Mahsa obtained her PhD in Biomedical Engineering from the University of British Columbia in 2021. In her PhD research, she integrated machine learning algorithms in state-of-the-art mobility assistive technologies to design and develop personalized and context-aware assistive devices (you can read more about her Ph.D. research here). She joined the Implantable Biosensing Laboratory (IBL) and the Human Motion Biomechanics Laboratory (HuMBL) in 2022 as a Postdoctoral Research Fellow and is co-supervised by Dr. Babak Shadgan, Dr. Calvin Kuo, and Dr. Brian Grunau. Mahsa’s research is focused on developing wearable systems for early detection of out-of-hospital sudden cardiac arrest (SCA). This work, which is part of the CanSAVE project, involves the development of multimodal sensor technologies and machine/deep learning models for accurate SCA detection.
Education
- PhD, Biomedical Engineering, The University of British Columbia, Vancouver, British Columbia, Canada (2017–2021)
- MASc, Biomedical Engineering, The University of British Columbia, Vancouver, British Columbia, Canada (2014–2016)
- MASc, Mechanical Engineering, Sharif University of Technology, Tehran, Iran (2010–2012)
- BSc, Mechanical Engineering, Sharif University of Technology, Tehran, Iran (2006–2010)
Awards
- Best Student Paper Award (IEEE International Conference on Automation Science and Engineering, 2021)
- Healthcare Robotics NSERC Fellowship (UBC, 2020-2021)
- Killam Graduate Teaching Assistant Award (UBC, 2020)
- Four Year Doctoral Fellowship (UBC, 2019 – 2021)
- The Natural Sciences and Engineering Research Council of Canada Postgraduate Scholarships (UBC, 2019 – 2021)
- Best Student Paper Award (Rehabilitation Engineering and Assistive Technology Society of North America Conference, 2017)
- Dean’s Honour List and High Distinction (SUT, 2010)
- Best Undergraduate Thesis Award (SUT, 2010)
Current Projects
- Wearable technologies for cardiac arrest detection
- Modelling the cardiovascular system PPG and ECG measurements
Interests
BiosigWearable Sensors, Biosignal Processing, Machine Learning, Assistive Technologies, Robotics with Applications in Rehabilitation, Control Systems.