Daniel Neider

University of California, Los Angeles, Max Planck Institute for Software Systems

Papers

Total Citations

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About

Daniel Neider is a leading researcher in formal methods, with a focus on automata learning, temporal logic, and controller synthesis for complex dynamical systems. His work bridges the gap between theoretical computer science and practical verification, enabling automated reasoning about safety and performance in infinite-state systems. Neider’s most cited paper, “An Automaton Learning Approach to Solving Safety Games over Infinite Graphs” (2016, 32 citations), introduces a novel technique that leverages learning algorithms to efficiently solve safety games—a foundational problem in reactive synthesis. He further advanced the field with “Scalable Anytime Algorithms for Learning Fragments of Linear Temporal Logic” (2022, 25 citations), which provides practical methods for inferring LTL specifications from traces, with applications in program verification, robotics, and process mining. His work on “Resilient abstraction-based controller design” (2020) extends these ideas to perturbed nonlinear systems, ensuring robust satisfaction of temporal logic specifications. Neider’s contributions are highly impactful, offering scalable, anytime solutions that make formal methods more accessible for real-world engineering challenges. His research continues to shape how we design and verify safe, intelligent systems.