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.

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.

Bonnie Chao

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.

Chloe Wang

Duncan Forster

I work on integrating diverse biological networks using graph neural networks in order to discover new subsystems in the cell.

Emily So

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.

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.

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.

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.

John Giorgi

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

Jun Ma is a Machine Leaning Lead at Canada's No. 1 hospital University Health Network (UHN). His research interests include medical vision and machine learning. His first-author 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. In addition, he has organized multiple international competitions, such as MICCAI 2021-2024 FLARE Challenge, NeurIPS 2022 Cell Segmentation Challenge, and CVPR 2024 MedSAM on Laptop Challenge. His work has been cited over 7000 times according to Google Scholar and has an H-index of 25. His GitHub projects have garnered over 10,000 stars.

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.

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’.

Laura Oliva

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 Zhang

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.

Oleksii Tsepa

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.

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.

Paola Driza

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.

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.

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.

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.

Roman Burakov

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 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.