Saeed Shakuri

Saeed Shakuri

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Last Updated: Apr. 2026

About me

I obtained my master's degree in Data Science at the University of Science and Culture, Tehran, Iran, advised by Dr. Alireza Rezvanian. My research interests broadly lie in Deep Learning within real-world applications, including but not limited to medical and healthcare domains, with some emphasis on Multimodal Learning, Computer Vision, and Natural Language Processing (including Large Language Models), while examining their Explainability and Interpretability. My thesis focused on Computer Vision methods (Object Detection) in detecting lung nodules for lung cancer in CT scan images.

Research interests:

Deep Learning, Multimodal Learning, Computer Vision, Medical Applications in Deep Learning

Education

• University of Science and Culture, Tehran, Iran
  M.Sc., Data Science (GPA 4 out of 4 or 19.05 out of 20)
  Thesis: Object detection using Few-shot Learning and Vision Transformers.
  Thesis grade: Excellent.

  2021 - 2025
• Technical and Vocational University, Tehran, Iran
  B.E., Electronics Engineering (Shamsipour College)
  Project: A smart house project with an electronic device and a mobile application for monitoring and controlling household environmental conditions and switches via WiFi and a mobile application.

  2018 - 2021
• Technical and Vocational University, Karaj, Iran
  AS, Electronics Engineering (Beheshti College)

  2016 - 2018

Publications

• (Published) Saeed Shakuri and Alireza Rezvanian. ”An Efficient Approach in Detecting Lung Nodules Using Swin Transformer” 19th Iranian Conference on Intelligent Systems (ICIS), IEEE, 2024.
• (Published) Ghadami, Omid, Alireza Rezvanian, and Saeed Shakuri. ”Scalable Real-time Emotion Recognition using EfficientNetV2 and Resolution Scaling.” 10th International Conference on Web Research (ICWR), IEEE, 2024.
• (Under Review) Omid Ghadami, Alireza Rezvanian, Saeed Shakuri, and Mohammad Shamami. ”Real-time facial emotion recognition in smartphones using EfficientNetV2 and quantization-aware training.” Multimedia Tools and Application, Springer.
• (In preparation) Saeed Shakuri, Alireza Rezvanian, ”Lung Nodule Detection Using Few-shot Learning and Swin Transformer” to be submitted to Computerized Medical Imaging and Graphics.

Teaching Assistant

• Information Retrieval on the Web (Graduate class)
  University of Science and Culture
  Dr. Alireza Mansouri

  Fall 2024
• Artificial Intelligence (Undergraduate class)
  University of Science and Culture
  Dr. Azadeh Tabatabei

  Fall 2023
• Machine Learning (Graduate class)
  University of Science and Culture
  Dr. Alireza Rezvanian

  Fall 2022

Notable Academic Projects

• Traffic Sign Detection Using Faster R-CNN, FPN, and Transfer Learning. GitHub
• Object detection with Detectron2 GitHub
• Measuring sentence similarity with a TF-IDF approach GitHub
• Computer Vision projects using PyTorch GitHub
• A classification project using Ensemble Learning with the Abalone dataset GitHub
• An Image classification project using Transfer Learning with ResNet50 GitHub

Professional Services

• Posters Presented
  2nd Symposium on Frontiers in Computer and Data Sciences - An Efficient Approach in Detecting Lung Nodules Using Swin Transformer

  Feb. 2025
  Reviewer
  Elsevier - International Journal of Electrical and Computer Engineering

  Oct. 2024
  Wiley - The Journal of Engineering

  Aug. 2023
  Elsevier - Data in Brief Journal

  Mar. 2023 - Apr. 2023
• Judge
  University of Science and Culture
  Conducting assessments of computer science bachelor students’ final projects, followed by assigning grades.

  Jul. 2023 & Jan. 2024
• Invited Presenter
  University of Science and Culture
  Presentation title: ”An Introduction to Few-Shot Learning”

  Dec. 2022

Work Experience

• Rastar Creative Development Co.
  R&D Artificial Intelligence Developer
  Fine-tuning diffusion-based generative models for style adaptation using Python and PyTorch.

  Dec. 2025 - Feb. 2026
• BlazingFallApps
  Software Developer
  Developing the following mobile applications with the Flutter framework: A digital board game, a warehouse handling application for shopping stores, an application for better interaction between doctors and their patients, and a real estate application.

  Mar. 2020 - Nov. 2021