I am a tenure-track assistant professor at the Statistical Analysis of Networks and Systems (SANS) research group, Universitat Politecnica de Catalunya (UPC). I hold a B.Sc in Computer Science (from 2017) and a M.Sc in Research in Data Science (from 2019) from the UPC. I hold a Ph.D. in Computer Science (from 2023). I have carried out a Ph.D. on the application of data-driven techniques to Internet of Things (IoT) sensor data to ensure data quality levels. Thesis entitled: "On the data quality improvement of air pollution monitoring low-cost sensor networks using data-driven techniques". This thesis places special emphasis on the use of machine learning techniques as well as the creation of a graph signal processing-based framework for the monitoring of sensor network measurements. My supervisors were professors Jorge García Vidal and Jose M. Barceló Ordinas.
🏆 I have recently received the Prize Enrique Fuentes Quintana 2023 for the best doctoral thesis in the areas of Engineering, Mathematics, Physics, Chemistry and Architecture.
🏆 I have recently received the Special Doctoral Prize of the UPC for my doctoral thesis.
I have participated in several research projects including an European project, three national projects, three regional projects, a CDTI company-related project, and two industrial projects. All of them are devoted to enhancing the data quality of IoT platforms for thei widespread adoption, where my main role has been the investigation of machine learning-based techniques to improve the quality of the data provided by low-cost sensors. I have been a recipient of the FI AGAUR Ph.D. fellowship.
I carried out a research stay at the New York University Tandon School of Engineering under the supervision of Professor Masoud Ghandehari during the months Oct-Nov 2022. This stage was focused on urban data analytics and the analysis of air pollution data.
Google Scholar Profile: Pau Ferrer Cid - Google Acadèmic
Linkedin Profile: Pau Ferrer Cid | LinkedIn
Calibration of Low-Cost Air Pollution Sensors
Graph-Based Analysis of Air Pollution Sensor Networks
Graph-Based Anomaly Detection