Research Field:

My research primarily focuses on the application of data-driven methods namely: Statistical Learning, Machine Learning and Deep Learning in testing and validation. This approach can be applied to various machine elements, providing valuable insights that aid in product development.

Main Research Topics:

• Data-Driven Testing: Developing robust testing strategies for machine elements by utilizing data, which address the gap in traditional testing methods.
• Validation Techniques: Employing data-driven methodologies to ensure accurate validation of machine element performance and reliability, especially in complex systems.
• Product Development: Leveraging insights from data analysis to enhance the development of more efficient and dependable machine elements, aiming to innovate product development processes.

Bachelor-/Masterarbeiten

• Currently open Bachelor- or Master theses are to be found here.

Publications

You can also find an overview of the publications at https://www.researchgate.net/profile/Nehal-Afifi-6