MODELS 2024
Sun 22 - Fri 27 September 2024 Linz, Austria
Nelly Bencomo

Registered user since Wed 31 Aug 2016

Name:Nelly Bencomo
Bio:

Nelly Bencomo is an Associate Professor in Computer Science at Durham University in the UK (since September 2021). In 2019, she was granted the Leverhulme Fellowship “QuantUn: quantification of uncertainty using Bayesian surprises.” Nelly is the principal investigator of the research project Twenty20Insight funded under the EPSRCs to work on Software Engineering, RE, and AI (2020-2023). Before, she was an EU Marie Curie Fellow, from May 2011-May 2013 under a Marie-Curie Fellowship (Grant) Requirements@run.time: Requirements-aware Systems. She was a Senior Researcher at Lancaster University until May 2011 after being was awarded her Ph.D. in Computer Science by Lancaster University in 2008. Nelly exploits the interdisciplinary aspects of software engineering, comprising both technical and human concerns while developing techniques for intelligent, autonomous and highly-distributed systems. With other colleagues, she coined the research topics models@run.time and requirements@run.time. Nelly has actively participated in different European Projects and the EPSRC in the UK in the area of self-adaptive and autonomous systems. She was the program chair of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) in 2014, and co-program chair of the 12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) in 2018. Nelly is an Associate Editor of IEEE Transactions on Software Engineering (TSE) and a member of the Editorial Board of the Journal of Software and Systems Modeling.

Country:United Kingdom
Affiliation:Durham University
Research interests:Decision-making under Uncertainty, Bayesian Inference and ML for Autonomous and Adaptive Systems, Responsible Software, AI Ethics, Human Values in Software, Software Engineering, Requirements Engineering, MDE, runtime models

Contributions

Show activities from other conferences

Using general profile