Dr. Muhammad Yasir Siddiqui | Deep Learning | Best Researcher Award

Dr. Muhammad Yasir Siddiqui | Deep Learning | Best Researcher Award

Dr. Muhammad Yasir Siddiqui, Seoul National University of Science and Technology, South Korea

Dr. Muhammad Yasir Siddiqui is a seasoned researcher and academician with extensive experience in Artificial Intelligence and Deep Learning. Currently serving as a Research Professor at SeoulTech in South Korea, he has contributed to renowned research projects across Pakistan, Denmark, and South Korea. With over nine publications in peer-reviewed journals and conferences, his work focuses on cutting-edge technologies such as 3D applications and autonomous vehicles.

Professional Profile:

Google Scholar

Suitability for Best Researcher Award

Dr. Muhammad Yasir Siddiqui is a highly qualified candidate for the Best Researcher Award due to his extensive contributions to Artificial Intelligence (AI), Deep Learning, and Computer Vision. His research has led to significant advancements in 3D applications, autonomous vehicles, and AI-driven perception systems, making him a strong contender for this recognition.

Education and Experience

  • πŸŽ“ PhD in Engineering (Computer Vision, AI)
    Mar. 2019 – Feb. 2023
    Mechanical System Engineering, Tongmyong University, South Korea

  • πŸŽ“ Master of Science in Computer Science
    Sep. 2008 – Mar. 2011
    Blekinge Institute of Technology, Karlskrona, Sweden

  • πŸŽ“ Master in Information Technology
    Sep. 2005 – Mar. 2007
    Quaid-i-Azam University, Islamabad, Pakistan

  • πŸŽ“ Bachelor of Science in Computer Science
    Sep. 2000 – Mar. 2004
    Allama Iqbal Open University, Islamabad, Pakistan

  • πŸ‘¨β€πŸ« Research Professor
    Mar. 2023 – Jun. 2025
    Dept. of Electrical and Information System, SeoulTech, South Korea

  • πŸ‘¨β€πŸ« Senior Lecturer
    Jun. 2016 – Feb. 2019
    School of Computing and Engineering, Lahore Leads University, Pakistan

  • πŸ’» Software Engineer
    Mar. 2011 – Apr. 2015
    Edixen Solutions, Copenhagen, Denmark

Professional Development

Throughout his career, Dr. Siddiqui has demonstrated a commitment to advancing technology and education. As a Research Professor at SeoulTech, he leads programming lectures and spearheads machine learning and deep learning projects, focusing on computer vision and big data. His tenure as a Senior Lecturer at Lahore Leads University involved teaching programming languages, guiding research initiatives, and supervising undergraduate projects. At Edixen Solutions in Denmark, he led a team of over 20 professionals, driving multi-phased research initiatives that directly impacted product development across web, API, and mobile platforms.

Research Focus

Dr. Siddiqui’s research interests lie at the intersection of Deep Learning and Computer Vision. He specializes in developing algorithms for classification, detection, and segmentation tasks, with a particular emphasis on depth estimation and 3D instance segmentation. His work extends to augmented and virtual reality (AR/VR) applications, where he explores immersive technologies that enhance user experiences. Additionally, he contributes to advancements in autonomous vehicles, focusing on the integration of AI-driven perception systems to improve safety and efficiency.

Awards and Honors

  • πŸ₯‰ 3rd Position
    Busan Datathon Competition
    Korea Intelligence Information Society, Digital Ministry by Busan Techno Park

  • πŸŽ“ Fully Funded Professor’s Fellowship
    Co-funded by the BK21 for PhD studies

  • πŸŽ“ Fully Funded Scholarship
    Ministry of Education, Sweden for Master’s studies

  • πŸŽ“ Fully Funded Scholarship
    Software Technology Park, Islamabad, Pakistan for Master’s studies

  • πŸŽ“ Partially Funded Scholarship
    University Scholarship for Bachelor’s studies

Publication Top Notes:

πŸ“„ A comparative analysis of machine learning approaches for plant disease identification – 31 citations, 2017
πŸ“ Recognition of Pashto handwritten characters based on deep learning – 28 citations, 2020
βš™οΈ Faster metallic surface defect detection using deep learning with channel shuffling – 12 citations, 2024
πŸ” Modeling & Evaluating The Performance Of Convolutional Neural Networks For Classifying Steel Surface Defects – 8 citations, 2024
πŸ“š Deep learning-based 3D instance and semantic segmentation: A review – 8 citations, 2024