Dra. Marley Vellasco

Neuro-Evolutionary Models based on Quantum-Inspired Evolutionary Algorithms

Abstract: The talk presents an overview of quantum-inspired evolutionary algorithms and their application to the evolution of different neural network models, such as Multi-Layer Percetrons, Recurrent Neural Networks and Spiking Neural Networks. Applications in control, time series forecasting, pattern classification and clustering are presented. Additionally, neural architecture search in the context of deep neural networks (Convolutional Neural Networks) is also discussed.

Bio: Marley Maria Bernardes Rebuzzi Vellasco received the BSc and MSc degrees in Electrical Engineering from the Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil, and the PhD degree in Computer Science from the University College London (UCL). Dr. Vellasco is the founder and the Head of the Computational Intelligence and Robotics Laboratory (LIRA) at PUC-Rio. Her research interests are related to Computational Intelligence methods and applications, including Neural Networks, Fuzzy Logic and Evolutionary Computation applied to decision support systems, pattern classification, time-series forecasting, control, optimization and Data Mining. Her main research área is related to Hybrid Intelligent Systems, developing Neuro-Evolutionary and Fuzzy-Evolutionary models based on Quantum-Inspired Evolutionary Algorithms and Genetic Programming.

Marley has been an Action Editor of the Neural Networks Journal since January 2012. She is also Associate Editor of IEEE Systems Journal and IEEE Transactions on Fuzzy Systems. She has been a member of the Board of Governors of International Neural Network Society (INNS) since 2011 and of the IEEE Neural Network Technical Committee since 2010. She is currently member of IEEE Computational Intelligence Society (CIS) Administrative Committee and Chair of the IEEE CIS Finance Sub-Committee of the Conference Committee.
She is the author of four books and more than 400 scientific papers in the area of soft computing and machine learning. She has supervised more than 40 PhD Thesis and 85 MSc Dissertations, and has coordinated more than 50 research projects with industries, some of them resulting in Technology Inovation prizes.