Greater Madrid Metropolitan Area
• Applied Mathematician • Scientist • currently working in data science at the intersection of Applied Mathematics and Materials Science ✔️ helping combat some key issues related to climate change ✔️ making an impact with key topics related to health, energy, sustainability, and other material challenges ✔️ I love research (R&D), learning, making an impact, employing my Maths and Data Science skills in exciting applications, diving into new fields of science, innovation, and networking. ✔️ I cannot live without (multicultural) communication, science, learning about other cultures, and creating comfortable and constructive environments. ✔️ My mission is to inspire other young people to join us in addressing some of the key challenges concerning our planet, to continue to impact crucial topics, and to make the world a better place. My website: https://christinaschenk.de/, https://materials.imdea.org/christina-schenk/ In detail my main interests lie in the following topics: • mathematical modeling • optimization • nonlinear differential equations • numerical analysis and methods • control • data science and machine learning • data analytics • experimental design • Bayesian and statistical inference • uncertainty quantification • applied analysis • scientific computing • software development • energy and healthcare (pharma, biochemistry, synthetic biology, chemistry, mechanics, materials)
Leading the ML4Materials (Machine Learning for Materials Discovery) Lab. In this role, she collaborates with and supports several research groups at IMDEA Materials in key areas like: - Calibration and prediction of materials behavior for enhanced characterisation and design - Hybrid methods combining physics-based, data-driven, and probabilistic approaches - Advanced surrogate modeling techniques - Optimal experimental design through Bayesian optimization and advanced design of experiments methodologies - Other process control and optimization methods
Modeling and machine learning for materials discovery, property prediction, and process optimization in close collaboration with several groups across IMDEA Materials.
Research especially with respect to Predictive Modeling of Metabolism Through Monte Carlo Sampling and Bayesian Inference, Machine Learning for Metabolic Modeling and Design, and Analysis and Numerical Mathematics for Systems of Partial/Ordinary Integro-differential Equations. Group of Prof. Elena Akhmatskaya on Modeling and Simulation in Life and Material Sciences
Research especially with respect to Predictive Modeling of Metabolism Through Monte Carlo Sampling and Bayesian Inference and Machine Learning for Metabolic Modeling and Design. Group of Dr. Héctor García Martín on Quantitative Metabolic Modeling
Research especially with regard to the development of algorithms and software for parameter estimation of pharmaceutical processes at the Chemical Engineering Department in the Research Group of Prof. Lorenz T. Biegler
Research especially in the context of the BMBF project on Robust Energy-Optimization of Fermentation Processes for the Production of Biogas and Wine at the Mathematics Department in the Research Group of Prof. Volker H. Schulz