Dr Danish Mahmood Khan
- Senior Lecturer
Biography
Dr Danish Mahmood Khan earned his Ph.D. from Universiti Teknologi PETRONAS (UTP), Malaysia, in 2021, specializing in electroencephalography (EEG) and brain connectivity for diagnosing neurological disorders using artificial intelligence. He also holds both Bachelor's and Master’s degrees in Telecommunications from NED University of Engineering and Technology (NEDUET), Karachi, Pakistan. Dr Khan is a registered engineer with the Pakistan Engineering Council (Lifetime) and a member of the Institution of Engineers, Pakistan.
Before his current role as Senior Lecturer at Sunway University, Dr Khan served as an Assistant Professor at NEDUET and also spent four years as a Research Scientist at UTP. With over 14 years of professional experience in research and education, his work focuses on advancing EEG-based methodologies to understand brain function, diagnose neurological disorders, and apply artificial intelligence in medical diagnostics, image processing, and computer vision.
Dr Khan has authored numerous high-impact publications in prominent international journals and actively contributes as a peer reviewer for leading publishers such as IEEE, Nature, and Springer-Nature. He also serves on the Editorial Board of Discover Psychology, a Springer Nature journal.
Academic & Professional Qualifications
- PhD in Electrical & Electronic Engineering, Universiti Teknologi PETRONAS, Malaysia (2021)
- Masters in Telecommunications Engineering, NED University of Engineering and Technology, Karachi Pakistan (2012)
- Bachelors in Telecommunications Engineering, NED University of Engineering and Technology, Karachi Pakistan (2009)
Research Interests
- Neuroscience
- Brain Computer Interface
- Image Processing and Computer Vision
- Machine and deep learning including explainable AI
Teaching Areas
- Digital Image Processing
- Machine and Deep Learning
- Programming Fundamentals
- Communication Systems
Courses Taught
- AI in Telecommunications
- Computer & Programming
- Image Processing & Computer Vision
- Signal & Systems
Notable Publications
Huda S, Danish M. Khan, Masroor K, Rashid A, Shabbir M. “Advancements in automated diagnosis of autism spectrum disorder through deep learning and resting-state functional mri biomarkers: a systematic review”. Cognitive Neurodynamics. 2024 Sep 13:1-7. (Q2, JCR IF 2023 = 3.1)
Rahman N, Danish M. Khan, Masroor K, Arshad M, Rafiq A, Fahim SM. “Advances in brain-computer interface for decoding speech imagery from EEG signals: a systematic review”. Cognitive Neurodynamics. 2024 Sep 4:1-9. (Q2, JCR IF 2023 = 3.1)
Warren SL, Danish M. Khan, Moustafa AA. “Assistive tools for classifying neurological disorders using fMRI and deep learning: A guide and example”. Brain and Behavior. 2024 Jun;14(6):e3554. (Q2, JCR IF 2022 = 3.1)
Danish M. Khan, Yahya N, Kamel N, Faye I. A Novel Method for Efficient Estimation of Brain Effective Connectivity in EEG. Computer Methods and Programs in Biomedicine. 2023 Jan 1; 228:107242. (Q1, JCR IF 2021 = 7.027)
M. W. Sabir, Z. Khan, N. M. Saad, Danish M. Khan, M. A. Al-Khasawneh, K. Perveen, A. Qayyum, and S. S. A. Ali, “Segmentation of liver tumor in ct scan using resu-net,” Applied Sciences, vol. 12, no. 17, p. 8650,2022. (Q2. JCR IF 2021 = 2.838)
Danish M. Khan, K. Masroor, M. F. M. Jailani, N. Yahya, M. Z. Yusoff, and S. M. Khan, “Development of wavelet coherence EEG as a biomarker for diagnosis of major depressive disorder,” IEEE Sensors Journal, vol. 22, no. 5, pp. 4315–4325, 2022. (Q2. JCR IF 2020 = 3.301)
Danish M. Khan, Norashikin Yahya, Nidal Kamel, and Ibrahima Faye. "Effective Connectivity in Default Mode Network for Alcoholism Diagnosis," IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29:796–808, 2021. (Q1. JCR IF 2019 = 3.34)
Danish M. Khan, Norashikin Yahya, Nidal Kamel, and Ibrahima Faye. Automated diagnosis of major depressive disorder using brain effective connectivity and 3d convolutional neural network. IEEE Access, 9:8835–8846, 2021. (Q1. JCR IF 2019 = 3.745)
Danish M. Khan, Nidal Kamel, Mustapha Muzaimi, and Timothy Hill. Effective connectivity for default mode network analysis of alcoholism. Brain Connectivity, 11(1):12–29, 2021. (Q1. JCR IF 2019 = 5.26)
Muhammad Ahsan, Mohd Zuki, Danish M. Khan, Norashikin Yahya and Mansoor Ebrahim. Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network. Sensors, 2021. (Q1. JCR IF 2020 = 3.576)
Achievements & Accolades
- Secured HORIZON-WIDERA-2023-TALENTS-02-01 — ERA Fellowships, Type: HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships (Worth: 166278.72 EUR)
- Secured National Grassroots ICT Research Initiative (NGIRI) 2023-24 funding for the final-year design project titled "Brainwave-Driven Robotics: Decoding Motor Imagery EEG Signals for Robotic Arm Control using Deep Learning" (FYP Code: NGIRI-2024-24524).
- Secured National Grassroots ICT Research Initiative (NGIRI) 2023-24 funding for the final-year design project titled "AI-Based EEG Model Deployment for Major Depressive Disorder Diagnosis on Embedded Systems" (FYP Code: NGIRI-2024-25242).
- Higher Education Commission (HEC), Pakistan approved PhD supervisor for “Engineering and Technology”. Registration Ref: HEC/HRD/ASA/2023/194254.
- Department’s Best Postgraduate Student Award from Dean of Centre of Graduate Studies, Universiti Teknologi PETRONAS, Malaysia, 2021.
- Won Graduate Assistantship Scheme from Universiti Teknologi PETRONAS, Malaysia for PhD.
Professional Associations
- Registered Engineer with Pakistan Engineering Council (PEC) TELE/02770
- Member, The Institution of Engineers, Pakistan (IEP)
- Editorial Board Member, Discover Psychology, SpringerNature
