Pavan Kapanipathi
Research Manager at Indian AI Research Organization
New York City Metropolitan Area United States
Summary
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Senior🎓
Top SchoolPavan Kapanipathi is a research manager and principal research scientist at IBM T.J. Watson Research Center with 14 years of experience bridging knowledge graphs, neuro-symbolic reasoning, and NLP to tackle Knowledge Base Question Answering, inference, and information extraction. He leads a team focused on KBQA and chairs IBM’s Reasoning PIC, publishing consistently at major AI/NLP conferences while translating research into applied systems. His background spans industry research internships and academic work on semantic enrichment, large-scale social stream analysis, and Hadoop-based processing, giving him deep practical experience with RDF/SPARQL, Java, and probabilistic models. Pavan holds an MS and PhD in Computer Science from Wright State University and contributes advisory expertise to the Indian AI Research Organization, reflecting a blend of academic rigor and industry impact. An oft-overlooked strength is his track record of turning semantic web and social-data research prototypes into production-aware components and patents.
14 years of coding experience
9 years of employment as a software developer
BE Computer Science, BE Computer Science at Visvesvaraya Technological UniversityDoctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at Wright State University
Github Skills (16)
playing8
classify8
graph6
nlp6
semantic6
large-scale6
datasets6
language-processing6
natural-language-processing5
question-answering5
scale5
machine-learning5
artificial-intelligence4
inference4
fundamental3
Programming languages (1)
Github contributions (5)
pavan046/Semantic-Similarity
May 2012 - Nov 2012
Contributions:49 commits in 5 months
IBM/knowledge-enabled-textual-entailment
Nov 2018 - Jun 2020
Natural Language Inference is fundamental to many Natural Language Processing applications such as semantic search and question answering. The task of NLI has gained significant attention in the recent times due to the release of fairly large scale, challenging datasets. Present approaches that address NLI are largely focused on learning based on the given text in order to classify whether the given premise entails, contradicts, or is neutral to the given hypothesis. On the other hand, techniques for Inference, as a central topic in artificial intelligence, has had knowledge bases playing an important role, in particular for formal reasoning tasks. While, there are many open knowledge bases that comprise of various types of information, their use for natural language inference has not been well explored. In this work, we present a simple technique that can harnesses knowledge bases, provided in the form of a graph, for natural language inference.
Contributions:2 commits, 1 PR, 2 pushes in 1 year 7 months
particularlanguage-processingtimeschallengingnatural-language-processing
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