Our projects are supported by various funding agencies, including
(but not limited to) NSF, NIH, and Microsoft.
- NSF: CAREER: Scalable Bayesian Learning for Multi-source and Multi-aspect Data.
- NSF: Science and Technology Center: Emerging Frontiers of Science of Information.
- NSF: Relational Learning and Inference for Network Models (This project focuses the development of new methodologies and models for analyzing social and biological networks.)
- NSF: CDI Type I: Collaborative Research: Integration of relational learning with ab-initio methods for prediction of material properties. With G. Ceder, MIT
- NIH R01: A Temporal View of the Plasmodium-red Blood Cell Interactome, With D. J. Lacount, Purdue
- Eli Lilly: Identifying novel somatic splicing
isoforms using tumor normal paired RNA-Seq data
- CBR: Integrating Imaging Phenotypes and Genotypes for Early Detection of AD. With L. Shen and A. Saykin, Indiana University Medical School
- Showalter: Genome-wide Examination of Binding Sites for Transcription Factors Responsible for Prostate Cancer Prevention, With J. Fleet, Purdue University