Ashish Kulkarni

Learn More
Intrinsic flow instability has recently been reported in the blood flow pathways of the surgically created total-cavopulmonary connection. Besides its contribution to the hydrodynamic power loss and hepatic blood mixing, this flow unsteadiness causes enormous challenges in its computational fluid dynamics (CFD) modeling. This paper investigates the(More)
Nanoscale drug delivery vehicles have been harnessed extensively as carriers for cancer chemotherapeutics. However, traditional pharmaceutical approaches for nanoformulation have been a challenge with molecules that exhibit incompatible physicochemical properties, such as platinum-based chemotherapeutics. Here we propose a paradigm based on rational design(More)
We present an approach and a system for collective disambiguation of entity mentions occurring in natural language text. Given an input text, the system spots mentions and their candidate entities. Candidate entities across all mentions are jointly modeled as binary nodes in a Markov Random Field. Their edges correspond to the joint signal between pairs of(More)
Metastasis is a major cause of mortality and remains a hurdle in the search for a cure for cancer. Not much is known about metastatic cancer cells and endothelial cross-talk, which occurs at multiple stages during metastasis. Here we report a dynamic regulation of the endothelium by cancer cells through the formation of nanoscale intercellular membrane(More)
In this paper we present work done towards populating a domain ontology using a public knowledge base like DBpedia. Using an academic ontology as our target we identify mappings between a subset of its predicates and those in DBpedia and other linked datasets. In the semantic web context, ontology mapping allows linking of independently developed ontologies(More)
Traditional fluid mechanics textbooks are generally written with problem sets comprised of closed, analytical solutions. However, it is recognized that complex flow fields are not easily represented in terms of a closed solution. A tool that allows the student to visualize complex flow phenomena in a virtual environment can significantly enhance the(More)
Recently Kulkarni et al. [20] proposed an approach for collective disambiguation of entity mentions occurring in natural language text. Their model achieves disambiguation by efficiently computing exact MAP inference in a binary labeled Markov Random Field. Here, we build on their disambiguation model and propose an approach to jointly learn the node and(More)