Mathis Teva Rocton

Mathis Teva Rocton is a PreDoc Researcher at the Department of Algorithms and Complexity, Technische Universität Wien (TU Wien). He holds an MSc and the title of Univ.Ass. His research focuses on parameterized complexity, graph theory, and theoretical computer science with applications in artificial intelligence. Rocton is involved in projects such as 'Parameterisierte Analyse in der Künstlichen Intelligenz' (2021–2026) and 'Parameterized Graph Drawing' (2023–2027).

His recent work explores twin-width concepts, feedback edge parameters, and neural network training complexity. Notable publications include contributions to IPEC 2024, STACS 2024, and NeurIPS 2023. He has supervised diploma theses, including David Jäger's work on 'Turbocharging twin-width heuristics with SAT' (2024). Rocton's research bridges algorithmic theory and practical applications in AI.