Rodolfo Abreu, PhD
About Rodolfo Abreu, PhD
Rodolfo Abreu, PhD, is a Senior Data Scientist in Customer Success at Feedzai, where he develops client-specific solutions to combat financial fraud using machine learning. He has a background in Biomedical Engineering and has held various research positions, including postdoctoral roles and a research traineeship in Portugal.
Work at Feedzai
Rodolfo Abreu serves as a Senior Data Scientist in the Customer Success department at Feedzai, a role he has held since 2021. In this position, he leads the development of client-custom solutions aimed at preventing financial fraud through the application of machine learning techniques to big data. His responsibilities include communicating proposed solutions to clients in both technical and non-technical terms, ensuring that clients understand the value and functionality of the solutions provided.
Education and Expertise
Rodolfo Abreu holds a Doctor of Philosophy (PhD) in Biomedical/Medical Engineering from Instituto Superior Técnico, where he studied from 2013 to 2017. Prior to that, he earned a Master's degree in the same field from Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa (FCT NOVA) from 2007 to 2012. His educational background provides a strong foundation in biomedical engineering principles, which he applies in his current role and previous research positions.
Background
Rodolfo Abreu began his career as a Research Trainee at PLUX - Wireless Biosignals S.A. in 2012, where he worked for 10 months in Lisboa e Região, Portugal. He then pursued his PhD at Instituto Superior Técnico from 2013 to 2017. Following his doctoral studies, he held a postdoctoral research position at Instituto Superior Técnico from 2017 to 2019, and subsequently worked as a Postdoctoral Research Associate at ICNAS - Institute of Nuclear Sciences Applied to Health from 2019 to 2021.
Research Contributions
During his postdoctoral research positions, Rodolfo Abreu developed new approaches for integrating information from various imaging modalities using multiple machine learning techniques. His work focused on enhancing the understanding and application of biomedical data, contributing to advancements in the field of medical imaging and data analysis.