Roberto Pereira

Ico_CTTC
Roberto Matheus Pinheiro Pereira | CTTC

Sustainable Artificial Intelligence (SAI)

Phd , Researcher

Phone: +34 93 645 29 00

Roberto Pereira received his M.Sc. degree in Informatics from the Technical University of Munich (Germany) in 2019 and Bachelors degree in Computer Science in 2014 from the Federal University of Maranhão (Brazil). Due to his research in the field accessibility on handwritten musical content using deep learning, he was awarded with the “Young Researcher Reward” by FAPEMA in 2014. Since then he has conducted numerous researches and deployed models in the fields of deep learning, computer vision, electrical signals, NLP, dimensionality reduction and wireless communication.

Currently, Roberto Pereira is a Marie Sk?odowska-Curie fellow ESR working on a European Training Network (ETN) project called Windmill. The network consists of a consortium of leading international research institutes and companies with experts in wireless communications and machine learning. The project itself aims at developing new network management and optimization tools based on machine learning.

As an Early Stage Researcher, Roberto’s role in the Windmill project is to analyze and synthesize machine learning solutions in large dimensional settings. Thus, he is focused on dimensionality reduction methods, unsupervised learning algorithms, smart agents and large optimization scenarios.

There are no related projects.

Asymptotics of Distances Between Sample Covariance Matrices
IEEE TRANSACTIONS ON SIGNAL PROCESSING. pp. 1-14 January 2024.
Pereira R., Mestre X., Gregoratti D.
10.1109/TSP.2024.3368771 Google Scholar
Consistent Estimators of a New Class of Covariance Matrix Distances in the Large Dimensional Regime
International Conference on Acoustics Speech and Signal Processing ICASSP. January 2023.
Pereira R., Mestre X., Gregoratti D.
10.1109/ICASSP49357.2023.10096831 Google Scholar
CLUSTERING COMPLEX SUBSPACES IN LARGE DIMENSIONS
International Conference on Acoustics Speech and Signal Processing ICASSP. Vol. 2022-May. pp. 5712-5716 January 2022.
Pereira, R, Mestre, X, Gregoratti, D
10.1109/ICASSP43922.2022.9747627 Google Scholar
Beam Aware Stochastic Multihop Routing for Flying Ad-hoc Networks
2022 Ieee International Conference On Communications Workshops, Icc Workshops 2022. pp. 1065-1070 January 2022.
Deshpande A.A., Zanella A., Pereira R., Pastore A., Mestre X., Chiariotti F.
10.1109/ICCWorkshops53468.2022.9814607 Google Scholar
FLOOR MAP RECONSTRUCTION THROUGH RADIO SENSING AND LEARNING BY A LARGE INTELLIGENT SURFACE
IEEE International Workshop on Machine Learning for Signal Processing. January 2022.
Vaca-Rubio, CJ, Pereira, R, Mestre, X, Gregoratti, D, Tan, ZH, de Carvalho, E, Popovski, P
10.1109/MLSP55214.2022.9943430 Google Scholar
User Clustering for Rate Splitting using Machine Learning
European Signal Processing Conference. pp. 722-726 January 2022.
Pereira, R, Deshpande, AA, Vaca-Rubio, CJ, Mestre, X, Zanella, A, Gregoratti, D, de Carvalho, E, Popovski, P
Google Scholar
Beam Aware Stochastic Multihop Routing for Flying Ad-hoc Networks
Ieee International Conference On Communications Workshops. pp. 1065-1070 January 2022.
Deshpande, AA, Zanella, A, Pereira, R, Pastore, A, Mestre, X, Chiariotti, F
Google Scholar
Asymptotic spectral behavior of kernel matrices in complex valued observations
2021 Ieee Data Science And Learning Workshop (dslw). January 2021.
Mestre, X, Pereira, R, Gregoratti, D
10.1109/DSLW51110.2021.9523410 Google Scholar
Subspace Based Hierarchical Channel Clustering in Massive MIMO
2021 Ieee Globecom Workshops, Gc Wkshps 2021 - Proceedings. January 2021.
Pereira R., Mestre X., Gregoratti D.
10.1109/GCWkshps52748.2021.9682075 Google Scholar
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