Topics related to Knowledge Representation, Constraint Satisfaction, Optimization, and Self-Adaptive Algorithms 
 

My research is aimed at improving automated intelligent decision making in real world applications. To that end, I have developed novel and efficient solvers for general problem representation languages. So far much of my work has focused on solvers for problems expressed as Quantified Boolean Formulas (QBF), in which many types of problems like planning and verification are easily expressed. I have a passion for integrating techniques from other research areas such as Machine Learning. This has enabled me not only to develop truly novel approaches producing state of the art results, but also to engage in exciting cross-disciplinary collaborations; I am eager to continue along these lines. Lastly, my interests also extend to other kinds of reasoning and inference problems such as optimization and probabilistic reasoning.

Software 

• Contributed to the Microsoft Solver Foundation

• QBF Solvers and Preprocessing Tools

• MiniQBF - a basic search-based QBF solver

CV 

CV (pdf)