Mohamed Moustafa received his PhD in electrical engineering from the City University of New York in 2001. Even before graduation, he joined a startup company, Visionics Corp., NewYork, USA as a research scientist in 1998 where he conducted research in specific aspects of computer vision algorithms for human facial recognition, including face detection at a distance, tracking, pose estimation and pose compensation. He continued growing with the company, which is now known as IDEMIA; one of the largest biometrics companies in the world. Moustafa served in IDEMIA in many capacities, until 2017, including senior reseach scientist, and R&D group leader in USA, Germany and France. He led multiple projects, including embedded face detection into major Japanese manufacturer digital cameras, invented an efficient way to estimate the face pose angle from a face image and built a 3D face model from a single 2D input face image. Since 2017, Moustafa has been principal research scientist with VKANSEE Tech, NY, USA; a startup company applying his AI and computer vision research in the area of embedded finger/palm print recognition.
In addition to Moustafa's industrial career, he has passion for academia. In 2011, he joined the American university in Cairo as an associate professor with the computer science and engineering department, where he teaches AI, computer vision, and machine learning related courses. Additionally, he enjoys supervising research projects with his magnificient pool of students applying deep learning methods in autonomous driving, human action recognition, and medical diagnosis among others.
He holds four issued US patents in the field of biometrics and computer vision. He has co-authored more than 50 research papers published in international journals and conferences. He is a member of the IEEE, the IEEE Computational Intelligence Society, the IEEE technical committee on multimedia computing, and the IEEE technical committee on pattern analysis and machine intelligence.
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- H. Eraqi, J. Honer, and M. Moustafa “Efficient Occupancy Grid Mapping and Camera- LiDAR Fusion for Conditional Imitation Learning Driving,” IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC 2020), Athens, Greece, September 2020.
- T. Khorshed, M. Moustafa, and A. Rafea, “Deep Learning for Multi-Tissue Cancer Classification of Gene Expressions (GeneXNet)”, IEEE Access, pp. 1 – 15, ISSN: 2169-536, DOI: 10.1109/ACCESS.2020.2992907, May 2020.
- Y. El-Din, M. Moustafa, H. Mahdi, “Deep convolutional neural networks for face and iris presentation attack detection: Survey and case study,” IET Biometrics, DOI: 10.1049/iet- bmt.2020.0004, Print ISSN 2047-4938, April 2020.
- Y. Abouelnaga, H. Eraqi, M. Saad, and M. Moustafa, “Driver Distraction Identification with an Ensemble of Convolutional Neural Networks,” Journal of Advanced Transportation (JAT), vol. 11, no. 1805, pp. 1 – 12, February 2019. DOI: 10.1155/2019/4125865.
- Y. Abouelnaga, H. Eraqi, and M. Moustafa, “Real-time Distracted Driver Posture Classification,” NIPS Workshop on Machine Learning for Intelligent Transportation Systems (NIPS 2018), Montreal, Canada, December 2018.
H. Eraqi, M. Moustafa, and J. Honer , “End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies,” NIPS Workshop on Machine Learning for Intelligent Transportation Systems (NIPS 2017), California, USA, December 2017.
S. Hegazy, and M. Moustafa, “Classifying Aggressive Drivers for Better Traffic Signal Control,” Workshop at IEEE 20th International Conference on Intelligent Transportation Systems (ITSC 2017), Yokohama, Japan, October 2017.
Y. Abouelnaga, O. Ali, H. Rady, and M. Moustafa, “CIFAR-10: KNN-based Ensemble of Classifiers,” The 2016 International Conference on Computational Science and Computational Intelligence (CSCI'16), Las Vegas, USA, December 2016.
E. Ahmed and M. Moustafa, “ House price estimation from visual and textual features ,” 8th International Conference on Neural Computation Theory and Applications, NCTA'16, Porto, Portugal, November 2016.
M. Moustafa, “Applying deep learning to classify pornographic images and videos,” 7th Pacific Rim Symposium on Image and Video Technology (PSIVT 2015), Auckland, New Zealand, November 2015.
Research and Teaching Interests
Moustafa's research interests include Artificial Intelligence, deep learning, machine intelligence, biometrics, computer vision, neural networks, genetic algorithms. He is currently teaching graduate courses: "Neural Networks and Genetic Algorithms", "Pattern Analysis", and "Computer Vision" in addition to the undergraduate courses "Fundamentals of Computer Vision", "Practical Machine Deep Learning", and "Digital Signal Processing".
PhD (GPA 4/4) Electrical Engineering, The City University of New York, USA, 2001.
MSc Electrical and Computer Engineering, Ain Shams University, Egypt, 1997.
BSc Computer Engineering, with Honor degree, Ain Shams University, Egypt, 1993.