CV

Education

• Ph.D. in Computer Science, Concordia University, Montréal, Canada
  January 2022 – August 2026
  Thesis: Automatic Quantification of Medical Image Registration Quality Using Deep Learning

• M.Sc. in Electrical Engineering, Sharif University of Technology, Tehran, Iran
  September 2017 – September 2019
  Thesis: Fault Detection in Offshore Wind Turbines Using Data-Driven Methods

• B.Sc. in Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
  September 2013 – September 2017
  Thesis: Classification of Emotion Signals Using CNN and DNN Methods

Research Interests

• Deep Learning for Medical Image Analysis
• Medical Image Registration
• Anatomical Landmark Detection
• Uncertainty-Aware and Trustworthy AI
• Foundation Models and Self-Supervised Learning
• Agentic AI and Large-Scale Machine Learning

Work Experience

• April 2026 – Present: Data Scientist II
  Pathward, Sioux Falls, USA
  • Developed large-scale machine learning models for financial risk, transaction monitoring, and decision systems.
  • Built multiclass classification and anomaly detection systems using H2O Distributed Random Forest, XGBoost, KNN, and Isolation Forest on 100M+ records.
  • Applied SMOTE, class weighting, feature engineering, feature importance analysis, and error diagnostics to improve rare-event detection and model robustness.
  • Developed agentic AI tools using LangChain, LangGraph, and Agents SDKs for automated feature analysis, error analysis, and model diagnostics.
  • Worked with Snowflake, Snowpark, SQL, and large-scale data pipelines to support scalable analytical workflows.
• June 2025 – February 2026: Machine Learning Scientist
  Cleerly, Denver, USA
  • Developed machine learning algorithms for medical imaging behind an FDA-cleared product.
  • Built an end-to-end preprocessing pipeline for raw CCTA scans to support downstream deep learning workflows.
  • Designed and implemented deep learning models for coronary artery tree landmark detection, heart fingerprinting, and vessel stitching.
  • Developed non-rigid deep learning and point-cloud registration models to align coronary vessels using CCTA scans.
  • Trained and optimized large-scale deep learning models on AWS EC2 using Python, PyTorch, ITK, MONAI, NiBabel, and NumPy.
• April 2024 – September 2024: Research Scientist Intern
  Elekta, Montréal, Canada
  • Developed deep learning-based image registration for real-time tissue motion tracking in 2D-Cine MRI-guided radiotherapy.
  • Trained foundation models using Python, PyTorch, ITK, MONAI, NiBabel, and NumPy on NVIDIA A100 GPUs.
  • Achieved 12% improvement in image alignment using anatomical landmarks.
  • Contributed to a journal manuscript and a U.S. patent application based on the developed methodology.
• January 2022 – April 2026: Research Assistant / PhD Candidate
  Concordia University, Montréal, Canada
  • Conducted research on medical image registration, anatomical landmark detection, uncertainty estimation, and explainable medical AI.
  • Developed self-supervised and contrast-agnostic learning frameworks for anatomical landmark detection in brain MRI.
  • Developed uncertainty-aware registration error estimation methods for MRI-ultrasound registration.
  • Collaborated with clinical and industrial partners to design AI methods aligned with real-world medical imaging needs.
  • Mentored junior lab members and contributed to research projects involving LLMs, VLMs, and interpretable medical AI.
• June 2019 – January 2021: Head of AI Group
  Hami Holding, Tehran, Iran
  • Led the development of AI-based algorithmic trading systems.
  • Implemented preprocessing and temporal analyses for time-series data and stock price prediction.
  • Supervised a team of programmers on AI and backend data analysis.
• May 2017 – September 2017: Research Intern
  Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
  • Worked on EEG signal processing methods.
  • Gained experience in time-series analysis and feature extraction.

Publications

You can find a complete list of my publications on Google Scholar.

Selected publication venues include MICCAI, ICCV, IEEE TBME, MELBA, SPIE Medical Imaging, IEEE IUS, Information Fusion, and IJCARS.

Skills

• Programming Languages
  • Python, C/C++, MATLAB, Shell
• Machine Learning Frameworks
  • PyTorch, TensorFlow, Hugging Face Transformers, Keras, Scikit-learn, H2O.ai
• Medical Imaging & Computer Vision Tools
  • ITK, VTK, MONAI, TorchIO, SimpleITK, ANTs, 3D Slicer, MINC, NiBabel
• Data Processing & Visualization
  • Pandas, NumPy, Matplotlib, Seaborn, Power BI, Tableau
• Databases & Cloud Tools
  • Snowflake, SQL, MongoDB, MySQL, Oracle SQL, PostgreSQL, AWS EC2, AWS S3
• Development Tools
  • Docker, Git, VS Code, Jupyter, Jira, Confluence, Overleaf
• AI / LLM Tools
  • LangChain, LangGraph, Agents SDKs, LLMs, VLMs, prompt engineering

Honors and Awards

• ICCV Conference Travel Award, 2025
• Government of Québec BF2 FRQS Doctoral Fellowship, 2025
• Concordia University In-Course Graduate Bursary in Computer Science, 2025
• Concordia University CENPARMI Graduate Scholarship, 2025
• IEEE Transactions on Medical Imaging (TMI) Distinguished Reviewer, 2024
• Mitacs Accelerate Fellowship for Internship at Elekta, 2024
• Concordia University Carolyn Renaud Teaching Assistantship Award, 2024
• Concordia University Conference and Exposition Award, 2023
• Concordia University Travel Conference Support, 2023
• Image Processing Best Student Paper Award, SPIE Medical Imaging, 2023
• Concordia University Tuition Award of Excellence for International Students, 2021
• Ranked 5th among 34 students in Electrical Engineering, Control Group, Amirkabir University of Technology, 2017
• Top 1% in Iran’s national MSc entrance exam, among more than 30,000 participants, 2017
• Top 1% in Iran’s national BSc entrance exam, among more than 250,000 participants, 2013

Volunteering

• Peer reviewer for:
  • IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)
  • European Conference on Computer Vision (ECCV)
  • Neural Information Processing Systems (NeurIPS)
  • International Conference on Machine Learning (ICML)
  • International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
  • IEEE Transactions on Medical Imaging (TMI)
• Review records available at Web of Science.
• Member of the student committee at RBIQ-TransMedTech meeting and RBIQ Scientific Day, Quebec Bio-Imaging Network (QBIN), 2024