Rate My Professor Nelly Bencomo

Her research specializations encompass AI and machine learning, decision-making under uncertainty, just transitions, software engineering for autonomous and self-adaptive systems, software engineering for AI/ML systems, autonomous systems, and runtime models. Bencomo has earned significant recognition, including Best Paper Awards at the International Conference on Engineering Digital Twins 2024, SEAMS 2024, and REFSQ 2013; two 10-year Most Influential Paper Awards in 2019 for 'RELAX: Incorporating Uncertainty into the Specification of Self-Adaptive Systems' and 'A Goal-Based Modeling Approach to Develop Requirements of an Adaptive System with Environmental Uncertainty'; a Leverhulme Fellowship for 'QuantUn: quantification of uncertainty using Bayesian surprises' in 2019-2020; and a Marie Curie Intra-European Fellowship. She was listed among Women for Innovation in Health Science in the North of England in 2024. Key publications include the edited volume 'Models@run.time: Foundations, Applications, and Roadmaps' (2014); 'Declarative Lifecycle Management in Digital Twins' (EDTconf 2024); 'Code Gradients: Towards Automated Traceability of LLM-Generated Code' (RE 2024); 'Uncertainty Flow Diagrams: Towards a Systematic Representation of Uncertainty Propagation' (SEAMS 2024); and 'Latency-aware RDMSim' (SEAMS 2024). Her contributions have advanced uncertainty handling and self-adaptation in software engineering.