Machine Learning and Complex Networks are two fertile research areas which have borrowed heavily from each other. The proposed workshop will have research talks on two broad aspects involving these areas.

Techniques in machine learning which are used to learn the dynamics of complex networks (link prediction, community formation etc.) as well as learn the dynamics of various functionalities performed on the networks (opinion dynamics, incentive propagation, recommendation, etc.).

Machine learning systems involving distributed learning over networks (distributed loss minimization, distributed recommendation, etc.), learning with graphical models (conditional random fields, structured learning, etc), neural networks (deep learning, etc).

Talk on other areas on the theme may also be delivered.

Eligibility Criteria:

Pursuing PhD
Pursuing MS / MTech with CGPA > 9
Final year BTech / MSc Candidates with CGPA > 9.0

Discipline: CS / ECE / EE / IT / Maths / Physics / Statistics or related.

Apply with: CV (Mentioning grades of all major exams)
Summary of present research activity (5 - 10 sentences)