Bernardo Dias Raimundo is a Ph.D. student and guest lecturer at NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa. His research lies at the intersection of data science and financial decision-making, with a focus on model uncertainty, estimation risk, and portfolio allocation under incomplete information. He investigates how combining diverse sources of information, whether in forecasting models or investment strategies, can improve decision-making under uncertainty. His work draws methodological parallels between forecast combination and portfolio theory, applying tools such as shrinkage estimation, robust optimisation, and convex programming to enhance out-of-sample performance, robustness, and interpretability. His broader academic interests include decision-making under uncertainty, the integration of statistical learning with economic reasoning, and the design of interpretable models for financial risk and allocation. He holds an MSc in Statistics and Information Management with a specialisation in Risk Analysis, and a BSc in Economics. Prior to academia, he worked in internal audit and consulting for both financial and non-financial institutions.