Dr. Fahman Saeed | Signal Distortion Awards | Best Researcher Award

Dr. Fahman Saeed | Signal Distortion Awards | Best Researcher Award 

Dr. Fahman Saeed, Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia

Dr. Fahman Saeed is an Assistant Professor in the College of Computer and Information Sciences at Imam Mohammad Ibn Saud Islamic University (IMSIU) in Riyadh, Saudi Arabia. With a Ph.D. in Computer Science from King Saud University, his research focuses on deep learning models, particularly for automatic diabetic retinopathy screening. He has contributed significantly to various research projects, including the development of fingerprint interoperability solutions and privacy-protected breast cancer screening systems, earning multiple ISI papers, patents, and conference presentations. Dr. Saeed also has extensive experience in machine learning, specializing in PyTorch, TensorFlow, and large language models. In addition to his academic achievements, he actively participates in professional activities, such as curriculum development and leading workshops on AI, NLP, and generative AI. His dedication to education and research, coupled with his expertise in artificial intelligence, continues to influence both his academic institution and the broader scientific community.

Professional Profile:

ORCID

Suitability for Best Researcher Award: Fahman Saeed

Fahman Saeed is exceptionally suited for the Best Researcher Award due to his outstanding contributions to the field of computer science, particularly in the areas of deep learning, machine learning, and artificial intelligence. With a robust academic background and extensive experience in both research and teaching, Dr. Saeed has demonstrated leadership in advancing the application of machine learning technologies in critical areas like medical diagnostics and data security.

Education πŸŽ“

  • Ph.D. in Computer Science
    • Institution: King Saud University, Saudi Arabia πŸŽ“
    • Graduation: November 2021 πŸ“…
    • Dissertation: Developing an auto deep learning model with less complexity and high performance for automatic diabetic retinopathy screening πŸ§ πŸ’»
  • M.Sc. in Computer Science
    • Institution: King Saud University, Saudi Arabia πŸŽ“
    • Graduation: May 2014 πŸ“…
  • B.Sc. in Computer Science
    • Institution: King Saud University, Saudi Arabia πŸŽ“
    • Graduation: February 2007 πŸ“…

Academic Experience πŸ“š

  • Assistant Professor
    • Institution: College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia 🏫
    • Duration: 2022 to Present ⏳
    • Responsibilities: Teaching courses in Artificial Intelligence πŸ€–, Natural Language Processing πŸ’¬, Algorithm Design and Analysis πŸ’», Image Processing πŸ–ΌοΈ, and Computer Vision πŸ‘€
  • Lecturer (Part-time)
    • Institution: King Saud University, Riyadh, Saudi Arabia πŸŽ“
    • Duration: 2017 to 2021 ⏳
  • Researcher
    • Institution: King Saud University, Riyadh, Saudi Arabia πŸ§ͺ
    • Duration: March 2015 to 2021 ⏳
    • Projects:
      • Automatic Diabetic Retinopathy Screening πŸ©ΊπŸ‘οΈ
        • Achievements: Two ISI papers πŸ“„
      • Identification of Fingerprint Interoperability πŸ§‘β€βš–οΈ
        • Achievements: One patent, one ISI paper, two conference papers πŸ“‘
      • Cloud-Based Privacy-Protected Computer-Aided Diagnosis System for Breast Cancer Screening 🩻
        • Achievements: One ISI paper πŸ“„

Publication Top Notes

Adaptive Renewable Energy Forecasting Utilizing a Data-Driven PCA-Transformer Architecture

Blockchain-Based Quality Assurance System for Academic Programs
Optimal Sizing and Placement of Distributed Generation under N-1 Contingency Using Hybrid Crow Search–Particle Swarm Algorithm
A Data-Driven Convolutional Neural Network Approach for Power Quality Disturbance Signal Classification (DeepPQDS-FKTNet)

Designing the Architecture of a Convolutional Neural Network Automatically for Diabetic Retinopathy Diagnosis