About
Filipe R. Ramos is an Associate Professor at the Faculty of Social Sciences and Technology of the European University (FCST-UE), while also maintaining collaborative positions at ISCTE-Business School and the IPCB School of Technology (EST-IPCB). He serves as an integrated researcher at the Center for Statistics and Applications of the University of Lisbon (CEAUL), demonstrating his strong commitment to both academic teaching and research.
His academic journey includes:
- PhD in Management, specializing in Quantitative Methods from ISCTE-Business School (2021)
- Postgraduate studies in online teaching and digital teaching skills from European University of Madrid SLU (2021)
- Master's degree in Teaching Mathematics from Nova University of Lisbon (2016)
- Diploma of Advanced Studies in Management from ISCTE-Business School (2014-2015)
- Trainer Certification from Scientific-Pedagogical Council for Continuing Education (2012)
- Master's degree in Financial Mathematics from University of Lisbon and ISCTE-Business School (2009-2011)
- Bachelor's degree in Mathematics (Teaching) from University of Lisbon (1999-2004)
Ramos specializes in data analysis and mathematical modeling with three core research areas: Time Series Analysis and Forecasting (particularly applied to Economics, Management, and Finance); Data Analysis/Data Science; and Machine Learning/Deep Learning. His work bridges theoretical mathematical approaches with practical business applications, developing predictive models that address real-world challenges in e-commerce, finance, logistics, and consumer behavior. He has made significant contributions to understanding how digital transformation affects consumer engagement and business operations.
His recent publications demonstrate a strong trend toward applying advanced machine learning and deep learning techniques to solve complex business problems. The research spans multiple domains including e-commerce trend analysis, leadership studies, logistics optimization, and financial forecasting. A notable pattern is the integration of traditional statistical methods with modern AI approaches to create more robust predictive models. His work frequently addresses practical business challenges with a strong methodological foundation, often published in Q1 and Q2 journals.
Ramos has been actively involved in academic advising across multiple institutions:
- Doctoral Theses: 3 students currently in progress
- Master's Dissertations: 27 total (8 in progress, 19 completed)
- Master's Final Projects: 1 completed
His teaching portfolio for the 2024/2025 academic year includes advanced courses such as Time Series Analysis and Forecasting, Business Analytics Research Seminar, and multiple supervised learning modules, reflecting his expertise in data science and machine learning applications.
