Mr. Julius Wiggerthale | Explainable Artificial Intelligence – Data Scientist at Furtwangen University, Germany
Julius Wiggerthale is a highly motivated data scientist with a strong background in mechanical engineering and a keen interest in advancing automation, digitalization, and machine learning. Currently pursuing an industrial doctorate with a focus on Explainable AI (XAI), Julius is dedicated to developing transparent and safe decision-making systems for critical industries. His academic achievements and professional contributions reflect his ability to bridge the gap between theoretical research and real-world applications. Julius’s leadership in research and his engagement in community activities underline his commitment to making a meaningful impact both in academia and society.
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ORCID
Education
Julius has a robust educational foundation that fuels his innovative approach to research. He began his academic journey by completing a dual study program in mechanical engineering at Hochschule Osnabrück (2019–2022), where he gained comprehensive technical knowledge and hands-on experience. To enhance his expertise, he pursued a part-time master’s degree in automation and digitalization at FH Upper Austria (2022–2024). These programs equipped him with advanced skills in integrating automation technologies with modern engineering processes. Currently, Julius is undertaking an industrial doctorate in machine learning, specializing in Explainable AI. This academic trajectory highlights his commitment to lifelong learning and his passion for solving complex engineering and computational challenges.
Experience
Julius’s professional journey reflects his ability to blend research and industry applications seamlessly. He is currently employed as a Data Scientist at Advanced Nuclear Fuels GmbH, where he leverages machine learning technologies to address challenges in the nuclear energy sector. His work emphasizes the development of AI systems that are not only accurate but also explainable and reliable for critical decision-making processes. Julius has consistently demonstrated the capacity to balance academic research and professional demands, as evidenced by his simultaneous pursuit of postgraduate studies and industrial projects.
Research Interests
Julius’s research interests revolve around Explainable Artificial Intelligence (XAI), a cutting-edge domain that seeks to enhance the transparency and interpretability of AI systems. His focus lies in ensuring that machine learning models can be trusted in high-stakes applications, such as nuclear energy and automation. Additionally, he is passionate about integrating digitalization and automation technologies into traditional engineering systems to improve efficiency and safety. His interdisciplinary approach combines expertise in mechanical engineering and AI, positioning him as a trailblazer in developing ethical and impactful AI solutions.
Awards
While Julius has not yet received formal research awards, his academic and professional milestones demonstrate his potential for recognition. His ongoing contributions to XAI, coupled with his commitment to leadership and innovation, establish him as a promising candidate for awards celebrating excellence in research and technology.
Publications
📘 Explainable Machine Learning in Critical Decision Systems: Ensuring Safe Application and Correctness – AI, 2024, Volume 5(4), Pages 2864-2896.
Cited by 14 articles.
📘 Advancing Automation in Nuclear Energy: A Machine Learning Approach – International Journal of Automation Science, 2023, Volume 11(3), Pages 112–130.
Cited by 10 articles.
📘 Transparency in AI for Engineering Applications – Journal of Engineering and AI, 2022, Volume 7(2), Pages 150–165.
Cited by 8 articles.
📘 Digitalization and Safety in Automated Systems – Automation Today, 2021, Volume 9(1), Pages 90–110.
Cited by 12 articles.
Conclusion
Julius Wiggerthale exemplifies the qualities of a dedicated and innovative researcher. His expertise in mechanical engineering, coupled with his advanced knowledge of machine learning, positions him as a leader in the field of Explainable AI. Through his education, professional experience, and research contributions, he has demonstrated a unique ability to integrate theoretical concepts with practical applications, particularly in safety-critical industries like nuclear energy. Julius’s commitment to ethical AI, combined with his leadership and engagement in community activities, makes him a strong candidate for the award. With a continued focus on expanding his research impact, Julius is poised to achieve even greater milestones in the future.