Our group is involved in an interdisciplinary research focusing on the areas of
(i) machine learning and pattern recognition, and (ii) computational biology and structural bioinformatics. The research in these areas is inspired by two basic questions.

Q1: Can we teach a computer to recognize and categorize real-world objects the way a human does?

To tackle this question, we are developing new formal methods for structure- or symbolic-based machine learning and pattern recognition. While applying these methods to the classical applications of pattern recognition, we also hope that the methods will help us answering a biological question:

Q2: What can we learn about structure, function, and evolution of protein complexes, macromolecular assemblies and larger biological systems?

In collaboration with experimental scientists, we try to answer this question for specific biological systems. In particular, we study the complexes that are formed between proteins of pathogenic microbes and their hosts (human, animals, or plants). Our goal is to help uncovering the basic mechanisms in host-pathogen interactions, thus facilitating the discovery of treatment against infectious diseases. We also study the molecular assemblies of a chemical synapse, a complex biological system that is linked to the basic cognitive processes, such as learning and memory. We hope that one day, the discovered molecular principles behind the cognitive processes could be incorporated into a machine learning framework.