Herbert Woisetschläger

I recently graduated with a PhD in computer science from the Technical University of Munich, Germany, specializing in a wide range of machine learning applications, including Large Language Models (LLMs), generative AI, and agentic systems, in both centralized and distributed settings. My work follows two major themes: democratization of machine learning tools and system efficiency (e.g., by automating choices based on operating cost). Throughout my PhD, I was fortunate to collaborate with fantastic people on federated learning in resource-constrained environments, LLM pre-training, and cost optimal inference using LLM zoos. We managed to place our work in top-tier conferences like NeurIPS, ICLR, IJCAI, and ACM Middleware. For my PhD, I was advised by Prof. Hans-Arno Jacobsen (Toronto, Canada) and mentored by Prof. Shiqiang Wang (Exeter, UK). For a broader perspective on industry research, I spent two summers at the IBM T.J. Watson Research Center in the U.S., working on various aspects of pre- and post-training of IBM Granite models.

Before I started my PhD, I spent two years working as a management consultant for the German and Swiss branch of Capgemini Invent specializing in performance and cost optimization programs for consumer good retailers, financial institutions, and manufacturers. I also did an internship at Detecon, Inc. in Silicon Valley working on corporate startup incubation as well as accelerator programs for the public sector. This is where I built interest in optimizing and automating business processes through quantitative methods.

For my recent research contributions, please check out my Google Scholar profile.