Bahar Taskesen

Bahar Taskesen serves as Assistant Professor of Operations Management and Liew Family Junior Faculty Fellow at the University of Chicago Booth School of Business, focusing on data-driven decision systems with applications in responsible AI development and operations management.

Her academic background includes:

• PhD in Risk Analytics and Optimization from École polytechnique fédérale de Lausanne (EPFL), Switzerland (2024), supervised by Daniel Kuhn
• Bachelor of Science in Electrical and Electronics Engineering from Middle East Technical University (2018)

Her research integrates optimal transport theory, statistical inference, and stochastic optimization to address algorithmic fairness and robustness challenges. She develops frameworks for reliable decision-making under uncertainty, with significant implications for AI ethics and operational efficiency in complex systems. Her work bridges theoretical mathematics with practical implementations in machine learning and control systems.

Recent publications demonstrate consistent focus on optimization under ambiguity, particularly through distributionally robust methods and fairness-aware algorithms. The 2024 WINE-accepted paper on multi-armed bandits exemplifies her approach of combining theoretical rigor with real-world applicability in sequential decision problems.

Her research impact is recognized through:

• Kilian Schindler Excellence Award (October 2024) for outstanding doctoral thesis

As a new faculty member, she is establishing her research group while teaching Workshop in Operations/Management Science (2025-2026). Her GitHub repositories indicate active development of open-source tools for fairness testing and optimization.

Previously affiliated with EPFL's Risk Analytics and Optimization Lab, she now leads research at Chicago Booth focused on creating mathematically sound frameworks for ethical AI deployment in operational contexts.