Aras Selvi is a PhD candidate at Imperial College London (Department of Analytics and Operations, Business School) as a member of the group “Models and Algorithms for Decision-Making under Uncertainty” supervised by Professor Wolfram Wiesemann. His research interests include robust and distributionally robust optimisation, machine learning, computational privacy, and their intersections.

Aras is a member of the Computational Optimisation Group (Department of Computing), Data Science Institute, and Imperial Business Analytics. He serves as a reviewer in leading optimisation and machine learning venues including SIAM Journal on Optimization, Management Science, NeurIPS, and ICML. He is also an academic mentor on the Imperial’s Data Spark programme where his past projects used data analytics to improve operational efficiencies and commercial performance for a global energy company as well as derived analytical tools to prevent global nuclear proliferation for a major policy institute. He supervises student-led projects on deriving analytical tools to facilitate the decision-making (under uncertainty) processes of major global non-profit organisations. He works as a teacher or teaching assistant for graduate-level optimisation and machine learning modules at Imperial College London, London School of Economics, and London Business School.

Aras is interested in developing efficient algorithms to solve hard optimisation problems arising in privacy, machine learning, and business.