Research interests: My research interests lie in the fields of optimization, computational mathematics, and risk-aware decision-making. I focus on the analysis and development of models and associated efficient algorithms for the solution of nonlinear (possibly non-smooth and/or stochastic and/or black-box) optimization problems, with applications in operational research, data science, and engineering. I am also interested in statistical and machine learning, as well as causal inference with experimental and observational data. My broader aim is to bridge these areas, using optimization as a common language, for delivering efficient decision-making that is both data-driven and robust to uncertainty.

• Selected papers:

  1. Strong duality in risk-constrained nonconvex functional programming, arXiv preprint (2026)

     • D. Kalogerias, S. Pougkakiotis

  2. Model-free learning of  two-stage beamformers for passive IRS-aided network design, IEEE Transactions on Signal Processing, (2024)

     • H. Hashmi, S. Pougkakiotis, D. Kalogerias

  3. An interior point-proximal method of multipliers for convex quadratic programming, Computational Optimization and Applications, 78 (2021)

     • S. Pougkakiotis, J. Gondzio