People / Students / Class of 2026
Johannes is pursuing an M.S. in Machine Learning and Data Science at Northwestern University, where he focuses on building scalable, efficient, and secure AI systems. He previously graduated first in his class from Baden-Wuerttemberg Cooperative State University with a B.Sc. in Computer Science – Computational Data Science after completing an IT specialist apprenticeship at BSH Home Appliances Group.
Over nearly seven years at BSH, Johannes worked across the full software stack, ranging from production-critical web services and distributed cloud infrastructure to real-time analytics and machine learning systems. As part of the company’s Data Analytics organization, he helped design large-scale AWS-based data platforms processing sensor streams from globally distributed factories, built real-time anomaly detection pipelines, and developed full-stack applications for industrial monitoring and data-driven decision making.
Alongside his industry work, Johannes pursued research at the intersection of data systems and machine learning. His projects include developing a GraphQL-to-Gremlin transpilation framework for graph databases, optimizing large-scale time-series databases for industrial sensor workloads, and training compact OCR and sequence models for CAPTCHA recognition. At Northwestern, he further expanded into applied AI research through projects in multi-agent anomaly detection and AI security, including work on securing Model Context Protocol (MCP)-based agentic systems in collaboration with Amazon Web Services. During his internship at BMW Group, he focuses on benchmarking and optimizing modern language and vision-language models for efficient on-device deployment under strict latency and memory constraints.
Johannes is particularly interested in the technical foundations of modern AI systems, from model architectures and inference optimization to distributed infrastructure, security, and deployment on constrained hardware. Through the MLDS program, he aims to deepen his expertise in building reliable, high-performance AI systems that bridge cutting-edge machine learning research with impactful real-world applications.
