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.
Department of Laboratory Medicine and Pathobiology
Department of Computer Science
Peter Munk Cardiac Centre
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.
Hi! I am a PhD student in biomedical engineering. I currently work on using machine learning methods to analyze donor lung X-ray images. During my spare time, I enjoy trivia, board games, piano, and singing.
I work on integrating diverse biological networks using graph neural networks in order to discover new subsystems in the cell.
I am currently a MSc Medical Biophysics student, cosupervised by Dr. Bo Wang and Dr. Benjamin Haibe-Kains. I am interested in using machine learning algorithms to analyze characteristics of cell populations and predict drug response.
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.
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.
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.
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.”
Jun Ma is currently a Postdoctoral Fellow at University of Toronto. He received his Ph.D. degree in Mathematics at Nanjing University of Science and Technology. His research interests focus on medical image segmentation, including variational models, deep learning, and domain adaptation. He has published six first-author papers on top journals, such as TPAMI, TMI, MedIA, and SIIMS. He has won the champion of three international medical image analysis challenges (QUBIQ, EMIDEC, ADAM). He is the lead organizer of MICCAI 2021 FLARE Challenge.
Kaden completed a HBSc specializing in Computer Science from the University of Toronto and is currently a full-time researcher at the Toronto General Hospital Research Institute. His research interests center around medical diagnosis using clinical and medical imaging data, and consequently overcoming the practical limitations encountered in such environments.
I’m a Master’s student in the department of Laboratory Medicine and Pathobiology, doing my thesis at WangLab. Prior to this, I graduated from McMaster for a BHSc Health Sciences degree. My work will focus on spatial-omics in pathology.
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.
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’.
I am a Post-doctoral Fellow at University of Toronto, working with Wang lab and Cyclica Inc., an AI-Based Drug Discovery Company. My research is focused on graph-based machine learning algorithms that can better predict proteins’ biological functions from their 3D atomic structure.
I am a Computer Science MSc student at UofT with experience in Drug Discovery, Wearable Robotics, and Pricing domains. Specialize in GraphML, NLP, and Time-Series. Currently doing Graph-based Molecular Generation with Conditional Diffusions.
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.
Paola is an MSc candidate in the Department of Laboratory Medicine and Pathobiology (LMP), supervised by Dr. Bo Wang. Her research focuses on the intersection of cardiovascular disease and medical imaging, whilst utilizing machine learning. She obtained her HBSc in Mathematics, Chemistry, and Italian at the University of Toronto in 2022. Outside of the lab, she enjoys: badminton, reading, baking, and exploring coffee shops in Toronto.
Rashmi is a cardiac surgery resident interested in the use of machine learning algorithms in echocardiography automation, disease detection in cardiovascular imaging and outcome prognostication.
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.
Roman is a 4th year undergraduate researcher from Ukraine. He has 2 years of research & industry experience in Deep Learning, including Computer Vision and Time-Series forecasting systems. His current research interests lay within the intersection of Brain-Computer Interfaces, Unsupervised Learning, and Differential Privacy. He is also applying for a Doctoral program at the University of Toronto.
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.
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.
|Fatemeh Darbeha (Master, now Layer 6)|
|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)|
|Jesse Sun (Undergrad, now University of Waterloo)|
|Karthik Bhaskar (Master, now at CIBC)|
|Mark Zaidi (Ph.D at UofT)|
|Mehran Karimzadeh (Post-doctoral fellow, now at Exai Bio)|
|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)|
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