Portfolio & Research

Moses Ndebugre

Ph.D. Candidate, Electrical Engineering

Researcher and engineer specializing in Deep Learning applications in cybersecurity. Passionate about bridging rigorous analysis with real-world engineering impact.

Institution

North Carolina A&T State University, College of Engineering

Department

Electrical & Computer Engineering

Research Focus

Vulnerability Detection using Deep Learning Applications

Advisor

Dr Ahmad Patooghy

Email

myndebugre@aggies.ncat.edu

01

About

I am a Ph.D. candidate in Electrical Engineering with a focus on Deep Learning applications in engineering systems. My work bridges theoretical foundations with applied cybersecurity and fault detection, producing tools and insights that directly inform industrial and research decision-making.

My academic journey began with a B.S. in Telecommunication Engineering followed by an M.S. in Electronics and Communication Engineering. I have since been involved in federally funded research projects and industry collaborations, developing robust simulation frameworks and analyzing complex multi-physics phenomena.

Outside of research, I am passionate about science communication, mentoring junior engineers, and open-source scientific software. I believe in making rigorous engineering tools accessible to a broader community.

3
Published papers
2
Conference presentations
1
Research grants
02

Course Projects

Autonomous Mobile Robots

Autonomous Mobile Robots

Simulation and analysis of autonomous robot navigation, path planning, and obstacle avoidance across multiple scenarios.

11 simulations

Linear Control

Linear Control Systems

Design and simulation of linear control strategies including PID, state-space, and frequency-domain analysis across multiple plant models.

3 simulations

Project 3

Project 3 Title

Description of your third project — replace this with your actual course name and a brief summary of the work.

3 simulations

03

Research Papers

Peer-reviewed publications and preprints. Click a paper to access the full document.

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