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Özgür Simsek

Adjunct Researcher
Workplace Director emeritus Gigerenzer

ozgur [at] mpib-berlin [dot] mpg [dot] de

Short CV: 

Ph.D., Computer Science, 2008, University of Massachusetts Amherst 

M.S., Computer Science, 2004, University of Massachusetts Amherst 

M.S., Industrial Engineering and Operations Research, 1997, University of Massachusetts Amherst

Research Interests: 
My research is on solving complex problems through autonomous search, learning, and development, spanning a number of different fields, including machine learning, network science, and decision heuristics.

Selected Literature: 

Şimşek, Ö., Algorta, S., & Kothiyal, A. (2016). Why Most Decisions Are Easy in Tetris—And Perhaps in Other Sequential Decision Problems, As Well. In Proceedings of the Thirty-Third International Conference on Machine Learning (ICML).

Şimşek, Ö., & Buckmann, M. (2015). Learning from small samples: An analysis of simple decision heuristics. In Advances in Neural Information Processing Systems (NIPS) 28

Şimşek, Ö. (2013). Linear decision rule as aspiration for simple decision heuristics. In Advances in Neural Information Processing Systems (NIPS) 26

Şimşek, Ö., & Jensen, D. (2008). Navigating networks by using homophily and degree. Proceedings of the National Academy of Sciences (PNAS), 105(35), pp. 12758–12762.

Şimşek, Ö., & Barto, A. G. (2008). Skill characterization based on betweenness. In Advances in Neural Information Processing Systems (NIPS) 21.

Şimşek, Ö., & Barto, A. G. (2006). An intrinsic reward mechanism for efficient exploration. In Proceedings of the Twenty-Third International Conference on Machine Learning (ICML).

Şimşek, Ö., Wolfe, A. P., & Barto, A. G. (2005) Identifying useful subgoals in reinforcement learning by local graph partitioning. In Proceedings of the Twenty-Second International Conference on Machine Learning (ICML).

Neville, J., Şimşek, Ö., Jensen, D., Komoroske, J., Palmer, K., & Goldberg, H. (2005). Using relational knowledge discovery to prevent securities fraud. In Proceedings of the Eleventh International Conference on Knowledge Discovery and Data Mining (KDD).

Katsikopoulos, K. V., & Şimşek, Ö. (2005). Optimal doubling strategy against a suboptimal opponent. Journal of Applied Probability, 42, 867–872.

Şimşek, Ö., & Barto, A. G. (2004). Using relative novelty to identify useful temporal abstractions in reinforcement learning. In Proceedings of the Twenty-First International Conference on Machine Learning (ICML). ACM, New York, NY, USA.