focus in Health Service Outcomes and Evaluation. Presently, my research is centered on the application of machine learning methods to healthcare data, in particular cardiology.
Lin received her HB.A in Statistics and B.A in Economics from University of California, Berkeley in 2012, and received her M.A in Applied Statistics from University of California, Santa Barbara in 2015. Lin is currently a PhD candidate in Statistics Department at University of Toronto. She works as a research student in Wang’s lab and her research focuses on machine learning methods for analyzing single-cell data.
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.
Rex is currently a Computer Science Ph.D. student at the University of Toronto. He is a creative researcher and experienced software engineer. Rex is especially good at coding and mathematics, and his research focuses on machine learning, AI in healthcare, and computation biology.
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.
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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|Claire Luo (Undergrad, now Master at Columbia University)|
|Hossein Mousavi (Post-doctoral Fellow, now Circle Neurovascular Imaging)|
|Ines Birimahire (Master, now at H4H Humans4Help)|
|Ivy Yuan (Undergrad, now Master at ETH Zurich)|
|Karthik Bhaskar (Master, now at CIBC)|
|Katayoon Kasaian (Scientific Associate)|
|Mark Zaidi (Ph.D at UofT)|
|Osvald Nitski (Undergrad, now at General Motors)|
|Sayan Nag (Ph.D, now Toronto startup)|
|Shun Liao (Ph.D at UofT)|
|Xindi Wang (Master, now PhD at Westrn University)|
|Yini Yang (Master, now Adobe)|
|Yuchen Wang (Undergrad, now Master at Stanford University)|
|Zhiyong Dou (PhD at Huazhong University of Science and Technology, China)|