My research interests lie in the fields of Artificial Intelligence and Machine Learning and their application to healthcare problems. More specifically, I am interested in the areas of Relational Learning, Reinforcement Learning, Graphical Models, and Planning. Please read more about our projects and team on our team webpage.

Till 2013, I was a faculty member at Translational Science Institute of Wake Forest School of Medicine. I was a Post-Doc earlier at the Department of Computer Science in the University of Wisconsin Madison, working with Professors Jude Shavlik and David Page.

I completed my PhD in fall 2007 under Professor Prasad Tadepalli in the School of EECS at Oregon State University.

Note to incoming students: If you are interested in working with me, please register for a course that I teach. I do not hire students before they take my course. Also, I do not have any internship/short-term positions. Please do not contact me if you want to work with me for less than a year.


I am giving the invited talk this year at StaRAI workshop at IJCAI this year.

Our work on Active Feature Elicitation that enables identifying specific examples to acquire more information as advice has been accepted to IJCAI. This enables a new research study to identify potential recruits.

Check our latest work on Drug-Drug Discovery using kernel learning has been accepted to the IEEE Conference CHASE 2018. Congratulations Devendra and Mayukh.

Phillip's work on Unified Advice-Taking framework for SRL models accepted to the Frontiers in Robotics and AI journal special issue (pre-print coming soon).

Thanks Amazon for the Amazon Faculty Research Award

Mayukh's paper with Rakib on Advice seeking in Probabilistic Planning accepted at AAMAS (pdf coming soon). Congratulations Mayukh and Rakib!

Welcome Dr. Gautam Kunapuli who joins our lab as a Research Associate Professor.

I am presenting a tutorial on Statistical Relational AI with Luc De Raedt, Kristian Kersting and David Poole at NIPS 2017.

AAAI submission on Mixed Sum-Product Networks accepted!

Two papers at BIBM - one on predicting the number of cardiovascular procedures (Shuo) and the other on predicting jointly multiple procedures (Nandini).

Alex' paper on using ER diagrams to set background knowledge for our boosting algorithm has been accepted at Knowledge Capture.

Shuo's paper on learning for large scale recommendation systems using SRL has been accepted to Knowledge-based Systems journal. Congrats Shuo for scaling up SRL into a real recommendation system.

Two health informatics papers accepted!! Prediction of Parkison's by Devendra (AI in Medicine) and predicting Post-Partum Depression in collaboration with the prohealth group at CHASE 2017. Congrats everyone! pdfs coming soon.

Navdeep's paper on Relational Restricted Boltzmann Machines that uses Probabilistic Random Walks has been accepted to ILP.

Our paper on employing databases to accelarate relational learning has been awarded the best student paper award at ILP. Congrats Marcin, Tushar and the team.

I am guest editing Frontiers Robotics and AI journal issue on Statistical Relational Artificial Intelligence. Please consider submitting a paper and contact me if you need more information.

I am presenting the tutorial on Statistical Relational AI with David Poole and Kristian Kersting at AAAI.

Our partnership with CRANE has been finalized. See the news release for more details

Our paper on learning Poisson Sum Product Networks has been accepted at AAAI. Pdf coming soon.

Our paper on Markov Logic Networks for extracting adverse drug events from text will appear in KAIS journal. This work explores the use of MLNs for medical information extraction, more precisely, adverse drug events. We present a NLP based pipeline for extracting articles from PubMed.