Bruno Fonkeng
Ph.D. Researcher · Research Software · C/C++ Systems · ML Infrastructure · Compiler/Runtime Direction
Computer Science Ph.D. researcher at the University of North Dakota working across synthetic cybersecurity data, probabilistic generative modeling, trustworthy machine learning, reproducible scientific software, high-performance computing, and C/C++ systems development. My long-term engineering direction is compiler, runtime, and performance-oriented systems infrastructure.
Education
Academic training in physics, computer science, cybersecurity, machine learning, and high-performance computing.
Ph.D. in Computer Science
University of North Dakota
Doctoral research in probabilistic and generative modeling, synthetic cybersecurity data, malware classification, trustworthy evaluation, reproducibility, and high-performance research workflows.
M.S. in Computer Science
University of North Dakota
Graduate study in machine learning, artificial intelligence, cybersecurity, secure applications, data engineering, APIs, predictive modeling, and high-performance computing.
B.S. in Physics
The University of Bamenda, Cameroon
Foundation in mathematical reasoning, scientific modeling, laboratory analysis, statistics, experimentation, and first-principles problem solving.
Research Experience
Doctoral research, scientific software, and applied research infrastructure.
Ph.D. Researcher
University of North Dakota · ICCC Laboratory
Conduct research on synthetic cybersecurity data and the conditions under which probabilistic and generative models improve downstream malware classification.
- Develop GenCyberSynth, a framework for training, sampling, comparing, and evaluating cybersecurity-data generators.
- Study GAN, VAE, diffusion, autoregressive, masked autoregressive, restricted Boltzmann, and Gaussian-mixture approaches.
- Evaluate generated data using KID, MS-SSIM, balanced accuracy, macro F1, macro AUPRC, diversity, and downstream utility.
- Build reproducible workflows using fixed seeds, experiment manifests, SLURM, automated aggregation, reports, and artifact tracking.
- Investigate balanced and selective synthetic-data policies across class-specific generation budgets.
Research Assistant — TruNorth
University Research Project
Contributed to an applied decision-support initiative involving ROI-calculation workflows, requirements analysis, validation, system design, and technical documentation.
- Translated operational requirements into measurable inputs, calculations, outputs, and user-facing workflows.
- Supported applied research, validation, system design, and interdisciplinary documentation.
Teaching Experience
Undergraduate instruction, technical support, assessment, and feedback.
Graduate Teaching Assistant
University of North Dakota
Support instruction and assessment in systems programming, computer architecture, cybersecurity, computer networking, and information assurance.
- Assist students with C programming, pointers, memory, file I/O, processes, debugging, systems interfaces, and command-line tools.
- Review technical submissions and provide feedback on correctness, software design, security, evidence, and technical communication.
- Support laboratory, project, grading, and consultation activities across multiple computing courses.
Publications & Manuscripts
Peer-reviewed work and active research manuscripts.
First-Author Peer-Reviewed Conference Paper
Exact citation should match the official proceedings and Google Scholar record.
When Does Synthetic Data Help Malware Classification?
Examines when synthetic malware data improves downstream classification under controlled model and generation budgets.
Selective Synthetic Data Policies for Malware Classification
Studies balanced and class-selective augmentation policies using class-aware evaluation and controlled data budgets.
Presentations & Posters
Conference presentations and research communication.
IEEE CARS Conference Presentation
Technical research presentation delivered to an academic and professional audience.
Red River Valley ACS Research Poster
Research poster and visual communication of experimental methods and findings.
Selected Technical Projects
Research software, systems programming, ML infrastructure, and engineering tools.
GenCyberSynth
Reproducible research framework for synthetic malware-data generation, model comparison, downstream evaluation, artifact tracking, aggregation, and publication reporting.
Python · PyTorch · SLURM · HPCC Systems Mastery
Structured C systems-programming roadmap covering memory, pointers, arrays, modular interfaces, testing, debugging, data structures, and operating-system concepts.
C · GCC · Make · GDB · ValgrindMetricForge
Open-source ML metrics library with a C++ core, Python bindings, CMake builds, testing, examples, and education-focused documentation.
C++ · Python · CMake · ML MetricsC CLI Lab
Unix-inspired command-line tools built in C to explore streams, files, processes, arguments, text processing, directories, and POSIX behavior.
C · Linux · POSIX · CLIBuilding Reliable Systems from Research to Runtime
My academic and technical work connects mathematical reasoning, scientific research, trustworthy machine learning, reproducible infrastructure, C/C++ systems development, and a deliberate path toward compiler, runtime, and performance engineering.