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 2017, I was a faculty member at Indiana University and previously I was an Assistant Professor at 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.


Updates

I will be the track chair of AAAI AI for Social Good track 2022.

Nandini's work on boosting relational logistic regression models now appears in Data Mining and Knowledge Discovery journal.

Devendra and Mayukh's paper on Knowledge-based GANs has been accepted at KR 2021.

I had a nice discussion with Kristian Kersting on the exciting directions inside AI. Check out the youtube video.

Two papers accepted at AI in Medicine 2021. The first is on predicting drug-drug interactions from heterogenous data and the second is on extracting qualitative constraints when modeling gestational diabetes. Congratulations to all the students.

Harsha's fantastic work on combining planning and RL appears in ICAPS.

Srijita's work on cost-aware learning has been chosen as the honorable mention for best paper in CoDSCOMAD 2021. Congrats Srijita.

Three awesome women from our lab have graduated -- Congrats Nandini, Srijita and Navdeep.

Our collaborative work on Relational Boosted Bandits is accepted at AAAI 2021

Two papers accepted at CoDS-COMAD 2021. One on cost-based active learning and the other on human-guided learning of column networks

We have recieved two new grants -- ARO grant on learning deep tractable probabilistic models and a NIH R01 on adverse pregnancy outcomes.

Check our our work on Interactive Transfer Learning across Relational Domains that appears on Springer AI journal.

Yuqiao's work on Lifted Hybrid Variational Inference has been accepted at IJCAI 2020.

Devendra Dhami has graduate with his PhD. He will join Prof. Kristian Kersting as a post-doc and is looking for faculty positions in India. Best wishes Devendra.

Mayukh Das has graduated with his PhD and has joined Samsung labs India. Dr. Gautam Kunapuli has accepted an offer from Verisk Inc and has moved to NJ. Best wishes Mayukh and Gautam.

Navdeep's work on Non-Parametric Learning of Lifted Restricted Boltzmann Machines has been accepted to International Journal of Approximate Reasoning (IJAR). Congrats Navdeep.

Harsha's paper on injecting knowledge into gradient-boosting has been accepted at AAAI. Congratulations Harsha.

I am a program co-chair of SIAM Conference on Data Mining (SDM) 2020.

Thanks to IJCAI for recognizing me as a Distinguished SPC.

I am a program co-chair of CoDS-COMAD 2020. Please consider submitting a paper if you are going to be in India (CFP)

Check out our work on Machine Learning meeting Causal models when learning about Post-Partum Depression at AAAI Spring Symposium.

Paper on approximate counting using hypergraphs accepted to AAAI 2019. Congratulations Mayukh and Dev.

Mayukh's work on actively soliciting preferences for HTN planners appears in Knowledge-Based Systems general.

Thanks to AFRL for funding our proposal on Efficient Learning with Human-in-the-Loop in Structured, Noisy and Temporal Domains.

Thanks to Intel AI Academy for the Faculty Award.