STOEAN Ruxandra – Adjunct Research Associate Professor


Affiliation: Romanian Institute of Science and Technology, Cluj-Napoca, Romania / University of Craiova, Romania.

Ruxandra is also affiliated to the University of Malaga, Spain, where she co-supervises PhD students. Her current research interests include deep learning for image interpretation in medicine, engineering and cultural heritage, together with evolutionary optimization. She is an academic editor for PLOS ONE and expert evaluator for the European Commission.

Fields of interest/specialization: Deep learning, Machine learning, Evolutionary Computation, Artificial Intelligence.

Google Scholar:

Representative works:

  • Ruxandra Stoean, Analysis on the potential of an EA–surrogate modelling tandem for deep learning parametrization: an example for cancer classification from medical images, Neural Computing and Applications (Q1), 32, pp. 313–322,, WOS:000511974900002, 2020.
  • Nebojsa Bacanin, Ruxandra Stoean, Miodrag Zivkovic, Aleksandar Petrovic, Tarik A. Rashid, Timea Bezdan, Performance of Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization, Mathematics, (Q1), 9(21), 2705;, WOS:000723179600001, 2021.
  • Ruxandra Stoean, Catalin Stoean, Miguel Atencia, Roberto Rodríguez-Labrada, Gonzalo Joya, Ranking Information Extracted from Uncertainty Quantification of the Prediction of a Deep Learning Model on Medical Time Series Data, Mathematics (Q1), 8(7): 1078,, WOS:000558245700001, 2020.
  • Ruxandra Stoean, Catalin Stoean, Roberto Becerra-García, Rodolfo García-Bermúdez, Miguel Atencia, Francisco García-Lagos, Luis Velázquez-Pérez, Gonzalo Joya, A hybrid unsupervised— Deep learning tandem for electrooculography time series analysis. PLoS ONE (Q2), 15(7): e0236401., WOS:000554606400030, 2020.
  • Catalin Stoean, Ruxandra Stoean, Miguel Atencia, Moloud Abdar, Luis Velázquez-Pérez, Abbas Khosravi, Saeid Nahavandi, U. Rajendra Acharya, Gonzalo Joya, Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals, Sensors (Q1), 20(11), 3032,, WOS:000552737900025, 2020.