Farzad Beizaee

Researcher in Computer vision, Deep learning, and Medical image analysis

Resume

About Me

A PhD candidate with a strong passion for deep learning and computer vision. Experienced in developing and applying advanced machine learning techniques across diverse domains, including different learning-based models, medical image analysis, anomaly detection, and generative models. Proficient in designing and implementing innovative solutions to complex problems, with a proven track record of impactful research and practical applications.

Experience

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Zebra Technologies

Computer vision internship - -

Developing Character Detection for OCR.

  • Modifying real-time object detection network architecure.
  • Analytic tests and validations.
  • Generating synthetic data.
pytorch
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BARAI startup

Computer vision scientist - -

Wide range of different AI soloutions.

  • Clothes visual search and image retrieval.
  • Face authentication.
  • Multi-task attribute tagging.
  • Persian OCR.
  • Acne detection.
pytorch

VIDA startup

Computer vision scientist - -

Full face authentication.

  • face detection and recogntion.
  • spoof detection.
  • aliveness and blink detection.
pytorch
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Sharif University of Technology

Teaching assistantship - -

Teacher assistant in different courses in CE department.

  • Advanced 3D computer vision.
  • Deep learning.
  • Machine learning.
  • Fundamental of programming.
pytorch

Projects

Clothes visual search and tagging

Industrial anomaly detection

Brain anomaly detection

MRI harmonization

Neonatal brain assessment

Face authentication

RGB-D Human action recognition

Optical chararcter recognition

EEG-Based driver fatigue detection

Education / Training

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École de technologie supérieure (ÉTS Montréal)

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Ph.D. in computer science, Medical Image Analysis field.

  • Thesis: Neonatal brain assessment using deep neural network.
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Sharif Universiry of Technology

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M.Sc. in computer engineering and artificial intelligence and robotics major.

  • Thesis: Human action recognition from RGB-D videos using deep neural networks.
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Shiraz Universiry of Technology

Electrical engineering - Control - -

B.Sc in Electrical engineering and contol major.

  • Thesis: Myocardial infarction diagnosis with ECG data.

Publication

CVPR2025

doi.org/10.48550/arXiv.2503.19357

Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection
  • Farzad Beizaee, Gregory A. Lodygesnky, Christian Desrosiers, Jose Dolz.

doi.org/10.48550/arXiv.2503.04953

Spectral Informed Mamba for Robust Point Cloud Processing
  • Ali Bahri, Moslem Yazdanpanah, Mehrdad Noori, Sahar Dastani, Milad Cheraghalikhani, David Osowiechi, Gustavo Adolfo Vargas Hakim, Farzad Beizaee, Ismail Ben Ayed, Christian Desrosiers.

doi.org/10.48550/arXiv.2503.06369

Spectral State Space Model for Rotation-Invariant Visual Representation Learning
  • Sahar Dastani, Ali Bahri, Moslem Yazdanpanah, Mehrdad Noori, David Osowiechi, Gustavo Adolfo Vargas Hakim, Farzad Beizaee, Milad Cheraghalikhani, Arnab Kumar Mondal, Herve Lombaert, Christian Desrosiers.

MedIA

doi.org/10.1016/j.media.2025.103483

Harmonizing Flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization Medical Image Analysis.
  • Farzad Beizaee, Gregory A Lodygensky, Chris L Adamson, Deanne K Thompson, Jeanie LY Cheong, Alicia J Spittle, Peter J Anderson, Christian Desrosiers, Jose Dolz.

IPMI2025

doi.org/10.48550/arXiv.2502.16943

MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection.
  • Farzad Beizaee, Gregory A. Lodygesnky, Christian Desrosiers, Jose Dolz.

IPMI2023

doi.org/10.48550/arXiv.2301.11551

Harmonizing Flows: Unsupervised MR harmonization based on normalizing flows.
  • Farzad Beizaee, Christian Desrosiers, Gregory A. Lodygesnky, Jose Dolz.

Scientific Reports

doi.org/10.1038/s41598-023-40244-z

Determining regional brain growth in premature and mature infants in relation to age at MRI using deep neural networks.
  • Farzad Beizaee, Michele Bona, Christian Desrosiers, Jose Dolz, Gregory Lodygensky.

Neurips2024

doi.org/10.48550/arXiv.2406.13875

WATT: Weight Average Test-Time Adaptation of CLIP.
  • David Osowiechi, Mehrdad Noori, Gustavo Adolfo Vargas Hakim, Moslem Yazdanpanah, Ali Bahri, Milad Cheraghalikhani, Sahar Dastani, Farzad Beizaee, Ismail Ben Ayed, Christian Desrosiers

WACV2025

doi.org/10.48550/arXiv.2411.01116

Test-Time Adaptation in Point Clouds: Leveraging Sampling Variation with Weight Averaging.
  • Ali Bahri, Moslem Yazdanpanah, Mehrdad Noori, Sahar Dastani Oghani, Milad Cheraghalikhani, David Osowiech, Farzad Beizaee, Ismail Ben Ayed, Christian Desrosiers

Contact