Research Interests
My research interests lie in the intersection of machine learning, computational biology and bioinformatics. In particular, I am interested in the application and development of machine learning algorithms for analyzing high-dimensional, noisy biological data. On the computational side, I develop models using the framework of probabilistic graphical models to represent biological systems. These models represent the complex interaction patterns in these systems naturally as probabilistic networks. Learning these models from data requires us to develop novel and scalable learning and inference algorithms. On the biological side, I am interested in understanding, at a systems-level, how living systems function, evolve, and maintain their existence under changing external and internal conditions. Some topics which are of great interest to me include:

Research Interests

My research interests lie in the intersection of machine learning, computational biology and bioinformatics. In particular, I am interested in the application and development of machine learning algorithms for analyzing high-dimensional, noisy biological data. On the computational side, I develop models using the framework of probabilistic graphical models to represent biological systems. These models represent the complex interaction patterns in these systems naturally as probabilistic networks. Learning these models from data requires us to develop novel and scalable learning and inference algorithms. On the biological side, I am interested in understanding, at a systems-level, how living systems function, evolve, and maintain their existence under changing external and internal conditions. Some topics which are of great interest to me include:

Supervised and unsupervised network inference of functional and physcial networks from high-throughput data.
Learning networks of condition-specific responses, where a condition represents a global variable for a biological system (cell, tissue, organism) and affects the behavior of the system as a whole.
Inference of regulatory networks involved in stress responses.
Identification of evolutionary principles involved in shaping and constraining regulatory and protein interaction networks. This entails learning networks across multiple species.
Learning causal networks describing tissue and disease-specificity.
Learning high-resolution networks, that is networks where edges represent physical interactions, rather than functional interactions among nodes. Such networks include entities of many different types, some of which we cannot directly (protein and metabolite expression) requiring hidden variable inference algorithms.