My research focuses on the interplay between machine learning and optimization. I like studying both sides of this relationship: Integrating machine learning to optimization problems, and playing with the underlying optimization problem in machine learning algorithms. These problems mostly arise and have applications in multi-period multi-agent problems.
My current work is motivated by these two questions: (1) How does learning the uncertain data for the optimization problem impact the decision making process? (2) Can we tailor the machine learning algorithms by modifying the underlying optimization problem when our data has a special structure?
Preference Learning in Pricing: Determining the single product or bundle prices in multi-segmented markets.
Preference Learning in Multi-objective Optimization: Studying the impact of the choice of preference data on the convergence of weight learning algorithms in MOO.
SVM with Coefficient Restrictions: Modifying the underlying optimization problem in SVM to deal with linear separators with particular constraints on coefficients.
E. Karabulut, F. Gholizadeh, and R.Akhavan-Tabatabaei, “The value of adaptive menu sizes in peer-to-peer platforms", Transportation Research Part C: Emerging Technologies 145 (2022): 103948. https://doi.org/10.1016/j.trc.2022.103948
E. Karabulut, S.Ahmed, and G.Nemhauser. Decentralized Online Integer Programming. Computers & Operations Research 135 (2021): 105421. https://doi.org/10.1016/j.cor.2021.105421
E. Karabulut, S.Ahmed, and G.Nemhauser. Decentralized Algorithms for Distributed Integer Programming Problems with a Coupling Knapsack Constraint. Discrete Optimization 38 (2020): 100595. https://doi.org/10.1016/j.disopt.2020.100595
E. Karabulut, N. Aras, and K. Altınel. Optimal Sensor Deployment to Increase the Security of the Maximal Breach Path in Border Surveillance. European Journal of Operational Research. 259 (1): 19-36, 2016 http://dx.doi.org/10.1016/j.ejor.2016.09.016
E. Karabulut, K. Altınel and E. Alpaydın. Guiding k-means Using Lagrangean Relaxation and Subgradient Optimization. (technical report). 2013
2016 ARC Fellowship - Awarded by the Algorithms and Randomness Center at Georgia Institute of Technology