Fairness under distribution shift
RobustnessReproducibility
AI / ML security · Trustworthy AI
AI / ML Security Researcher
I study how AI systems behave when fairness, robustness, and security are tested beyond controlled settings.
Research Associate at Carnegie Mellon University, working across biometric security, distribution shift, reproducibility, and malware analysis.
Research
Architecture and demographic bias
Computer visionBiometrics
Security of AI-enabled systems
AI/ML securityEvaluation
Publications
Fairness-Aware Face Presentation Attack Detection Using Local Binary Patterns: Bridging Skin Tone Bias in Biometric Systems
Jema David Ndibwile, Ntung Ngela Landon, Floride Tuyisenge
Journal of Cybersecurity and Privacy, 6(1)Architectural Bias in Face Presentation Attack Detection: A Comparative Study of Vision Transformers and Convolutional Neural Networks
Ntung Ngela Landon, Floride Tuyisenge, Jema David Ndibwile
CSP 2026 · arXiv:2606.18510Fairness Under Distribution Shift in Face Presentation Attack Detection: A Cross-Dataset and Ablation-Based Analysis
Ntung Ngela Landon, Floride Tuyisenge, Remy Dukundane, Emmanuel Iduh, Jema David Ndibwile
Under review at IEEE AccessIn review
Adversarial Artificial Intelligence Threats in Smart Energy Grids: When Learning Systems Learn from the Attacker
Jema David Ndibwile, Ntung Ngela Landon, Lunodzo Mwinuka
Under review at Energy ReportsIn review
Experience
Mar 2026 — Mar 2027
Research Associate
Carnegie Mellon University · Pittsburgh
Leading research on the robustness and reproducibility of demographic fairness in face PAD under distribution shift, while contributing to a Microsoft-funded malware analysis project.
Aug — Dec 2025
Research Assistant
Carnegie Mellon University · Pittsburgh
Led comparative experiments across ViT-Tiny, ResNet18, and DeiT-S to study architecture, demographic performance, and cross-demographic generalization.
Jul — Nov 2025
AI, Data Management & Cybersecurity Intern
International Telecommunication Union · Geneva
Investigated platform vulnerabilities and the relationship between AI, data management, and security in AI-enabled information systems.
Jun — Aug 2025
Research Intern
Carnegie Mellon University · Pittsburgh
Initiated fairness research for underrepresented African populations and developed a lightweight face PAD pipeline with statistical fairness evaluation.
Projects
MSc practicum
Cross-Platform Automated Malware Analysis Pipeline
An automated Windows and Linux malware-analysis workflow integrating CAPE Sandbox, isolated virtual machines, structured reporting, and SOC/SIEM ingestion.
CAPE SandboxPythonSIEM
Applied security
Ethical Hacking & Penetration Testing
Security assessments spanning vulnerability discovery, wireless security, traffic analysis, and automated network scanning.
Kali LinuxBurp SuiteWireshark
BEng final project
IoT-Based Fetal Health Monitoring System
A remote monitoring system combining embedded sensors, signal processing, and wireless transmission of fetal movement indicators.
IoTSignal processingArduino
About
My path began with demographic fairness in face Presentation Attack Detection and expanded into the broader security, robustness, and reliability of AI-enabled systems.
I value reproducible experiments, transparent limitations, and research that can inform more secure real-world systems.
More about meEducation
MSc, Information Technology — Cybersecurity2024 — 2025
Carnegie Mellon University · GPA 3.64 / 4.00BEng, Computer Engineering2019 — 2023
University of Buea · GPA 3.51 / 4.00Methods & tools
Research methodsControlled experiments, ablations, cross-dataset evaluation, multi-seed replication
AI / MLComputer vision, CNNs, Vision Transformers, transfer learning, model evaluation
SecurityMalware analysis, penetration testing, biometric security, vulnerability assessment
ToolsPython, TensorFlow, OpenCV, scikit-learn, CAPE Sandbox, Wireshark