Publications

Compact uncertainty sets for robust optimization based on bootstrapped Dirichlet process mixture model

Neofytou Andreas, Liu Bin, Akartunali Kerem

Optimization, pp. 1-44 (2025)

https://doi.org/10.1080/02331934.2025.2451812

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Teaching

I have been extensively involved with teaching activities in various levels from undergraduate to postgraduate in the department of Computer and Information Sciences, mainly in Machine Learning and Deep Learning classes, along with Optimization classes in the department of Management of Science.

Research Interests

My broad research area encompasses machine learning and optimization, with a particular focus on decision-making under uncertainty. I attempt to merge machine learning and optimization, by bridging the gap of a good prediction to a good decision. My research interest is to improve the decision-making process under uncertainty with machine learning. Particularly by proposing variants and extensions of robust ML algorithms, starting from data leading to high quality (robust) decisions and predictions. From predictive to prescriptive solutions and approaches, leading to stronger prescriptive performance with advanced machine learning algorithms.