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Our Research Group

Principal Investigator

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Dr. Owais Khan

Lab Director

Dr. Owais Khan, Ph.D.

Assistant Professor

Department of Electrical, Computer & Biomedical Engineering

Toronto Metropolitan University

owaiskhan@ryerson.ca

Education:

  • B.A.Sc., University of Toronto, 2011

  • M.A.Sc., University of Toronto, 2013

  • Ph.D., University of Toronto/University of Oslo, 2017

  • Postdoc, Stanford University, 2020

Dr. Owais Khan became interested in cardiovascular biomechanics while taking a 4th year fluid mechanics course, and went on to do a masters and PhD in fluid mechanics of cardiovascular systems. He won a competitive scholarship to pursue a joint PhD program between the University of Toronto and Simula Research Lab (Norway) at the University of Oslo. In his Ph.D., Dr. Khan developed computational fluid dynamics methods to model blood flow in brain aneurysms. In the last year of his PhD, Dr. Khan won the MITACS Globalink Research Award that allowed him to intern at the University of Montpellier (France) to explore large-eddy simulation models for application in turbulent cardiovascular flows. After graduating, Dr. Khan joined the Stanford University School of Medicine, funded by the NSERC Postdoctoral Fellowship, and prestigious American Heart Association Fellowship. While at Stanford, Dr. Khan developed computational and imaging analysis methods for coronary artery disease and heart bypass patients. 


Graduate Students

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Anahita Abbasnejad

Ph.D Student

Anahita Abbasnejad

Ph.D Student

Department of  Biomedical Engineering

Toronto Metropolitan University

LinkedIn

Education:

  • B.Sc., Amirkabir University of Technology, 2018

  • M.Sc., Babol Noshirvani University of Technology, 2021

Anahita earned her B.Sc in biomedical engineering, primarily working on circuit design to measure biomedical signals. During her Masters, Ana focused in array processing and medical ultrasound image reconstruction methods. She joined CIMBL in Fall 2021, and is currently engaged in research on CTA image analysis coupled with computational fluid dynamics simulations. Her research aims to create computational methods for measuring blood flow and detecting coronary artery disease in heart disease patients.

 

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Arman Aghaee

Ph.D Student

Department of Biomedical Engineering

Toronto Metropolitan University

Education:

  • B.Sc., Hamedan University of Technology, 2017

  • M.Sc., Sharif University of Technology, 2018

Arman Aghaee was born in Shiraz, Iran. He completed his master's degree in the bioelectric area in the department of electrical engineering at Sharif University of Technology. His research interests are in deep learning, image processing, and data science. Arman will be joining CIMBL in Winter 2022.

Arman Aghaee

Ph.D Student

Undergraduate Students

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Vivian Tan

Vivian Tan

Undergraduate Student

Department of Electrical, Computer & Biomedical Engineering

Toronto Metropolitan University

LinkedIn

Education:

  • B.A.Sc., Toronto Metropolitan University, 2023

 

Vivian is an upper year undergraduate student in biomedical engineering at Ryerson University, interested in specializing in image processing, biomechanics, and computational fluid dynamics. For summer 2022, she was awarded the Ryerson URO award. She currently works in computational fluid dynamics and 4D flow MRI in collaboration with the Hospital for Sick Children. 

Research Assistant

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Kayla Javadifar

Research Assistant

Kayla Javadifar

Undergraduate Student

Department of Electrical, Computer & Biomedical Engineering

Toronto Metropolitan University

LinkedIn

Education:

  • B.A.Sc., Toronto Metropolitan University, 2024

 

Kayla is an upper-year undergraduate student, working towards earning her Bachelor's degree in biomedical engineering at the Toronto Metropolitan University. During Summer 2021, she modeled the coronary artery bypass grafts using image segmentation computational fluid dynamics. For Summer 2022, she was awarded the prestigious NSERC URO Award. She is developing physiological models of healthy and diseased heart valves using immersed boundary methods. 

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