Daniel Neider

Daniel Neider is a Professor at TU Dortmund University, Germany. He holds former affiliations with Carl von Ossietzky University Oldenburg and the Max Planck Institute for Software Systems (Kaiserslautern). His research focuses on formal methods, automata theory, temporal logic, and their applications in verification, AI, and machine learning. Neider earned his PhD in 2014 from RWTH Aachen University and a second PhD in 2022 from Kaiserslautern University. His work bridges theoretical foundations with practical tools, such as developing algorithms for automata learning, robust temporal logics (rLTL), and neuro-symbolic verification frameworks for deep neural networks.

Key research areas include: (1) learning-based formal methods for system verification, (2) robust temporal logics for handling noisy environments, (3) reinforcement learning with temporal logic constraints, and (4) automated specification mining from data. He has contributed to tools like libALF (Automata Learning Framework) and Sorcar for property-driven invariant synthesis.

His recent work explores: (a) scalable LTL learning from noisy data using MaxSAT, (b) neuro-symbolic verification techniques, and (c) applying large language models to automate reinforcement learning with reward machines. He has published over 135 papers in top venues like AAAI, TACAS, and FMCAD, focusing on formal methods intersections with AI and systems verification.

Current projects involve: (i) robustness-aware synthesis for reactive systems, (ii) temporal logic inference from positive examples, and (iii) developing interpretable verification tools for complex operational domains. His lab collaborates on applying formal methods to real-world systems like smart contracts and neural networks.