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



 16 Sep 2022 Our paper titled Exploiting Domain Knowledge as Causal Independencies in Modeling Gestational Diabetes has been accepted to PSB 2023
Our paper titled “Exploiting Domain Knowledge as Causal Independencies in Modeling Gestational Diabetes” has been acc...

 16 Sep 2022 Our paper on improving compute efficiency in domain adaptation tasks using data subset selection has been accepted to NeurIPS 2022
Our paper titled “Orient: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift” ...

 16 Sep 2022 Our paper titled Active Feature Acquisition via Human Interaction in Relational domains has been accepted to CODS-COMAD 2023
Our paper titled “Active Feature Acquisition via Human Interaction in Relational domains” has been accepted to the 6t...

 02 Sep 2022 Our paper titled Association of Genetic Predisposition and Physical Activity With Risk of Gestational Diabetes in Nulliparous Women has been accepted at JAMA Network Open 2022
Our paper on “Association of Genetic Predisposition and Physical Activity With Risk of Gestational Diabetes in Nullip...

 26 May 2022 Our paper on extending RePReL for hybrid data is accepted at FUSION'22
We recently extended the RePReL framework for domains with a combination of structured and unstructured data. This wo...

For previous news, see the News Archive

Funding History


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2012
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Acknowledgements