Faculty Details

Ashwin Srinivasan

Professor (CSE)

PhD (1991) Electrical Eng. and Computer Science,
University of New South Wales, Sydney

Brief Bio

Ashwin received his PhD from the School of Electrical Engineering and Computer Science, University of New South Wales, Sydney, in 1991. Later that year, he joined the Turing Institute in Glasgow, which was conceived and established by Donald Michie. With Stephen Muggleton (now at Imperial) and Donald Michie, he started pioneering work on the development and application of techniques for machine learning in first-order logic, specifically, Inductive Logic Programming (ILP). His ILP program Aleph is perhaps still the most widely used ILP system in the world. From 1993, Ashwin was a member of the Oxford University Computing Laboratory, first as a post-doctoral researcher, then as the Nuffield Trust Research Fellow in Medical Mathematics, and finally as a faculty member of the Computing Laboratory. From July 2003 to 2009 he was a Research Staff Member of IBM Research -- India. In 2009, he left IBM Research to take up a Ramanujan Fellowship. In November 2010 he was appointed Professor and Dean at the South Asian University (SAU)., which was jointly established by the SAARC nations. His remit there was to help start the Faculty of Mathematics and Computer Science. From September 2012, he has been a Professor in Computer Science at the IIIT-D. Ashwin is currently also a Visiting Professor at the Oxford University Computing Laboratory, and a Visiting Professorial Fellow at the School of Computer Science and Engineering, University of New South Wales.


Phone: 011-26907441
Email: ashwiniiitd.ac.in
Website: http://faculty.iiitd.ac.in/~ashwin/
Office: B-203

Research Interests

Application: Construction of qualitative models of systems with applications to biological systems, Predicting drug binding energy using 3-dimensional molecular information.
Implementation: Development of the ILP system Aleph, Techniques for very large-scale data analysis with relational learning, Automatic feature discovery with ILP, Investigate novel search techniques for ILP.
Conceptual: The application of game theory and a Theory of Optimal Search to ILP, The use of designed experiments in ILP.

Teaching Interests

Inductive Logic Programming, Artificial Intelligence, Probabilistic Modelling