Mr. Muhammad Irfan Khan | Deep Learning | Best Researcher Award
University of Electronic Science and Technology of China | China
Muhammad Irfan Khan is a dedicated ML Security Engineer, researcher, and academic professional specializing in artificial intelligence, cybersecurity, and image processing, currently pursuing his M.S. in Information and Communication Engineering at the University of Electronic Science and Technology of China (UESTC), Chengdu. He has worked as a Machine Learning & Security Engineer at Victoriam.ai Solution, USA, where he developed threat detection models and optimized real-time security frameworks, and as a Research Intern at LinkDoc Technology, contributing to medical image segmentation advancements. At Namal University, Pakistan, he gained substantial experience as a Research Assistant, Teaching Assistant, and Lab Engineer, supporting AI/ML research, supervising projects, and co-authoring multiple peer-reviewed publications. His research contributions include journal articles such as “Genetic Algorithm Based Hybrid Deep Learning Framework for Stability Prediction of ABO3 Perovskites in Solar Cell Applications” (Energies, 2025), “Forecasting Fluctuations in Cryptocurrency Trading Volume Using a Hybrid LSTM-DQN Reinforcement Learning” (Digital Finance Journal, 2025), “Machine Learning-Powered Malware Detection in Encrypted IoT Traffic” (IEEE Journal of IoT, 2024), and “Decoding Emotions: U-Net-Driven Pattern Recognition for fMRI Analysis” (IEEE Transactions on Medical Imaging, 2025), along with conference proceedings in ICICT and IBCAST. He has served as a reviewer for international journals and conferences, including Computational Economics (Springer), Scientific Reports (Nature), and AAAI-26. His technical strengths span deep learning, reinforcement learning, cybersecurity, computer vision, and data-driven optimization, while also excelling in leadership and collaborative research. Despite his growing recognition, his current Scopus/Google Scholar profile records 2 documents reflecting his early yet impactful stage in research.
Profile: Scopus | LinkedIn
Featured Publication
Wali, S., Khan, M. I., & Zulfiqar, N. (2025). Forecasting fluctuations in cryptocurrency trading volume using a hybrid LSTM–DQN reinforcement learning. Digital Finance Journal.