Alberto Costa is an Associate Professor of Urban Informatics (Research) at Singapore Management University (SMU). His research focuses on the development of optimization methods for resilient infrastructure, energy systems, and decision-making under uncertainty. He combines mathematical programming, black-box optimization, robust optimization, and AI-driven decision support to address real-world challenges in complex systems.
Prior to joining SMU, he held postdoctoral research positions at the Singapore University of Technology and Design (SUTD) and the National University of Singapore (NUS). He subsequently joined the Singapore-ETH Centre, where he served in several research and leadership roles, including Cluster Coordinator, Senior Researcher II, and Research Technical Lead.
His research has been recognized through the 2021 Beale–Orchard-Hays Prize for Excellence in Computational Mathematical Programming, awarded jointly with Prof. G. Nannicini. In 2025, he achieved a top-10 placement (among more than 6,000 submissions) in Google's Generative AI Capstone Project competition on Kaggle, with a project exploring the use of agentic AI to make research on advanced optimization methods more accessible to practitioners and decision-makers.
Beyond his academic research, he is the author of Objective: Zero Objectives, a book that explores human behavior and decision-making through the lens of optimization.
Qualifications
- Ph.D. in Operations Research, École Polytechnique, France, 2012
- M.Eng. in Computer Engineering, University of Padova, Italy, 2009
- B.Eng. in Computer Engineering, University of Padova, Italy, 2007
Research Interests
- Mathematical programming (mixed-integer linear and nonlinear optimization)
- Black-box optimization
- Optimization under uncertainty
- Clustering in complex networks
- Agentic AI for optimization and decision-making
Selected Publications
- Modelling fortification strategies for network resilience optimization: The case of immunization and mitigation. Costa, A., Ng, T.S., Kang, J., Wu, Z., & Su, B. IISE Transactions, 56(4), 411–423, 2024.
- Long-term solar PV planning: An economic-driven robust optimization approach. Costa, A., Ng, T.S., & Su, B. Applied Energy, 335, 120702, 2023.
- A smart sensor-data-driven optimization framework for improving the safety of excavation operations. Costa, A., Wang, Z.Z., Goh, S.H., & Smith, I.F.C. Expert Systems with Applications, 193, 116413, 2022.
- RBFOpt: An open-source library for black-box optimization with costly function evaluations. Costa, A., & Nannicini, G. Mathematical Programming Computation, 10(4), 597–629, 2018.
- MILP formulations for the modularity density maximization problem. Costa, A. European Journal of Operational Research 245 (1), 14-21, 2015.