I enjoy applying knowledge informed probabilistic reasoning (mainly Bayesian Reasoning) and Machine Learning techniques to solve challenging problems in sustainability and healthcare. My research interests include Semantic Web, Information Extraction, Internet of Things (IoT), and knowledge representation and reasoning under uncertainty.
Specifically, I work on algorithms that can leverage existing knowledge of a domain (e.g., ConceptNet, domain ontology, dictionaries) to complement structure and parameters of probabilistic models. My thesis work involves extracting events, understanding events, and recommending actions using probabilistic models in domains such as Smart Cities and Healthcare.
Invited to the NSF funded Data Science Workshop, 2015 at University of Washington, Seattle, Aug 5-7, 2015
I was offered the Eric & Wendy Schmidt Data Science for Social Good Fellowship for Summer 2014
A short article on my research appeared on our university newsroom, Nov 2013.
Invited for participation in Dagstuhl Seminar on Physical-Cyber-Social Computing, 2013.
Invited for a fully funded visit to University of Debrecen, Hungary, to talk on Data Analytics for IoT at a Faculty Seminar, October 2013.
NSF Travel Award for attending the International Semantic Web Conference (ISWC) 2012.
Best research showcase award (three out of 40 interns were chosen) for my internship work at IBM Research, India, in Summer 2012.
SIAM (Society for Industrial and Applied Mathematics)
pramod [at] knoesis [dot] org,
pramod [dot] atre [at] gmail [dot] com