Experience & Education

Research Professional, CRCHU de Québec (part-time, remote)

Laboratoire d'Uro-Oncologie Expérimentale · Supervisor: Dr. Frédéric Pouliot · Sep 2026 – Present

Machine learning and statistical methods for translational prostate cancer research, continuing the collaboration that produced my first-author review in Cancers (2026).

Current work

• Develop machine-learning and statistical methods for translational prostate cancer research using clinical, pathology, and molecular data.
• Extend and validate patient-similarity graph neural networks for prostate cancer outcome and survival prediction on the PROCURE cohort.

Related project: Patient-Similarity GNNs for Prostate Cancer Survival Prediction

Research Intern, IAEA Dosimetry Laboratory, Vienna

Division of Human Health · IAEA Marie Skłodowska-Curie Fellow · Sep 2025 – Aug 2026

Worked on radiotherapy dosimetry, postal dose-audit workflows, quality assurance, and audit-data analysis within the IAEA Dosimetry Laboratory.

Selected work & learning

• Developed Python-based independent dose-verification software for RPLD/OSLD postal dosimetry audits.
• Contributed to QUATRO audit-data analysis and quality-indicator development for radiotherapy centres.
• Gained hands-on exposure to dosimetry-lab workflows: ionization chamber calibration, reference dosimetry, irradiation procedures, and audit-result evaluation.
• Hands-on training in Co-60, HDR Ir-192, LINAC photon/electron, and orthovoltage X-ray dosimetry.

Related projects: Dose Evaluation Software · QUATRO Quality Indicators

Graduate Researcher, CRCHUQ / Université Laval, Québec

2023 – 2025

Conducted MSc research at the intersection of medical physics, oncology, and machine learning, focusing on AI-based prostate cancer prognosis.

Selected work

• Developed and evaluated patient-similarity graph neural networks for multi-outcome prostate cancer survival prediction.
• Benchmarked GNNs against classical survival models (Cox proportional hazards, Random Survival Forest).
• Built leakage-free graphs with nested cross-validation, and added SHAP-based explainability.
• Prepared a first-author manuscript based on the thesis research.

Related project: Patient-Similarity GNNs for Prostate Cancer Survival Prediction

AI Developer, Dentai, Healthcare-AI Startup

2020 – 2023

Developed deep-learning and computer-vision pipelines for dental and oral medical imaging applications in an industry R&D environment.

Selected work

• Developed algorithms for oral and dental disease-pattern detection.
• Built pattern-recognition systems for identifying clinically relevant visual patterns.
• Contributed to generative-model R&D for medical imaging applications.
• Worked on deep-learning workflows for high-resolution imaging projects.

Related project: Dental Imaging AI

Quebec Scientific Entrepreneurship Program (QcSE)

Fall 2026 cohort · Selected participant · In progress

Scientific entrepreneurship training for researchers, supported by the Fonds de recherche du Québec.

MSc Medical Physics, CAMPEP-accredited program

Université Laval, Canada · 2023 – 2025 · Degree conferred June 2026

Thesis: Graph neural networks and multi-task survival modeling for prostate cancer prognosis. Supervisor: Prof. Louis Archambault.

MICCAI-labeled Summer School on Deep Learning for Medical Imaging

ÉTS, Canada · 2024

Training in deep learning methods for medical imaging, including practical and research-oriented applications.

BSc Engineering Physics

Alzahra University, Iran · 2016 – 2021