ePotentia is a SME focused on full-stack AI development, ranging from efficient data gathering to large-scale cloud deployment. Unlike other AI companies, ePotentia has a strong focus on the fundamental aspects of the data, with each member of the core team having Ph.D.-level scientific expertise. The use case of ePotentia focuses on designing a database platform, which can store microstructural and related data in a standardized way, while at the same time providing statistical insights, automatic classification and annotation of data stored within the database platform, integration of the AI tools developed in the other business cases, and development and integration of explainable AI tools with the developed AI models.
Founded ePotentia after a research career at Ghent University, with a focus on AI-guided virtual screening of semiconducting compounds. Dedicated to applying innovative AI solutions to scientific problems. Main lecturer of the AI for Materials science course. Will be focusing on project management and architecture design during the project, as well as coordinating outreach.
After gaining research experience at Aalto university in advanced intermetallic materials science she spent several years as a business analyst at Groupon. At ePotentia she combines her previous experiences as scientific machine learning engineer. Barbara will be handling MLops for the project, ensuring seamless interoperability between machine learning models and data systems.
At Ghent University, she researched the behaviour of iron under extreme conditions using innovative simulations. Now she focuses on applying AI to materials data, as well as building user-friendly data exploration platforms at ePotentia. In AID4Greenest she will be applying these techniques for model development as well as frontend design as well as helping with outreach
Obtained two PhDs in physics and molecular magnetism at Adam Mickiewicz University and EIMM. Later she worked on the NOMAD materials database including their classification system and AI toolkit. During the project she will be focusing on the development of innovative materials AI models as well as database design, ensuring scientific robustness.