Bo Wang is an assistant professor at Dept. of Medical Biophysics 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.
I am currently a third year computer engineering undergraduate with biomedical engineering minor at University of Toronto. I am interested in applying computer intelligence in bio-medical field. My topics including survival prediction, interactive single cell analysis and immune host response.
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!
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.”
Katayoon received her PhD in Bioinformatics from the University of British Columbia. She specialized in translational cancer genomics, identifying diagnostic and prognostic molecular biomarkers of cancer. She is passionate about transforming lives through the application of advanced technologies in healthcare.
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.
Xindi is a research student at UHN, and her research focus on natural language processing in healthcare and biomedicine. She did her master degree at the University of Western Ontario, and her thesis was dealing with large-scale multi-label biomedical documents classification. Her undergraduate was in mathematics at the University of British Columbia.
I did my undergraduate at University of Toronto majored in Pharmacology and Statistical Science. Then I shift my interest to from Biology to Machine Learning as I was motivated by the use of computational techniques to better understand what is needed in the industry. Now I am doing my Master of Science in Bioinformatics at Georgia Tech. Always, I picture myself as a problem solver/strategist rather than a data cruncher. And I love how there’s always more to learn!
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.