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Research Areas

My primary fields of research are Theory of Computation (and complexity) and Logic, focusing on implicit complexity (relating logical complexity of concepts to computational, resource-based complexity), especially on emerging models of computation, such as stream computation, biological neural networks etc. My secondary research area is Artificial Intelligence, focusing on machine learning.

The field of implicit complexity relates conceptual complexity to resource-based complexity. One advantage of conceptual complexity definitions is that they are logical, and do not refer to a particular machine model or resource, and so are easily adaptable to new and emerging models of computation. My past and current research has been the study of conceptual complexity, and their relation to resource-based complexity classes over different data models. I plan to continue this study for emerging models of computation, especially where a computational model is yet to be defined in rigorous, mathematical detail, such as biologically realistic neural networks, DNA computation, probabilistic computation etc.

I am also interested in using machine learning /data mining techniques to solve real-world problems. Further, I am interested in advancements in neural networks - especially biologically inspired developments.