I received my Ph.D. from University of Massachusetts Amherst in 2010. My advisor was: Shlomo Zilberstein.
My CV is available here.
If you would like to schedule a meeting with me, please see my calendar.
I am interested in robust data-driven decision making. I study methods in reinforcement learning, approximate dynamic programming, robust mathematical optimization, and machine learning. I have worked on applications in domains that include precision agriculture, renewable energy management, supply chains, and others.
Interpretable Policies for Dynamic Product Recommendations Marek Petrik, Ronny Luss, Uncertainty in Artificial Intelligence (UAI), 2016.
Tight Approximations of Dynamic Risk Measures, Dan Iancu, Marek Petrik, Dharmashankar Subramanian, Mathematics of Operations Research, 40(3), 2015.
Finite-Sample Analysis of Proximal Gradient TD Algorithms, Bo Liu, Ji Liu, Mohammad Ghavamzadeh, Sridhar Mahadevan, Marek Petrik, Uncertainty in Artificial Intelligence (UAI), 2015, (Best Student Paper Award). [Appendix]
Optimal Threshold Control for Energy Arbitrage with Degradable Battery Storage, Marek Petrik, Xiaojian Wu, Uncertainty in Artificial Intelligence (UAI), 2015. [Appendix]
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning, Marek Petrik, Dharmashankar Subramanian, Conference on Neural Information Processing Systems (NIPS), (spotlight), 2014. [Full Paper].
- Email: firstname.lastname@example.org
- Phone: +1-413-230-7479
- IBM T.J. Watson Research Center
- Yorktown Heights, NY 10598