Robert Frenken
ISE PhD Student · The Ohio State University
graph neural networks, intrusion detection, knowledge distillation, CAN bus
PhD student in Industrial & Systems Engineering at The Ohio State University (expected May 2027), working at the Center for Automotive Research (CAR). My research uses graph neural networks and knowledge distillation for intrusion detection on vehicle CAN bus networks — deploying lightweight models on resource-constrained automotive platforms.
News
- Summer 2026 — Starting as Critical Infrastructure Analysis Graduate Intern at Lawrence Livermore National Laboratory
- Jan 2026 — Submitted CWD-SWGD-IDS (co-authored) to IEEE ITSC 2026
- Jun 2025 — Released multi-stage KD-GAT preprint
- May 2025 — KD-GAT accepted to IEEE ITSC 2025
Research Interests
- Graph neural networks for cybersecurity in automotive systems
- Knowledge distillation and model compression for edge deployment
- Federated learning for privacy-preserving intrusion detection
- LLM behavior in game-theoretic scenarios
Recent Projects
Ohio Campaign Finance Dashboard — Interactive data visualization platform exploring political contributions across Ohio legislative districts. Built with D3.js, Map-Libre, and Sevelteplot.
Mobility Systems Lab Resources — Technical knowledge base and HPC usage dashboard for lab onboarding.
CAIS 2025 Capstone — Empirical investigation of LLM coordination in game-theoretic scenarios (prisoner’s dilemma, battle-of-the-sexes, duopoly markets).
Technical Skills
ML/DL: PyTorch, PyTorch Geometric, Ray, MLflow, scikit-learn, Knowledge Distillation, Federated Learning
Data: DuckDB, SQL, Pandas, GeoPandas, Spark, NumPy
Visualization: Matplotlib, Vega Altair, D3.js, Observable Plot, Svelteplot
Infrastructure: SLURM, Docker, Git, Cloudflare, Linux