Professional practice
Technical expert work, scientific methodology, research, publications and teaching.
Professional profile
Ing. Mathias Ellmann, M. Sc., works as an IT expert, technical assessor, specialist author and lecturer. Core fields are software engineering, data and Data Science, machine learning and AI, and the traceable reconstruction and assessment of technical system states and decision evidence.
Expert statements are tied to a defined subject of examination, documented data and system states, disclosed methods and explicit limitations. Technical assessment is kept separate from legal evaluation.
Academic education
- Master of Science (M. Sc.) in Industrial Engineering with focus on International Information Systems and Information & Communication Systems, Friedrich-Alexander-Universität Erlangen-Nürnberg.
- Bachelor of Engineering (B. Eng.) in Electrical Engineering, specialisation in Energy and Automation Engineering, University of Applied Sciences Regensburg.
- University-level higher-education teaching coursework in the Master of Higher Education programme at Universität Hamburg; the programme was not completed as a master's degree.
Research and specialist publications
Publications cover software engineering, Data Science, AI and computer-science education. Own publications supplement expert work but do not replace required independent primary and specialist sources.
- Der Wille in der KI – zwischen Pflichtethik, Motivation, Ich-Zuständen und rechtlicher Verantwortung (2026).
- KI und Ethik in der Praxis: Leitlinien für Entwickler:innen, Data Scientists und Projektverantwortliche (2026).
- Verantwortungsvoller KI-Einsatz in der Praxis: Frameworks, Tools und regulatorische Einbettung in 15 Anwendungsfeldern (2026).
- Fernlehren und Fernlernen von Objektorientierter Programmierung (OOP) (2021).
- Umschulung zum IHK-Fachinformatiker in Anwendungsentwicklung und Systemintegration (2021).
- Effektives Lehren und Lernen in der Informatik, Wirtschaftsinformatik und verwandten Fachgebieten (2022).
Teaching and didactics
Teaching practice includes programming, databases and SQL, software engineering, web technologies and Data Science in classroom and online formats. Higher-education teaching coursework covered lesson planning, evaluation, communication and leadership, didactic models, method diversity, multimedia, e-learning and blended learning.
In 2018, the teaching concept for the Operations Research Lab seminar received the Leuphana Teaching Award. In winter semester 2018/19, the Online Live Trainer continuing-education programme at DIPLOMA Hochschule was completed with certification.
Continuing professional education
Documented online professional development from 2026 totals approximately 190 hours, covering HTML5/CSS3, JavaScript, Java web development, Spring Boot and Hibernate, AWS deployment, Data Science, machine learning, NLP, technical SEO, Scrum, Product Ownership, Kanban and OKR.
Methodological principles
- Define the subject of examination and system state explicitly.
- Document sources, data, provenance and version context.
- Separate finding, interpretation, professional assessment and statement boundary.
- Consider alternative explanations and counter-evidence.
- Do not equate technical evidence with approval or legal permissibility.
Related technical knowledge
Initial technical classification of an enquiry
Before accepting an assignment, the technical question, subject of examination, available records, potential conflicts of interest and the required scope are reviewed. An individual non-binding proposal can then be prepared.
Contact Mathias Ellmann