PhD Candidate (since 03/23)
Research Area:
How can AI improve company-internal productivity and foster work quality for effective decision-making by changing processes and ways of working within large-scale and transnational organizations.
→ Effective AI adoption and scaling inside large and transnational organizations
→ Defence Industry specific challenges within AI adoption context
Summary:
After my bachelor’s degree in International Business in cooperation with Airbus, I decided to pursue my M.Sc. in Technology Management at Columbia University in the City of New York. During that time, I also spent eight months at the UN Secretariat within the Department of Management Strategy, Policy, and Compliance, which further fostered my interest in technology and business. Currently, I am a Business Operations Manager and the Data Officer for Airbus Defence Digital & Cyber, and External PhD candidate at the TUM CSO.
Professional Background:
Airbus Defence and Space (Munich) UN Secretariat (New York) DMG Mori (Munich) BoxOrganizer (Munich)
Educational Background:
Columbia University University of California Santa Barbara (UCSB) Baden-Württemberg Cooperative State University Ravensburg
Hobbies:
Soccer, Skiing, Traveling, Endurance Sports, Golf, Bouldern
Publications:
Leyh, N. (2026). Automated Machine Learning in Action: A Performance Evaluation for Predictive Analytics Tasks. Acta Informatica Pragensia, 15(1), Forthcoming article. https://doi.org/10.18267/j.aip.288
Coming soon (already accepted):
Wieland, D.; Leyh, N.; Ahrens, F. (2026). From Prompts to Probes: How large language models improve response quality in open-ended survey research. In Proceedings of 59th Hawaii International Conference on System Sciences (HICCS) 2026.
Leyh, N. (2026). Can AutoML Handle the Constraints of Finance? A Domain-Specific Benchmark of Automated ML Frameworks and TabPFN. In Proceedings of Australasian Conference on Information Systems (ACIS) 2025.
Conference Presentations:
Leyh, N. L. (2025). Elevating Product Portfolio Management: Opportunities with Retrieval Augmented Generation Systems. Academy of Management 2025.
Leyh, N. L. (2025). Navigating Challenges in Product Portfolio Management: Harnessing Retrieval-Augmented Generation Systems in the Aerospace and Defense Industry. European Academy of Management 2025.
Leyh, N. (2025). Automated Machine Learning in Action: A Performance Evaluation for Predictive Analytics Tasks [Conference presentation]. 8th International Conference on Research in Management, Cambridge, United Kingdom.
Leyh, N. (2025). Evaluating AutoML Performance: Insights from Financial Predictive Analytics Tasks [Conference presentation]. 8th International Conference on Research in Management, Cambridge, United Kingdom.
Leyh, N. (2025). Evaluating AutoML Frameworks and TabPFN for Financial Predictive Analytics Tasks: A Benchmark Study. Jahrestagung der Wissenschaftlichen Kommission Technologie, Innovation and Entrepreneurship - TIE 2025
Contact:
Email: nicolas.leyh@tum.de
Open Thesis / IDP Project Offers below:
