معرفی
Anabela Ribeiro Dias da Costa is an Assistant Professor at the Department of Quantitative Methods for Management and Economics within the Lisbon Business School at ISCTE - Lisbon University Institute. She has maintained continuous academic activity at ISCTE since October 1989, serving as both educator and researcher with expertise spanning quantitative methods for management and economics.
Her educational background includes:
- PhD in Statistics and Operations Research (2008) from Faculty of Sciences - University of Lisbon
- Master's in Statistics and Operations Research (1993) from Faculty of Sciences - University of Lisbon
- Bachelor's in Statistics and Operations Research (1989) from Faculty of Sciences - University of Lisbon
Dr. Costa's research primarily focuses on investment project evaluation, particularly for R&D initiatives and natural resource management projects. She has developed expertise in applying real options theory to portfolio evaluation with budget constraints. Her work bridges theoretical operations research with practical financial applications, creating decision support tools for complex investment scenarios. In recent years, she has expanded her research into data mining and deep learning applications for financial time series forecasting, demonstrating adaptability to emerging analytical methodologies.
Analysis of her publication history reveals a strong trajectory from traditional operations research methods toward contemporary machine learning approaches. Her earlier work centered on dynamic programming and surrogate constraint relaxations for capital budgeting problems, while her recent publications increasingly focus on LSTM networks and comparative studies of deep learning techniques for financial forecasting. This evolution demonstrates her ability to integrate established operations research principles with cutting-edge computational methods.
Dr. Costa has served in multiple academic leadership roles, most notably as Coordinator of the Bachelor's Degree in Management program from 2016 through 2025, demonstrating institutional commitment and administrative capability. Her teaching portfolio includes foundational courses in Mathematics and Statistics alongside specialized instruction in Data Mining and Project Evaluation Methods, which she has offered in English since the 2009/10 academic year.
Her research contributions have generated significant scholarly impact, with publications accumulating 9 citations in Web of Science and Scopus, and 23 citations in Google Scholar. Her work appears in reputable venues including Computational Management Science (Q2 journal) and numerous international conferences such as EURO, APDIO, and AIRO.
