Ashish Kulkarni

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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)
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)
Translation systems are known to benefit from the availability of a bilingual lexicon for a domain of interest. A system, aiming to build such a lexicon from source language corpus, often requires human assistance and is confronted by conflicting requirements of minimizing human translation effort while improving the translation quality. We present an(More)
We study the problem of ontology population for a domain ontology and present solutions based on semi-automatic techniques. A domain ontology for an organization, often consists of classes whose instances are either specific to, or independent of the organization. E.g. in an academic domain ontology, classes like Professor, Department could be organization(More)