Dr. Christina Schenk

I am a Staff Scientist at the IMDEA Materials Institute in Getafe, Madrid, where I lead the ML4Materials Lab (Machine Learning for Materials Discovery). My research combines machine learning with materials modeling to accelerate the discovery and design of new materials, in close collaboration with several groups across IMDEA. I also hold the R3 Researcher Certificate from the Spanish State Research Agency since 2024 and was recently awarded a prestigious Ramón y Cajal (RyC) grant to further advance my independent research program in machine learning for materials discovery.
Before joining IMDEA, I was a postdoctoral researcher in Elena Akhmatskaya’s group on Modeling and Simulation in Life and Materials Sciences at the Basque Center for Applied Mathematics (BCAM) and an affiliate postdoctoral researcher in Hector García Martín’s group at Lawrence Berkeley National Laboratory (LBL), USA. During this period, I focused on predictive metabolic modeling using Monte Carlo sampling and machine learning, working with collaborators at BCAM, LBL, and Washington University in St. Louis. I continued these collaborations as an affiliate researcher at LBL and a visiting fellow at BCAM for more than a year after my postdoctoral role.
Earlier, I worked as a postdoctoral researcher in Lorenz Biegler’s group at Carnegie Mellon University in Pittsburgh, USA, focusing on optimization and numerical methods for process design, analysis, operations, and control.
I obtained my B.Sc. (2011), M.Sc. (2013), and Ph.D. (2018) in (Applied) Mathematics from Trier University in Germany. My doctoral research, supervised by Volker Schulz in the PDE-Constrained Optimization group, concentrated on the modeling, simulation, and optimization of fermentation processes.

Research

• Modeling, simulation and optimization with particular focus on energy, healthcare and sustainability applications:
• Hybrid methods combining physics-based, data-driven, and probabilistic approaches
• Advanced surrogate modeling techniques
• Optimal experimental design methods
• Process optimization and workflow control
• Development of Bayesian calibration and prediction frameworks
• Modeling and prediction of steel creep behavior
• Deep learning models for predicting metamaterial properties
• Models and algorithms for autonomous materials discovery
• Mathematical modeling and machine learning for controlling complex diffusion models in disease transmission
• Analysis of reaction-diffusion equation systems
• Model complexity reduction via optimal scaling for population balance models (e.g., latex particle morphology formation)
• Machine learning applied to metabolic modeling and optimal design
• Predictive metabolic modeling using advanced sampling techniques
• Algorithm and software development for variance and parameter estimation of reaction kinetics from spectroscopic data in chemical and 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, weakly hyperbolic differential, and reaction-advection equations
• Population balance models



IMDEA Materials Profile

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Current research projects

BAT4FIRE

HERA

AID4GREENEST

DIGIBIOFAM

IRIDISCENTE

SUSCOMPAUTO



Industrial and Public Research Partners

Syspro Automation

IDAERO SOLUTIONS

AENIUM

mim-tech ALFA SLU

Blesol Tech

Renishaw Ibérica, S.A.U.

TheNextPangea S.L.

AIMEN Technology Center

REINOSA Forgings Castings

ePotentia

EurA

OCAS

Lawrence Berkeley National Lab

Festo SE & Co. KG

The Dow Chemical Company

Pfizer Inc.

Eli Lilly and Company

Dienstleistungszentrum Ländlicher Raum Mosel

fp sensor systems

LWG Veitshöchheim

BGA Warsow GmbH & Co. KG

agriKomp GmbH

...

Publications

Articles

C. Schenk, M. Hernández-del-Valle, L. Calero-Lumbreras, M. Noack, M. Haranczyk (2025): Noise-Aware Optimization in Nominally Identical Manufacturing and Measuring Systems for High-Throughput Parallel Workflows (under review, Preprint )

C. Schenk, J. Ayensa Jiménez, I. Romero (2025): Bayesian Calibration and Model Assessment of Cell Migration Dynamics with Surrogate Model Integration (under review, Preprint )

S. Baird,...C. Schenk,...et al. (2025): Bayesian Optimization Hackathon for Chemistry and Materials, ChemRxiv, DOI: 10.26434/chemrxiv-2025-dzh5z ( Preprint )

S. Rusconi, C. Schenk, R. Ceuca, A. Zarnescu, E. Akhmatskaya (2025): Complexity reduction of physical models: an equation-free approach by means of scaling (under review, Preprint )

B. Ozdemir, M. Hernández-del-Valle, C. Schenk, D.Y. Wang, M. Haranczyk (2025): Bayesian Optimization Guiding the Experimental Mapping of the Pareto Front of Mechanical and Flame-Retardant Properties in Polyamide Nanocomposites, Advanced Intelligent Discovery (available here)

C. Schenk, J. Hobson, M. Haranczyk, D.Y. Wang (2025): Data-Driven Design and Green Preparation of Bio-Based Flame Retardant Polyamide Composites, Materials Chemistry A, 13: 26228 - 26243, DOI: 10.1039/D5TA02511G, (available here, Preprint )

C. Schenk, J. Hobson, M. Haranczyk, D.Y. Wang (2025): Data-Driven Design and Green Preparation of Bio-Based Flame Retardant Polyamide Composites (Front Cover Image), Materials Chemistry A, 13:26021-26021, DOI: 10.1039/D5TA90185E (available here)

C. Schenk, M. Haranczyk (2025): A Novel Constrained Sampling Method for Efficient Exploration in Materials and Chemical Mixture Design, Computational Materials Science, 252:113780 (available here Preprint )

B. Ozdemir, M. Hernández-del-Valle, M. Gaunt, C. Schenk, L. Echevarría-Pastrana, J.P. Fernandez-Blazquez, D.Y. Wang, M. Haranczyk (2024): Toward 3D printability prediction for thermoplastic polymer nanocomposites: Insights from extrusion printing of PLA-based systems, Additive Manufacturing, 105533 (available here, Preprint )

C. Schenk, A. Vasudevan, M. Haranczyk, I. Romero (2024): Model-Based Reinforcement Learning Control of Reaction-Diffusion Problems, Optimal Control Applications and Methods, 45(6):2897-2914 (available here , Preprint )

M. Hernández-del-Valle, C. Schenk, L. Echevarría-Pastrana, B. Ozdemir, E. Dios-Lázaro, J. Ilarraza-Zuazo, D.-Y. Wang, and M. Haranczyk (2023): Robotically automated 3D printing and testing of thermoplastic material specimens, Digital Discovery, 2023, 2(6):1969-1979 (available here)

T. Backman, C. Schenk, T. Radivojevic, D. Ando, J. Singh, J. J. Czajka, Z. Costello, J. D. Keasling, Y. Tang, E. Akhmatskaya, H. Garcia Martin (2023): BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale, PLOS Computational Biology (available here , Preprint )

J. Bartsch, A. Borzì, C. Schenk, D. Schmidt, J. Müller, V. Schulz, K. Velten (2023): An Extended Model of Wine Fermentation Including Aromas and Acids. in: J. Wittmann, K. Chudej (eds), Simulation in den Umwelt- und Geowissenschaften, Workshop Bayreuth 2023, Berichte aus der Umweltinformatik, Shaker Verlag, ISBN: 9783844092509, pp 125-136 ( Preprint)

I. Romero, C. Schenk (2023): Connecting beams and continua: variational basis and mathematical analysis, Meccanica, 58:1973–1982, DOI: 10.1007/s11012-023-01702-0, (available here)

G. W. Roell, C. Schenk, W. E. Anthony, R. R. Carr, A. Ponukumati, J. Kim, E. Akhmatskaya, M. Foston, G. Dantas, T. S. Moon, Y. J. Tang, H. García Martín (2023): A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism, ACS Synthetic Biology, 12(6):1632–1644, DOI:10.1021/acssynbio.2c00618 (available here, Supplementary Info )

C. Schenk, D. Portillo, and I. Romero (2023): Linking discrete and continuum diffusion models: Well-posedness and stable finite element discretizations, Int. Journal for Numerical Methods in Engineering, 124(9):2105-2121, DOI: doi.org/10.1002/nme.7204 (available here, Preprint )

S. Rusconi, C. Schenk, A. Zarnescu and E. Akhmatskaya (2023): Reducing model complexity by means of the optimal scaling: Population balance model for latex particles morphology formation, Applied Mathematics and Computation, 443:127756, DOI: 10.1016/j.amc.2022.127756 (available here)

C. Schenk and V. Schulz (2022): Existence, Uniqueness, and Numerical Modeling of Wine Fermentation Based on Integro-Differential Equations, SIAM Journal on Applied Mathematics, 82(4), DOI: 10.1137/20M1362309 (available here, Preprint)

J. Bartsch, A. Borzì, J. Müller, C. Schenk, D. Schmidt, V. Schulz and K. Velten (2021): Energy conservation for wine fermentation - Nonlinear model predictive control for placement and real-time control of cooling plates, in: A. Milde, K.-H. Küfer, P. Maass and V. Schulz (eds.), "German Success Stories in Industrial Mathematics", Mathematics in Industry, Springer (available here or here)

C. Schenk, L. T. Biegler, L. Han and J. Mustakis (2020): Kinetic Parameter Estimation from Spectroscopic Data for a Multi-Stage Solid-Liquid Pharmaceutical Process, Organic Process Research & Development, DOI: 10.1021/acs.oprd.0c00277 (available here, Preprint)

J. Müller*, C. Schenk*, R. Keicher, D. Schmidt, V. Schulz and K. Velten (2020): Optimization of an Externally Mixed Biogas Plant Using a Robust CFD Method, Computers and Electronics in Agriculture, Volume 171, 105294, DOI: 10.1016/j.compag.2020.105294 (available here, Preprint)

C. Schenk*, M. Short*, J. S. Rodriguez, D. Thierry, L. T. Biegler, S. Garcia-Muñoz, W. Chen (2020): Introducing KIPET : A novel open-source software package for kinetic parameter estimation from experimental datasets including spectra, Computers and Chemical Engineering, Volume 134, 106716, DOI: 10.1016/j.compchemeng.2019.106716 (available here, Preprint)

M. Short, C. Schenk, D. Thierry, J. S. Rodriguez, L. T. Biegler, S. Garcia-Muñoz (2019): KIPET - An Open-Source Kinetic Parameter Estimation Toolkit, Computer Aided Chemical Engineering, Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design, Volume 47, Pages: 299-304, DOI: 10.1016/B978-0-12-818597-1.50047-3 (available here, Preprint)

C. Schenk, V. Schulz, A. Rosch, C. von Wallbrunn (2017): Less cooling energy in wine fermentation - A case study in mathematical modeling, simulation and optimization, Food and Bioproducts Processing, Volume 103, Pages: 131-138, DOI: 10.1016/j.fbp.2017.04.001 (available here, Preprint)

C. Schenk, V. Schulz (2015): Energy-optimal control of temperature for wine fermentation based on a novel model including the yeast dying phase. IFAC-PapersOnLine, 48(23), 5th IFAC Conference on Nonlinear Model Predictive Control (NMPC'15), Seville, Spain, 452-457, doi:10.1016/j.ifacol.2015.11.320 (available here, Preprint)

*: Co-first authors

Preprints

• A. Borzì, J. Merger, J. Müller, A. Rosch, C. Schenk, D. Schmidt, S. Schmidt, V. Schulz, K. Velten, C. von Wallbrunn, M. Zänglein (2014): Novel model for wine fermentation including the yeast dying phase. ArXiv-Preprint, arXiv:1412.6068 (available here)

Book Reviews

• C. Schenk (2019): Review of Pyomo: Optimization Modeling in Python (William E. Hart, Carl D. Laird, Jean-Paul Watson, David L. Woodruff, Gabriel A. Hackebeil, Bethany Nicholson, and John D. Siirola, 2nd Edition). SIAM Rev., 61(1), pp. 209-225 (available here)

Theses

• C. Schenk (2018): Modeling, Simulation and Optimization of Wine Fermentation, PhD thesis, Examiners: Prof. Dr. Volker Schulz (Trier University) and Prof. Dr. Lorenz T. Biegler (Carnegie Mellon University) (available here)
• C. Schenk (2013): Optimal Design of Cardiovascular Stents, Master's thesis
• C. Schenk (2011): Nonlocal Diffusion Equations, Bachelor's thesis

Software

• ACBICI - A Configurable BayesIan Calibration and Inference package
• CASTRO - ConstrAined Sequential laTin hypeRcube sampling methOd
• BayFLUX - A Bayesian method to quantify metabolic FLUXes and their uncertainty at the genome scale
• KIPET - Kinetic Parameter Estimation Toolkit

Media

C. Schenk (2025) - Dissemination article on “Is it possible to reduce fires in the home and industry without using toxic compounds?” - "Es posible reducir incendios en el hogar y la industria sin usar compuestos tóxicos?" (in Spanish), The Conversation, DOI: 10.64628/AAO.jnut7knh9, (available here)

C. Schenk (2025)- Testimonial "How to tackle a $10 million-$1 billion bottleneck: building smarter tools for faster, more efficient drug and materials discovery" (available here)

C. Schenk (2025): Talk at Pint of Science Madrid on “The Future of Materials: Powered by AI and Robots!” - "El futuro de los materiales: ¡Impulsado por IA y Robots!" (in Spanish) (Abstract)

Tackling the green steel challenge: AID4GREENEST celebrates its one-year anniversary (2024)

F. Wang, Q. Gallagher, A. Gupta, C. Schenk (2024): What is the role of molecular featurization in Bayesian optimization? (Github repo), 5th Place in Bayesian Optimization Hackathon for Chemistry and Materials 27-28 March 2024, sponsored by the Acceleration Consortium and Merck KGaA

C. Schenk (2024): Image contribution to 2024 Math Remix Creative Challenge

Postdoctoral Research at IMDEA Materials with Christina Schenk (2023)

C. Schenk (2023) - Testimonial, Her Maths Story (available here)

C. Schenk (2023) - Testimonial, Mathematics Embassy at Trier University (in German) (available here)

The Voices of IMDEA Materials - Volume 1 (2022)

Get to know IMDEA Materials with Christina Schenk (2022)

C. Schenk (2021) - Testimonial, Women at BCAM, BCAM Activity Report 2020 (available here)

C. Schenk (2021) - Testimonial, FemSTEM (available here)

C. Schenk (2021) - Talk at San Felix School in Ortuella to 13-year-old students on "The role of hidden figures for becoming the next Katherine Johnson, Marie Curie or Ada Lovelace" ("El papel de las figuras ocultas para convertirse en la próxima Katherine Johnson, Marie Curie o Ada Lovelace") (in English and Spanish) as part of the "11 de febrero” initiative in the context of the International Day of Women and Girls in Science, BCAM News Article.

Article on our "Optimization of an Externally Mixed Biogas Plant Using a Robust CFD Method" article in “Mapping Ignorance”: Optimal mixer placement in industrial-size biogas fermenters by César Tomé López (2020) (available here)

C. Schenk (2020): The scientists who inspired us (I): Ada Lovelace, BCAM News (available here)

L. Biegler: The Optimization of Chemical Engineering

Zauberei im Keller (German)

Study Mathematics at Trier University (German)

C. Schenk (STEM Woman) (since 2015): MINT-Frau (STEM Woman) Ada Lovelace Project (in German) (Profile available here)

Activities

Contact Me

• 
  IMDEA Materials Institute,
  C/ Eric Kandel, 2
  Tecnogetafe
  28906, Getafe, Madrid (Spain)
  
• Christina Schenk (IMDEA Materials)
  


• mail[at]christinaschenk.de, christina.schenk[at]imdea.org



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