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

08 May 2023 Two Papers accepted to UAI 2023!
We have two papers accepted to UAI 2023. Congrats to Sahil, Saurabh and our wonderful collaborators Kristian Kersting...

25 Feb 2023 Congratulations to Dr. Kokel for successfully defending her thesis
Dr. Harsha Kokel defended her dissertation on “Beyond Data: Efficient Knowledge-Guided Learning for Sparse and Struct...

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...

For previous news, see the News Archive