About Me

I am an Assistant Professor in the department of Electrical Engineering at New Mexico Tech. I have completed my M.S. and Ph.D. in Electrical Engineering from the University of Texas at San Antonio (UTSA), USA in 2021 and 2022 respectively. Before joining UTSA, I received B.Sc. degree in Electronics and Communication Engineering from Khulna University of Engineering and Technology, Bangladesh in 2014. My research interest focuses on foundational AI, multimodal data modeling, and secure and trustworthy cyber and cyber-physical systems.

Research Areas

• Machine Learning/ Deep Learning
• Multimodal Computer Vision
• Neuromorphic Computing
• AI-enabled Cybersecurity
• Secure and Trustworthy CPS Systems
• Safety and Security of Intelligent Transportation Systems
• Human Factor based Behavioral Abstraction and Modeling
• Audio-Visual Perception Analysis
• AI-Security

News/Updates

• August 2022: Joined New Mexico Tech as an Assistant Professor in the department of Electrical Engineering.
• July 2022: Defended PhD dissertation on Towards Modeling Host-based Data for Cyber-Psychological Assessment in Cyber Threat Detection.
• April 2022: Awarded 1st place in the research award competition provided by UTSA Cyber Center For Security and Analytics.Details Here
• November 2021: Participating in 2021 CyberForce Competition as Blue Team, by U.S Department of Energy (DOE) and Argonne National Lab (ANL)
• August 2021: Received College of Engineering (COE) Doctoral Scholarship from The University of Texas at San Antonio
• May 2021: Ranked in top 20 (out of 330) in CONQUER THE HILL: Adventure Edition, Cyberforce competition by U.S Department of Energy (DOE)
• May 2021: Selected as Finalists in Mad Hack: Fury Code 2021, organized by NSIN, Department of Defense (DOD)
• March 2021: Our manuscript ExHPD: Exploiting Human, Physical and Driving Behaviors to Detect Vehicle Cyber Attacks has been accepted for publication in the IEEE Internet of Things Journal - IF: 9.93.
• May 2020: Our manuscript DeepRan: Attention-based BiLSTM and CRF for Ransomware Early Detection and Classification has been accepted for publication in the Information Systems Frontiers Journal - IF: 6.19.
• October 2019: Collaborative manuscript Detailed Statistical Models of Host-Based Data for Detection of Malicious Activity with Sandia national Lab (SNL) published by U.S. Department of Energy (DOE).
• August 2019: Attending Uncertainty Quantification Summer School 2019 by University of Southern California (USC) and Sandia National Laboratory (SNL)