Ghulam Mujtaba | Computer Vision | Computer Vision

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Assist Prof Dr.Ghulam Mujtaba | Computer Vision |Best Researcher Award

Assistant Professor Regis University United States

Ghulam Mujtaba is a Postdoctoral Researcher at West Virginia University, specializing in deep learning and computer vision. With over seven years of industrial experience, he has developed state-of-the-art techniques for action recognition on resource-constrained edge devices. His work has led to the publication of over 10 refereed articles and one pending USA patent.

Profile

Scopus

Education 🎓

  • Ph.D. in Engineering (2018 – 2021), Gachon University, South Korea. Dissertation: “Lightweight Client-driven Personalized Multimedia Framework for Next Generation Streaming Platforms.”
  • M.Sc. in Computer Science (2014 – 2016), Indus University, Pakistan.
  • B.Sc. in Computer Science (2009 – 2013), COMSATS Institute of Information Technology, Pakistan.

Experience 💼

  • Postdoctoral Researcher, West Virginia University (2023 – Present)
  • Research Engineer, C-JeS Gulliver Studio, South Korea (2022 – 2023)
  • Senior Researcher, DeltaX, South Korea (2021 – 2022)
  • Visiting Researcher, MCSLab, Sungkyunkwan University, South Korea (2019 – 2021)
  • Graduate Research Assistant, Gachon University, South Korea (2018 – 2021)

Research Interests 🔍

Ghulam’s research focuses on Computer Vision, Deep Learning for Visual Analysis, and Multimedia Retrieval. He is passionate about developing lightweight deep learning models for edge devices and enhancing realism in digital human characters for Metaverse applications.

Awards 🏆

  • Korea Transportation Safety Authority Chairman Award for Self-Driving Data Contest 2021.
  • Amazon Research Award 2021 (proposal led to a patent application).

Publications 📚

  1. FRC-GIF: Frame Ranking-based Personalized Artistic Media Generation Method for Resource Constrained Devices, IEEE Transactions on Big Data, 2023. Cited by 7
  2. LTC-SUM: Lightweight Client-driven Personalized Video Summarization Framework Using 2D CNN, IEEE Access, 2022. Cited by 15
  3. Client-driven Animated GIF Generation Framework Using an Acoustic Feature, Multimedia Tools and Applications, 2021. Cited by 10
  4. Client-Driven Personalized Trailer Framework Using Thumbnail Containers, IEEE Access, 2020. Cited by 12
  5. Energy-Efficient Data Encryption Techniques in Smartphones, Wireless Personal Communications, 2019. Cited by 20