Statistical Artificial Intelligence and Relational Learning Group is interested in making smart machines that humans can use reliably in their everyday lives. Artificial Intelligence (AI) has made tremendous progress since its early days, and its advancement has helped shape the progress of research in many other fields. Traditionally, AI is used with either the logical approach to address structured problems or with the statistical approach to handle uncertainty. Our research interests lie at the intersection: advancement and application of algorithms that can do both.

Research Highlights


Latest News



 06 Nov 2020 Congratulations to Dr. Kaur for successfully defending her thesis
Dr. Navdeep Kaur defended her dissertation on “Efficient Combination of Neural and Symbolic Learning for Relational D...

 05 Oct 2020 Congratulations to Dr. Ramanan for successfully defending her thesis
Dr. Nandini Ramanan defended her dissertation on “Effective and Efficient Structure Learning of Graphical Models” on ...

 29 Sep 2020 Paper on Human-Guided Learning of Column Networks: Knowledge Injection for Relational Deep Learning accepted to CoDS-COMAD 2021
Paper on Human-Guided Learning of Column Networks: Knowledge Injection for Relational Deep Learning accepted to CoDS-...

 29 Sep 2020 Paper on A Clustering based Selection Framework for Cost Aware and Test-time Feature Elicitation accepted to CoDS-COMAD 2021
Paper on A Clustering based Selection Framework for Cost Aware and Test-time Feature Elicitation accepted to CoDS-COM...

 15 Sep 2020 Professor Natarajan's talk from RBCDSAI Summit is now available on youtube
Watch Professor Natarajan’s talk from Indian Institute of Technology Madras Robert Bosch Centre for Data Science and ...

For previous news, see the News Archive

Funding History


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2012
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Acknowledgements