About

As a PhD student at Stanford University, I work with Professor Stephen Boyd on software tools for modeling and solving optimization problems.

Before attending Stanford, I was a consultant at McKinsey Digital, where I worked on data science projects in the automotive and energy sectors. I received my MSc in Robotics, Systems and Control from ETH Zurich, where I was awarded the Willi Studer Prize for the best degree and the ETH Medal for my Master's thesis. I received my BSc in Mechanical and Process Engineering from TU Darmstadt, winning the VDI Prize for the best degree.

Publications

• M. Schaller, G. Banjac, S. Diamond, A. Agrawal, B. Stellato, S. Boyd, "Embedded Code Generation with CVXPY", IEEE Control Systems Letters 6, 2653-2658, 2022. Read paper
• P. Duhr, M. Schaller, L. Arzilli, A. Cerofolini, C. H. Onder, "Time-Optimal Energy Management of the Formula 1 Power Unit with Active Battery Path Constraints", 2021 European Control Conference (ECC), 913-920, 2021. Read paper
• P. Duhr, M. Schaller, L. Arzilli, A. Cerofolini, C. H. Onder, "Analysis of Optimal Energy Management Strategies for the Hybrid Electric Formula 1 Car", 38th FISITA 2021 World Congress, 2021. Read paper
• S. Löckel, S. Ju, M. Schaller, P. van Vliet, J. Peters, "An Adaptive Human Driver Model for Realistic Race Car Simulations", IEEE Transactions on Systems, Man, and Cybernetics: Systems, 6718-6730, 2023. Read paper

Software

CVXPYgen is a tool that generates custom solver implementations in C for convex optimization problem families modeled with CVXPY. It is suitable for embedded systems and also provides a Python wrapper for desktop applications.

Teaching

I have been a teaching assistant for the following courses at Stanford and ETH:

• EE 364A: Convex Optimization I. Professor Stephen Boyd. Stanford. Winter 2024-25.
• 151-0573-00: System Modeling. Professor Lino Guzzella. ETH. Autumn 2020-21.