Dr. Ahipasaoglu got her doctorate at Cornell University under the supervision of Prof. Michael Todd. Later on, she worked as a postgradute researcher at the Operations Research and Financial Engineering Department of Princeton University and the Management Science Group at the LSE.
She is the Co-Lead for the “Modelling the Systems World” for freshmores, and teaches Optimisation for ESD juniors, and Advanced Topics in Optimisation for ESD seniors. She also teaches graduate level courses in Optimisation time to time.
Her research interests are very broad including nonlinear optimisation, statistical learning, multi objective optimisation and game theory. The problems that she is interested are motivated by real-world applications in engineering, transportation science, finance, computer science and statistics.
- PhD in Operations Research, Cornell University (2004-2009)
- MS in Industrial Engineering, Bilkent University, Turkey (2002-2004)
- BS in Industrial Engineering, Bilkent University, Turkey (1998-2002)
Selected Recent Publications
- Ahipasaoglu, S. D., Arikan, U., and Natarajan, K. On the Flexibility of using Marginal Distribution Choice Models in Traffic Equilibrium, Transportation Research Part B: Methodological, (91), 130-158, 2016.
- Karakaya, G., Galelli, S., Ahipasaoglu, S.D., and Taormina, R. Identifying (quasi) equally informative subsets in feature selection problems for classification: a max-relevance min-redundancy approach. Cybernetics, IEEE Transactions on, 2015.
- Ahipasaoglu, S. D., Meskarian, R., Magnanti, T., and Natarajan, K. Beyond Normality: A Distributionally Robust Stochastic User Equilibrium Model, Transportation Research Part B: Methodological, (81) 331-654, 2015.
- Ahipasaoglu, S. D., Fast Algorithms for the Minimum Volume Estimator, Journal of Global Optimisation, (62) 351-370, 2015.
- Ahipasaoglu, S. D., A First-Order Algorithm for the A-Optimal Experimental Design Problem, Statistics and Computing, (25) 1113-1127, 2015.
- Nonlinear optimization
- Robust Optimisation
- Statistical Learning
- Optimal Experimental Design