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 (TUM & U. of Toronto, Canada) and mentored by Prof. Shiqiang Wang (U. of 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 or the list of working papers below. The working papers I post are typically under submission at the time they appear here. If you have comments or critique for them, please let me know. I am always happy to discuss.