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About Dr Mohammad Samar Ansari

Dr. Ansari has


Presently involved in the teaching and supervision in the following modules:

  • SE7048 AI for Modern Use-Cases
  • SE7227 Applied Business Intelligence for Managers
  • SE7045 MSc Project
  • SE7200 Research Dissertation
  • SE7220 Risk Management
  • SE5024 Electronic and Electrical Engineering Integrative Design Project


For his Ph.D., Dr. Ansari worked on the design of high-performance neural network hardware for the solution of systems of simultaneous linear equations and other mathematical programming problems.

During his post-doctoral research, Dr. Ansari worked on the design of Deep Learning models for Cybersecurity Alert Prediction (which is a relatively less-explored area, as compared to attack detection). Presently, he is looking into Machine Learning / Deep Learning applications in Computer Vision, Video Processing, and Smart Cities. Privacy-preserving techniques for heterogenous user-centric data is another area in which he is currently interested. This interest has stemmed from his awareness of the need and importance GDPR/DPA regulations.

Published Work

Selected Publications:

  1. Mohammad Samar Ansari. “A single-layer asymmetric RNN with low hardware complexity for solving linear equations”. In: Neurocomputing 485 (2022), pp. 74–88.
  2. Mohammad Samar Ansari, Vaclav Bartos, and Brian Lee. “GRU-based deep learning approach for network intrusion alert prediction”. In: Future Generation Computer Systems (2021).
  3. Syed Sahil Abbas Zaidi, Mohammad Samar Ansari, Asra Aslam, Nadia Kanwal, Mamoona Asghar, and Brian Lee. “A survey of modern deep learning based object detection models”. In: Digital Signal Processing 126 (2022), p. 103514.
  4. Saeed Hamood Alsamhi, Faris Almalki, Ou Ma, Mohammad Samar Ansari, and Brian Lee. “Predictive Estimation of Optimal Signal Strength from Drones over IoT Frameworks in Smart Cities”. In: IEEE Transactions on Mobile Computing (2021).
  5. Nadia Kanwal, Mamoona Naveed Asghar, Mohammad Samar Ansari, Martin Fleury, Brian Lee, Marco Herbst, and Yuansong Qiao. “Preserving chain-of-evidence in surveillance videos for authentication and trust-enabled sharing”. In: IEEE Access 8 (2020), pp. 153413–153424.

For a complete list of publications, please visit the Google Scholar profile here:


  • Bachelor of Technology (Electronics Engineering)
  • Master of Technology (Electronics Engineering)
  • Ph.D. (Electronics Engineering)