NCBL
NCBL

University of alberta

Sara Houshmand

 ABOUT ME


I am a highly motivated machine learning professional with a strong background in signal processing. My passion lies in applying ML and DL techniques to solve complex problems in the healthcare field. I hold M.Sc. and Ph.D. degrees in Mechatronic Engineering and Mechanical Engineering from K. N. Toosi University, obtained in 2015 and 2021, respectively. I have gained valuable research experience as a visiting researcher in the Health Science, and as an EEG researcher in York University. Currently, I am a Postdoctoral fellow researcher at the NCBLab, University of Alberta.

Supervisor

  • Dr. Hossein Rouhani

Postdoctoral Fellow (Mar 2023 - Present)

NCBL, University of Alberta

Project Name: Exploring Solutions to Enhance Accessibility in Canada's National Parks: A Machine Learning Approach

  • Applied cutting-edge machine learning and natural language processing techniques for Exploring Solutions to Enhance Accessibility in Canada's National Parks

Researcher (Mar 2022 - 2023)

Dr. Joseph DeSouza's Lab, York University

Project Name: Investigating Neuroplasticity and Long-Term Effects of Dance Training in Patients with Parkinson's Disease (PwPD)

  • Applied EEG signal processing and feature extraction techniques to measure brain activity and changes

  • Analyzed the impact of multisensory active exploration during dance training on cognitive and motor functions

Researcher (Apr 2021 - 2022)

Health and Technology District

Project Name: Innovative Dementia Detection Using Machine Learning and EEG Signals

  • Employed machine learning techniques to classify EEG signals of elderly patients with and without dementia

  • Worked with event-related potentials and extracted features related to cognitive and memory functions

Research Assistant (2016 - 2021)

Virtual Reality Lab, K. N. Toosi University of Technology

Project Name: Designing a New Cognitive Qualification System of Drivers: A National Project with collaboration of Iran Cognitive Science and Technologies Council

  • Conducted sleep-related tests such as polysomnography, MWT, and MSLT with sleep disorder patients and healthy drivers

  • Worked with video monitoring and Observer Rating of Drowsiness (ORD) process to assess driver's alertness

  • Employed EEG signal processing and feature extraction techniques to track brain activity and changes during the transition from alertness to drowsiness

  • Developed a new approach for early driver drowsiness detection using only a single channel based on alpha spindles in brain data

  • Employed various machine learning techniques, including K-nearest neighbors (KNN), support vector machines (SVM), and deep learning methods such as convolutional neural networks (CNN), to analyze the data and extract meaningful insights

  • Followed clinic policies and recorded test results securely

PUBLICATIONS

  1. "A Novel Convolutional Neural Network Method for Subject-Independent Driver Drowsiness Detection based on Single-channel data and EEG Alpha Spindles", Sage. Part H: Journal of engineering in medicine, 2021

  2. "An Efficient approach for driver drowsiness detection at moderate drowsiness level based on electroencephalography signal and vehicle dynamics data", Journal of Medical signals & sensors, 2022

  3. "Classifying features of electroencephalography signal to detect driver drowsiness in the early drowsy stage", Journal of sleep, 2021

  4. "Cortical Modulation Resulting from Long-term Dance Training in Parkinson’s disease: Evidence from fMRI and resting state EEG", Society for Neuroscience, San Diego 2022

  5. "Modulations of brain signals through multisensory active exploration during dance training in people with Parkinson's disease (PwPD): An observational study"

  6. "Dementia Detection in Elderly Patients: A Novel Clinical Application of Machine Learning and EEG Signals for Early Detection" (In preparation)

  7. "Deep-MCICA, Novel deep learning method for head-motion correction in fMRI data" (In preparation)

Teaching Experience at Islamic Azad University, West Tehran Branch (WTIAU) (2015 - 2016)

  • Thermodynamics 1: Imparted knowledge and facilitated learning in the field of thermodynamics, covering fundamental concepts and principles related to energy and its transformations

  • Mechanical Engineering Design Elements: Guided students in understanding the fundamental elements of mechanical engineering design, emphasizing the application of engineering principles in designing various mechanical components and systems

Certificate

MIT Schwarzman College of Computing:

“Data Science and Machine Learning: Making Data Driven Decisions”, (Jan - Apr 2023)