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Hassan Al-Hayawi

Data Scientist & Machine Learning Engineer

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About Me


• My work experience has provided me with strong research skills, particularly in statistics, machine learning, and analyzing large neuroimaging datasets across multiple imaging modalities.


• Machine Learning Experience: Highlighted through various projects in which I manage end-to-end machine learning pipelines, encompassing data processing, feature engineering, model development, model evaluation, and the deployment of scalable models in an operational environment.


• Extensive Background in Neuroimaging: Led multiple projects involving the collection, formatting, preprocessing, and analysis of MRI, fMRI, fNIRS, and EEG data. I developed and optimized ML models using functional neuroimaging data to improve prognostic capabilities in critical care settings and derive data-driven patient health insights.


• I thrive in collaborative environments where I can contribute to innovative solutions and team success.

Education

diploma

University of Western Ontario

London, ON., Canada | Sept. 2022 - Aug. 2024

M.Sc. - Cognitive, Developmental and Brain Sciences

• Thesis title: “Machine Learning for Prognosis of Acute Brain-Injured Patients in the ICU Using EEG Complexity Analysis and Naturalistic Narrative Stimuli”

| View Thesis |

• Supervisors: Dr. Adrian Owen & Dr. Derek Debicki

King’s University College

London, ON., Canada | Sept. 2018 - Apr. 2022

B.A. - Honours Specialization in Psychology

• Thesis title: “Cortical Function of Super Refractory Status Epilepticus: An fMRI Case Study”

• Supervisor: Dr. Loretta Norton

Experience

Technical AI Researcher

Toronto, ON., Canada

M31 AI (startup)

  • Designed and executed large-scale GPU-accelerated experiments on HPC infrastructure.
  • Development of medical imaging ML workflows and deep learning architectures for 3D MRI analysis.
  • Predicting AVM recurrence in pediatric patients as part of a collaboration with the SickKids Hospital.

Skills: Deep Learning · Neural Networks (ResNet & Transformer based models)

Machine Learning Research Assistant

London, ON., Canada

The Haeryfar Lab

  • Implement and refine a transfer learning–based convolutional neural network to fully automate the detection and scoring of liver inflammation and fibrosis in histology slides of a preclinical mouse model.
  • Leverage automated patch-based classification to replace labor-intensive manual scoring, increasing speed and consistency in histopathological evaluation of liver tissue.

Skills: CNN · Histology data · Pathophysiology

Research Analyst

London, ON., Canada

LHSC - London Health Sciences Centre

  • Investigated temporal brain patterns in critical care patients using time-series data from various functional neuroimaging modalities, such as fNIRS, fMRI, & EEG.
  • Made data-driven insights with machine learning techniques to enhance prognostic and diagnostic capabilities in the ICU.

Skills: Quantitative Research · Deep Learning · Statistics · Machine Learning · Feature Engineering · Functional Neuroimaging

Data Analyst

London, ON., Canada

The Owen Lab

  • Statistical Data Analyst for lab research.
  • Preprocessed & analyzed large high-dimensional datasets with advanced techniques for noise reduction, artifact removal, signal enhancement, statistical testing, optimization algorithms, feature extraction, & data visualization.
  • Utilized PCA for dimensionality reduction & ICA to eliminate artifacts & isolate meaningful patterns in the data.

Skills: Data Processing · Advanced Statistical Methods · Machine Learning · Optimization · Research Skills

Teaching Assistant

London, ON., Canada

Western University

  • Independently led tutorials for the following courses: Research Methods in Psychology (PSYCH 2801); Psychology as a Social Science (PSYCH 1003); Psychology as a Natural Science (PSYCH 1002).
  • Instructed students to work with data, conduct and test hypotheses, and visualize and interpret data effectively.
  • Collaborated with the course coordinator to align teaching and marking strategies with course objectives.
  • Organized and moderated online discussion forums, facilitating peer-to-peer knowledge sharing and collaborative learning.

Skills: University Teaching · Fundamental Research Skills

Best Buy

London, ON., Canada

Advanced Repair Agent

  • Documented and fixed issues with client laptops and desktop computers.
  • Maintained record of daily data communication transactions, problems and remedial actions taken, and installation activities.
  • Trained users in the proper use of hardware and/or software.

Skills: Problem-solving · Technical Skills · Customer Service · Documentation

Kognitive Sales Solutions

Windsor, ON., Canada

Team Lead & Field Marketing Representative

  • Led a high-performing sales team during promotional events, driving product visibility and customer engagement.
  • Initially hired as a Field Marketing Representative, exceeded sales targets, and was promoted to Team Lead.
  • Achieved recognition for consistently ranking at the top of the leaderboard for most sales, demonstrating exceptional sales skills and a commitment to team success.

Skills: Leadership · Collaboration · Customer Service · Sales

Skills

Projects

(update coming soon)

Deep learning prediction of AVM recurrence in children (project with SickKids Hospital)

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CNN for Binary Image Classification (Computer Vision)

• Model: Developed a model to automatically classify X-rays images of pneumonia-affected lungs, distinguishing them from normal lungs.

• Achieved 81% accuracy and an F1 score of 83.8%.

• Workflow: Fine-tuned a pre-trained CNN in PyTorch through data manipulation and transfer learning techniques for better classification while minimizing computational resource usage. Delivered modular scripts and clear documentation for data loading, training, and evaluation to streamline reproducibility and enable collaboration.

View Project

UNET Image Segmentation of MRI Mouse Kidneys and Bladders (Computer Vision)

• Manual segmentation of MRI images is costly and labour-intensive; a single dataset requires months of tedious work to complete. Automating this laborious task significantly improves the workflow and efficiency of preclinical research.

• Developed and implemented a U-NET convolutional neural network to automate MRI image segmentation for kidney and bladder volume analysis in oncological mouse models, reducing manual processing time from months to minutes while maintaining high accuracy.

• Collaborated with immunology researchers to confirm the accuracy of the segmentation results.

• The model consistently produced results like those obtained by experienced manual segmenters, with a mean difference of only 2-5%. Thus, the model reliably assesses cancer progression in preclinical studies.

View Project

EEG Complexity for Prognosis of ICU Patients

• Used various algorithms to perform binary classification on EEG complexity features to assess ICU patient prognosis.

• Used explainable statistical techniques to improve the transparency and interpretability of the models.

• Evaluated model performance using cross-validation techniques and metrics, including accuracy, precision, recall, and area under the ROC curve (AUC).

• Results showed 80% accuracy in predicting a patient's future clinical outcome using EEG recording from early ICU admission (AUC = 0.80–0.83).

| View Thesis | View Github |

Simultaneous EEG-fNIRS to Model HRF of Brain-Injured Patients

• Awarded a Provincial Scholarship (OGS) for my written proposal to more accurately estimate the hemodynamic response function (HRF) of patients using a gradient descent-based search algorithm that maximizes the Pearson correlation between the recorded fNIRS and EEG signals.

• This method will vastly improve the accuracy and sensitivity of functional neuroimaging results as it accounts for important underlying physiological mechanisms.

View Project

Awards & Distinctions

wrestling

Publications

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