Team

[ML4EMS] Machine Learning for Energy and Material Science: From Theory to Industry Applications

International Workshop on Machine Learning for Scientific Discovery in Advanced Materials

Cecília Coelho, Helmut Schmidt University, Hamburg, Germany Postdoctoral researcher at the Professorship of Computer Science in Mechanical Engineering, Helmut Schmidt University, Hamburg, Germany. PhD in Mathematics. Her current research focuses on enhancing the performance of real-world systems modelling (physics, biology, chemistry, finance and engineering) by exploring the symbiosis of differential equations and neural networks and the integration of expert-knowledge, in the form of explicit constraints, into neural networks. Her research topics are: Theoretical Machine Learning; Optimisation for Machine Learning; Scientific-Machine Learning; Differential Equations; Numerical Analysis; Climate AI. Publication in major AI venues including ICLR 2023/2024 and workshops at ECML PKDD 2023 and ECAI 2023/2024. Actively contributes to the academic community as a reviewer for major AI conferences, including NeurIPS, ICLR, and IJCAI. Organizer of the ECAI 2024 (mlde-ecai-2024.github.io) and 2025 (mlde-ecai-2025.github.io) Workshop [ML-DE] "Machine Learning Meets Differential Equations: From Theory to Applications", and the Mini-Colloquia "Integrative approaches in physics: using machine learning to explore magnetism, disordered media, and materials science." at the General Conference of the Condensed Matter Division (CMD31) 2024 (cmd31.sci-meet.net/mini-colloquia). Organiser and chair of the ECAI 2024 (symbiosisnn-des.github.io) and 2025 (mlde-ecai-2025.github.io) Tutorial "The Symbiosis of Neural Networks and Differential Equations: From Physics-Informed Neural Networks to Neural ODEs" and ECML 2025 (nnsdestutorial.github.io) Tutorial "Neural Networks and Differential Equations: From Infinite Layers to Continuous Modelling.".

Doaa Mohamed, Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr University Bochum, Germany PhD researcher at the Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr University Bochum. Her research focuses on machine learning under data-scarce conditions, active learning, uncertainty-aware modelling, and the development of scientific data infrastructures for advanced materials discovery. She also works on the design and integration of research databases supporting structured data management, interoperability, and reproducible machine learning workflows in materials science.

Luís Ferrás, Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal Assistant Professor at the Department of Mechanical Engineering (Section of Mathematics), Faculty of Engineering, University of Porto (FEUP), and a researcher at the Centre of Mathematics, University of Minho, Portugal. He received his PhD in Science and Engineering of Polymers and Composites from the University of Minho in 2012, a Ph.D. in Mathematics from the University of Chester in 2019, and was a visiting researcher at MIT in 2016 and 2017. His current research interests are numerical analysis, applied mathematics, partial and fractional differential equations, mathematical modelling, computational mechanics, computational fluid dynamics, complex viscoelastic flows, rheology, anomalous diffusion, and machine learning. He organised several conferences and workshops on Numerical Analysis and Applied Mathematics, such as the "International Workshop on Analysis and Numerical Approximation of Singular Problems" - IWANASP 2023 (iwanaspconference.wordpress.com), and the symposium "Applied Mathematics and Scientific Computing: from numerical methods to machine learning" at the 21st International Conference of Numerical Analysis and Applied Mathematics (ICNAAM) in 2023. Organizer of the ECAI 2024 (mlde-ecai-2024.github.io) and 2025 (mlde-ecai-2025.github.io) Workshop [ML-DE] "Machine Learning Meets Differential Equations: From Theory to Applications", and the Mini-Colloquia "Integrative approaches in physics: using machine learning to explore magnetism, disordered media, and materials science." at the General Conference of the Condensed Matter Division (CMD31) 2024 (cmd31.sci-meet.net/mini-colloquia). Organiser and chair of the ECAI 2024 (symbiosisnn-des.github.io) and 2025 (mlde-ecai-2025.github.io) Tutorial "The Symbiosis of Neural Networks and Differential Equations: From Physics-Informed Neural Networks to Neural ODEs" and ECML 2025 (nnsdestutorial.github.io) Tutorial "Neural Networks and Differential Equations: From Infinite Layers to Continuous Modelling.". Earlier, he co-organised events such as the 11th OpenFOAM Workshop (2016), several FOAM@PT and Iberian Meetings of OpenFOAM® Technology (2015–2019), and international conferences including 15th International Conference on Computational and Mathematical Methods in Science and Engineering (CMMSE) 2015 and the National Meeting of the Portuguese Society of Mathematics (2018).

Markus Stricker, Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr University Bochum, Germany Junior Professor for Materials Informatics and Data Science at the Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr University Bochum. He received his PhD in Mechanical Engineering from Karlsruhe Institute of Technology. His research focuses on exploiting the materials data space across simulations, experiments, and scientific literature, with particular emphasis on building representation bridges between different data modalities. He has organised and contributed to several workshops and conferences in artificial intelligence for materials science.

María Ortiz de Zuniga, Fusion for Energy (F4E) María is Head of Project Engineering, CAD and Data Management at Fusion for Energy (F4E), where she leads AI-driven engineering developments for the International Thermonuclear Experimental Reactor (ITER) fusion program. She holds Master's degrees in Mechanical Engineering and Computer Engineering and is currently pursuing a PhD in Artificial Intelligence applied to nuclear manufacturing. With over 17 years of experience in large-scale nuclear and fusion projects, she has led multidisciplinary teams and managed complex technical contracts spanning advanced manufacturing, nuclear mechanical systems, and quality assurance. Her research focuses on artificial intelligence for manufacturing and materials inspection, particularly data-driven methods for ultrasonic testing, welding quality control, and defect detection in ITER components. She has published multiple peer-reviewed papers on AI-based inspection and validation of nuclear manufacturing processes. Maria is an active member of the American Society of Mechanical Engineers (ASME), Seconded National Experts (SNE), and the Asociación Española para la Inteligencia Artificial (AEPIA), and serves as co-chair of the ASME PVP Computer Technologies Technical Committees CT-19 and MF-37. She has also co-led the Artificial Intelligence Community at Fusion for Energy, promoting the adoption of AI in high-reliability engineering environments.

Alaa Tharwat, Bielefeld University of Applied Sciences and Arts (HSBI), Germany Postdoctoral researcher and research group leader at Bielefeld University of Applied Sciences and Arts. His research interests include machine learning with small data, active learning strategies, and optimisation methods for Industry 4.0 applications. He has organised multiple workshops, tutorials, and special sessions at international conferences such as ECML PKDD and IJCNN.

Oliver Niggemann, Helmut Schmidt University, Hamburg, Germany Full Professor at Helmut Schmidt University, chair of "Computer Science in Mechanical Engineering" with a focus on AI and machine learning in cyber-physical systems. His contributions are evident in his publications at major AI conferences like AAAI (2015, 2019) and his organizer/chairing roles, i.e. at IJCAI 2021 workshop on AI and Product Design (www.hsu-hh.de/imb/en/ijcai-2021-workshop-ai-and-product-design), at International Workshop on Principle of Diagnosis (DX) 2021 (www.hsu-hh.de/imb/en/dx-2021), at the conference Machine Learning for Cyber-Physical Systems (ML4CPS) 2023/2024/2025/2026 (www.hsu-hh.de/imb/en/ml4cps), or at AAAI 2024 workshop on AI Planning for Cyber-Physical Systems (www.hsu-hh.de/imb/en/aaai24-caipi). He is also a PC member at ECAI 2023. IJCAI (2023, 2022, 2021, 2020, 2018) and AAAI (2022, 2021, 2020, 2018, 2017, 2016). Organizer of the ECAI 2024 (mlde-ecai-2024.github.io) and 2025 (mlde-ecai-2025.github.io) Workshop [ML-DE] "Machine Learning Meets Differential Equations: From Theory to Applications". He is the Principal Investigator of several research projects using ML for material science, such as for aircraft maintenance, sheet metal forming, material damage due to fire, among others.

Lei Zhang, Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr University Bochum, Germany Postdoctoral researcher at ICAMS, Ruhr University Bochum, working on data-driven approaches to accelerate materials discovery. His research focuses on applying natural language processing and machine learning to scientific literature mining, multi-modal learning, and optimisation for electrocatalyst discovery combined with experimental validation.