Bruno Fonkeng Systems Programmer · Ph.D. Researcher

Systems Programmer & Ph.D. Researcher

Building Toward Compiler and Runtime Engineering

I build low-level systems projects in C and C++, performance-oriented research infrastructure, and reproducible machine-learning tools. My work combines doctoral research in generative modeling and cybersecurity with a deliberate path through memory, operating systems, computer architecture, compilers, runtimes, and high-performance computing.

Compiler & Runtime Roadmap

Foundations

C, C++, pointers, memory management, modular design, debugging, testing, build systems, and fundamental data structures.

Active foundation building

Systems Depth

Operating systems, computer architecture, assembly, Linux systems programming, processes, concurrency, synchronization, profiling, and performance.

Next major depth stage

Language & Runtime

Lexing, parsing, abstract syntax trees, semantic analysis, intermediate representations, virtual machines, garbage collection, LLVM, optimization, and code generation.

Long-term specialization

Research Highlight

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GenCyberSynth

I develop reproducible machine-learning and cybersecurity research workflows that combine synthetic malware-image data, generative models, trustworthy evaluation, and high-performance computing infrastructure.

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Synthetic Data Generation

Generative Models

Trustworthy Evaluation

HPC Execution

Teaching Experience

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  • Teaching Assistant — Systems Programming

    CSCI 330 · Fall 2025 · Class 18372

    Supported instruction in C programming, memory, processes, files, system interfaces, debugging, and software-development fundamentals.

  • Teaching Assistant — Computer Architecture

    CSCI 370

    Supported foundational computer-architecture topics, including processor organization, memory hierarchy, instruction execution, and hardware-software interaction.

  • Teaching Assistant — Computer & Network Security

    CSCI 389 · Spring 2025 · Classes 28860–28929

    Supported coursework covering security principles, network threats, access control, defensive practices, and secure computing concepts.

Technical Capabilities

Skills for Building Reliable Research, ML, and Systems Infrastructure

My technical profile connects research software, C and C++ systems development, machine-learning infrastructure, reproducible experimentation, and compiler/runtime foundations. I focus on understanding underlying mechanisms, building dependable implementations, measuring behavior, and improving systems through evidence.

Role Alignment 01

Research Software Engineer

Reproducible experiment pipelines, scientific Python, high-performance workflows, metrics, data processing, automation, artifact management, testing, and research-oriented software design.

Applied through doctoral research and research tooling
Role Alignment 02

Systems Software Engineer

C and C++, memory management, pointers, modular interfaces, command-line tools, operating-system concepts, debugging, concurrency foundations, build systems, and low-level reasoning.

Active systems specialization and project development
Role Alignment 03

ML Systems & AI Infrastructure

Model-training pipelines, synthetic-data workflows, experiment orchestration, metrics, HPC execution, environment management, automation, reproducibility, and dependable machine-learning infrastructure.

Built through GenCyberSynth and ML metrics infrastructure
Role Alignment 04

Compiler & Runtime Engineering

Language implementation foundations, parsing, intermediate representations, execution models, virtual machines, memory, optimization, performance analysis, concurrency, and runtime architecture.

Long-term specialization built on active systems depth

Languages & Programming

Implementation across research and systems

  • C Systems Core
  • C++ Systems & Libraries
  • Python Research & Automation
  • SQL Data Systems
  • Shell Scripting Workflow Automation

Systems Engineering

Low-level software and machine behavior

  • Memory, Pointers & Ownership Core Foundation
  • Processes, Files & I/O Systems Interfaces
  • Data Structures & Algorithms Implementation
  • Concurrency & Synchronization Expanding Depth
  • Computer Architecture Hardware–Software

ML Systems & Research

Model infrastructure and trustworthy evaluation

  • PyTorch & Model Training Research Workflows
  • Generative Models GAN · VAE · Diffusion
  • ML Evaluation & Metrics Quality & Utility
  • Data Pipelines & Experiment Design Reproducible Research
  • Synthetic Data & Cybersecurity ML Doctoral Research

Performance & HPC

Measurement, scaling, and computational workflows

  • HPC & SLURM Workflows Research Execution
  • Performance Measurement Evidence Driven
  • Parallel & Batch Experimentation Scalable Workflows
  • Resource-Aware Computing CPU · Memory · Jobs
  • Profiling & Bottleneck Analysis Growing Capability

Tooling & Reliability

Build, test, diagnose, and reproduce

  • Git & GitHub Version Control
  • Make & CMake Build Systems
  • GDB, Valgrind & Sanitizers Diagnostics
  • Unit, Integration & Regression Testing Correctness
  • Docker & Environment Management Reproducibility

Compiler & Runtime Foundations

Long-term systems specialization

  • Lexing, Parsing & Syntax Trees Language Frontends
  • Intermediate Representations Compiler Pipeline
  • Bytecode & Virtual Machines Execution Models
  • Runtime Memory & Execution State Systems Direction
  • Optimization & Code Generation Advancing Roadmap

Technologies & Platforms

Languages, frameworks, infrastructure, and tools used across research, systems development, machine learning, teaching, and technical projects.

C C++ Python SQL Bash PyTorch NumPy Pandas scikit-learn FastAPI Linux WSL Windows GCC GDB Valgrind Sanitizers Make CMake Git GitHub Docker SLURM HPC Jupyter VS Code JSON REST APIs
01

Systems Understanding

Understand memory, data flow, ownership, control flow, interfaces, operating-system interaction, and failure modes.

02

Reproducible Engineering

Preserve commands, environments, tests, artifacts, configurations, metrics, and technical decisions.

03

Evidence-Based Optimization

Use measurements, profiling, experiments, diagnostics, and tests before making performance or correctness claims.

04

Progressive Technical Depth

Build increasingly demanding systems while strengthening the foundations required for compiler and runtime engineering.

Building Reliable Systems from Research to Runtime

My strongest opportunities are roles that value technical curiosity, implementation discipline, research rigor, systems thinking, and the ability to grow into increasingly complex infrastructure responsibilities.

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Technical Writing & Notes

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Using fgets and strtol Safely in C

A practical guide to robust integer input, validation, error handling, and avoiding the common problems caused by unsafe console-input patterns.

May 12, 2026 C, Input Parsing
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What Unix Tools Teach About Operating Systems

How rebuilding familiar command-line tools reveals deeper lessons about files, processes, streams, permissions, abstractions, and kernel services.

April 28, 2026 Systems, Operating Systems
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Why C Pointers Matter

Understanding addresses, indirection, memory layout, function parameters, arrays, and why pointers are central to low-level programming.

April 10, 2026 C, Memory
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