Presently I am a research associate at IMDEA Materials Institute in Getafe, Madrid (Spain). At IMDEA Materials, I have been collaborating with several groups developing models and algorithms enhanced by artificial intelligence concepts. In detail, my research has focused on modeling and the development of machine learning algorithms for the control of complex diffusion models applied to disease transmission dynamics and for autonomous materials discovery. Furthermore, we have been developing models for the prediction of metamaterial properties and for the prediction of steel creep behavior.
Before coming to IMDEA, I was a postdoctoral researcher in Elena Akhmatskaya's group on Modeling and Simulation in Life and Materials Sciences at BCAM for two years. Within this role, I mainly focused on predictive modeling of metabolism through Monte Carlo sampling and Machine Learning for metabolic modeling in collaboration with researchers from Lawrence Berkeley National Lab (LBL, USA) and Washington University in St. Louis (USA). I was still an affiliate researcher at LBL and a visiting fellow at BCAM for more than one more year.
Before moving to Spain, I had been a postdoctoral researcher in Lorenz Biegler´s group on Optimization and Numerical Methods for Process Design, Analysis, Operations and Control at Carnegie Mellon University in Pittsburgh, PA (USA) for almost two years.
I received my Bachelor´s, Master´s, and doctoral degree in (Applied) Mathematics from Trier University in Trier (Germany) in 2011, 2013, and 2018, respectively. My Ph.D., under the supervision of Volker Schulz in the group on PDE-Constrained Optimization, focused on Modeling, Simulation, and Optimization of Fermentation Processes.
- Modeling, simulation and optimization with particular focus on energy and healthcare applications:
- Development of models for predicting steel creep behavior
- Development of models for the prediction of metamaterial properties with deep learning
- Development of models and algorithms for autonomous materials discovery
- Mathematical modeling and development of machine learning algorithms for the control of complex diffusion models applied to disease transmission dynamics
- Analysis of systems of reaction-diffusion equations
- Optimal scaling for reducing model complexity for population balance models, e.g. for Latex Particles Morphology Formation
- Machine learning for metabolic modeling and optimal design
- Predictive metabolic modeling through advanced sampling techniques
- Algorithm and software development for variance and parameter estimation of reaction kinetics from spectroscopic data coming from chemical or pharmaceutical processes
- Mixed-effects models for kinetic parameter estimation
- Robust CFD-based optimization of biogas power plants
- Economic nonlinear model predictive control with parameter and state estimation for wine fermentation
- Numerical modeling and analysis of integro-differential equations, systems of weakly hyperbolic differential equations or respectively reaction-advection equations
- Population balance models