Sergio Lucia
Professor at TU Dortmund University
Dortmund, North Rhine-Westphalia, Germany
Summary
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Rockstar🎓
Top SchoolSergio Lucia is a Professor at TU Dortmund University and head of the Laboratory for Process Automation Systems, with 11 years of experience applying automatic control, AI and numerical optimization to energy-efficient and sustainable industrial processes. He holds a PhD summa cum laude in Control and Optimization and has led labs for smart buildings and IoT at multiple German universities, plus a postdoc stint at MIT, blending deep theory with practical deployments. Sergio contributes to open-source tooling for model predictive control—adding soft constraints and solver scaling to the widely used do-mpc Python toolbox—reflecting a focus on robust, production-ready control software. He is known for bridging academic research and engineering implementation, designing control architectures that scale from laboratory prototypes to real-world building and process automation.
11 years of coding experience
10 years of employment as a software developer
Diploma in Engineering, Control and Automation, Diploma in Engineering, Control and Automation at Universidad de ZaragozaDoctor of Philosophy (Ph.D.), Control and optimization, Summa cum laude, Doctor of Philosophy (Ph.D.), Control and optimization, Summa cum laude at TU Dortmund UniversityControl Systems, Astronautics, Control Systems, Astronautics at Technische Universität Berlin
Spanish, English, German
Github Skills (4)
mpc10
model-predictive-control10
casadi10
python10
Programming languages (1)
Github contributions (5)
do-mpc/do-mpc
May 2015 - Feb 2020
Model predictive control python toolbox
Role in this project:
Back-end Developer
Back-end DeveloperContributions:3 releases, 123 commits, 59 pushes in 4 years 9 months
Contributions summary:Sergio implemented soft constraints and new examples within the model predictive control (MPC) Python toolbox. They modified and scaled code within setup functions, specifically `setup_nlp.py` and `setup_solver.py`, which are central to the MPC framework. The changes involved scaling states and controls and including additional constraints within an example, which indicates the user's focus on core functionality of the toolbox.
model-predictive-controlpythonmhe-es
sergiolucia/DO-MPC
Dec 2014 - May 2015
Contributions:3 commits, 2 pushes in 5 months
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