PhD Scholar, South China University of Technology, Guangzhou, Guangdong, China
Dr. Awais Khan is a dedicated researcher specializing in deep learning, multimedia cloud computing, and artificial intelligence 🌐. Currently pursuing a Ph.D. in Information & Communication Engineering at South China University of Technology 🎓, his research focuses on deep learning models for Quality of Experience (QoE) in cloud environments. With a strong academic and professional background, Dr. Khan has contributed significantly to the fields of machine learning, computer vision, and multimedia processing. His work integrates innovative AI techniques for real-world applications, making him a prominent figure in computational research 🤖.
Publication Profile
📚 Education
Dr. Khan is on track to complete his Ph.D. (2021-2025) at South China University of Technology 🇨🇳, where he explores deep learning techniques for emotion-based QoE in cloud computing. He previously earned his M.S. in Computer Science (2016-2018) from Shah Abdul Latif University, Pakistan 🇵🇰, focusing on Sindhi text categorization using Support Vector Machines. His academic journey began with a B.S. in Computer Science (2011-2014) from the same institution, achieving commendable academic performance 📊.
👨🏫 Experience
Dr. Khan served as an Assistant Professor at ILMA University (2019-2022) in Pakistan, where he developed curriculum content, mentored students, and engaged in academic research. Prior to this, he was an Instructor at APTECH Computer Center (2014-2018), guiding students through machine learning projects and real-world applications 🎓. His early experience includes a Teaching Assistant role at Shah Abdul Latif University, where he supported research initiatives and practical learning in AI-related subjects 🔍.
🏆 Awards and Honors
Dr. Khan has received recognition for his contributions to AI, deep learning, and multimedia computing. His work has been featured in top-tier journals, and he has actively participated in research-driven initiatives. His academic excellence is reflected in his high GPA scores, international collaborations, and impactful research publications 📜.
🔬 Research Focus
Dr. Khan’s research spans deep learning, machine learning, and multimedia cloud computing. His core areas include domain generalization, multimodal learning, and fairness in AI models. He actively explores AI-driven QoE assessment for cloud gaming, representation learning for multimedia data, and security models for cloud environments. His interdisciplinary approach bridges AI, image/audio processing, and user experience enhancement 🌍.
📝 Conclusion
Dr. Awais Khan stands out as a researcher and educator dedicated to advancing AI applications in multimedia and cloud computing. With a solid academic foundation, extensive teaching experience, and an impressive publication record, he continues to push the boundaries of deep learning and machine learning research. His work significantly impacts QoE evaluation, multimedia security, and AI-driven automation, positioning him as a key contributor to the AI research community 🚀.
📄 Publications
Fog computing security: A review – Security and Privacy (2025) 🔗 [Cited By: TBD]
Deep learning-based QoE assessment of cloud gaming via emotions in a virtual reality environment – Journal of Cloud Computing (2025) 🔗 [Cited By: TBD]
Quality of experience (QoE) in cloud gaming: A comparative analysis of deep learning techniques via facial emotions in virtual reality environment – Sensors (2025) 🔗 [Cited By: TBD]
A proposed model for security of QoE data in cloud gaming environment – International Journal of Electronic Security and Digital Forensics (2025) 🔗 [Cited By: TBD]
Quality of experience that matters in gaming graphics: How to blend image processing and virtual reality – Electronics, vol. 13, no. 15 (2024) 🔗 [DOI: 10.3390/electronics13152998] [Cited By: TBD]
Unintended data behavior analysis using cryptography stealth approach against security and communication network– Mobile Networks and Applications (2023) 🔗 [Cited By: TBD]
Prediction of diabetic patients in Iraq using binary dragonfly algorithm with LSTM neural network – AIMS Electronics & Electrical Engineering, vol. 7, no. 3 (2023) 🔗 [Cited By: TBD]
Unmanned aerial vehicles: A review– Cognitive Robotics (2022) 🔗 [Cited By: TBD]
Analysis of the teaching quality on deep learning-based innovative ideological political education platform – Progress in Artificial Intelligence (2022) 🔗 [DOI: 10.1007/s13748-021-00272-0] [Cited By: TBD]
Examining the present and future integrated role of artificial intelligence in business: A survey study on the corporate sector – Journal of Computer and Communications, vol. 9, no. 1 (2021) 🔗 [Cited By: TBD]