01 — Research

Reading the signal in the data.

I build AI that reads what people are really signaling — emotion through movement, patterns through noise — and I love teaching as much as researching it. PhD candidate and programming lecturer at the University of Algarve.

About

Background

Pedro J. Vaz is an Invited Assistant at the Instituto Superior de Engenharia, Universidade do Algarve, Faro, where he lectures on programming and object-oriented programming and pursues a Ph.D. in Informatics Engineering as an FCT Ph.D. Grant Holder. He holds a B.Sc. in Electrical and Electronics Engineering (2009) and an M.Sc. in Electrical and Computer Engineering (2022) from the same institution, and since January 2024 he has been a member of NOVA LINCS — Multimodal Systems group. His trajectory spans software engineering at Maine Avenue Technologies (Madrid, 2009–2010), electrical and senior electronics design engineering at Stanley Black & Decker GmbH (Idstein, Germany, 2011–2015), and high school teaching (2018–2021) before moving into full-time research — first under the Green Spaces SMART Irrigation Control (GSSIC) project (2021–2023) and then under the Sustainable Horizons European Universities (SHEs) programme (2023–2024). His research interests include machine learning, affective computing, data science, electronics, and audio signal processing.
Publications

Selected work

2025

Affective Computing Emotional Body Gesture Recognition: Evolution and the Cream of the Crop

IEEE Access. With J.M.F. Rodrigues & P.J.S. Cardoso. DOI: 10.1109/ACCESS.2025.3630563

2025

Affective Computing Databases: In-Depth Analysis of Systematic Reviews and Surveys

IEEE Transactions on Affective Computing, vol. 16, no. 2, pp. 537–554. With J.M.F. Rodrigues & P.J.S. Cardoso. DOI: 10.1109/TAFFC.2024.3507289

2025

Impact of Employing Weather Forecast Data as Input to the Estimation of Evapotranspiration by Deep Neural Network Models

In: New Developments in Environmental and Energy Technologies (Springer), Proc. ESRE 2023, pp. 51–66. With G. Schütz, C. Guerrero & P.J.S. Cardoso. DOI: 10.1007/978-981-96-4345-5_5

2023

Hybrid Neural Network Based Models for Evapotranspiration Prediction over Limited Weather Parameters

IEEE Access, vol. 11, pp. 963–976. With G. Schütz, C. Guerrero & P.J.S. Cardoso. DOI: 10.1109/ACCESS.2022.3233301

2022

A Study on the Prediction of Evapotranspiration Using Freely Available Meteorological Data

In: Lecture Notes in Computer Science (Springer), Proc. ICCS 2022, pp. 436–450. With G. Schütz, C. Guerrero & P.J.S. Cardoso. DOI: 10.1007/978-3-031-08760-8_37

Full CV (CiênciaVitae) →