Rate My Professor Felipe Campelo

Felipe Campelo is Associate Professor in Data Science in the School of Engineering Mathematics and Technology at the University of Bristol. He holds a BSc, an MSc, and a PhD. His research centres on data science and optimisation approaches applied to a variety of problems in science and engineering, with a particular emphasis on applications in biology and bioinformatics. Campelo is a member of the Research in Intelligent Systems Laboratory at Bristol.

Prior to his current role, Campelo served as Senior Lecturer in Computer Science at Aston University from February 2019 to July 2024. He previously held positions at the Federal University of Minas Gerais, including Associate Professor of Optimisation and Computational Intelligence in the Department of Electrical Engineering from August 2018 to January 2019, and Assistant Professor in the same department from August 2010 to June 2018. Additionally, he is Adjunct of the Faculty of Graduate Studies at Dalhousie University since January 2021. Campelo's scholarly contributions have significantly impacted the fields of evolutionary computation, multi-objective optimisation, and computational intelligence. Notable publications include 'Metaphor-based metaheuristics, a call for action: the elephant in the room' (2022, Swarm Intelligence), 'A clonal selection algorithm for optimization in electromagnetics' (2005, IEEE Transactions on Magnetics), 'Electric distribution network expansion under load-evolution uncertainty using an immune system inspired algorithm' (2007, IEEE Transactions on Power Systems), 'Pareto cone ε-dominance: improving convergence and diversity in multiobjective evolutionary algorithms' (2011, International Conference on Evolutionary Multi-Criterion Optimization), and 'Lessons from the Evolutionary Computation Bestiary' (2023, Artificial Life). Recent works encompass 'Phylogeny-aware linear B-cell epitope predictor detects cryptic epitopes' (2024, Briefings in Bioinformatics), 'Hybrid simulation-optimization approach for power distribution systems resilience assessment' (2026, Reliability Engineering and System Safety), and 'Multivariate stacked regression pipeline to estimate correlated macro and micronutrients in potato plants using visible and near-infrared reflectance spectra' (2026, Artificial Intelligence in Agriculture). His research bridges methodological advancements in optimisation with practical applications in health, agriculture, and power systems.