Nitish Gupta
Senior Research Scientist at Google DeepMind
Bengaluru, Karnataka, India
Summary
👤
Senior🎓
Top SchoolNitish Gupta is a Senior Research Scientist at Google DeepMind with 11 years of experience in NLP and machine learning, holding a PhD from the University of Pennsylvania and advanced degrees from UIUC and IIT Kanpur. He has a strong research-to-product track record across Google, DeepMind, AI2, FAIR and university labs, focusing on explainable question answering, quantitative reasoning, and semantic parsing. Nitish blends deep academic rigor with practical engineering—contributing to widely used NLP libraries like CogComp by improving string handling and TF-IDF tooling for more robust text pipelines. Based in Bengaluru, he is known for turning complex language understanding problems into scalable, testable systems that bridge research and production.
11 years of coding experience
8 years of employment as a software developer
High School, High School at Delhi Public School - IndiaIndian Institute of Technology KanpurUniversity of Illinois Urbana-ChampaignDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Pennsylvania
Github Skills (9)
lucene10
javas10
java10
text-processing9
information-retrieval9
natural-language-processing8
data-structures6
data-structure6
big-data5
Programming languages (11)
Github contributions (5)
CogComp/cogcomp-nlp
Oct 2015 - Jul 2018
CogComp's Natural Language Processing Libraries and Demos: Modules include lemmatizer, ner, pos, prep-srl, quantifier, question type, relation-extraction, similarity, temporal normalizer, tokenizer, transliteration, verb-sense, and more.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:62 commits, 3 PRs, 2 branches in 2 years 9 months
Contributions summary:Nitish primarily contributed to the core-utilities library of the CogComp NLP project, specifically focusing on improving string handling and text processing functionalities. They added a function to remove control characters from strings, along with associated tests, improving the robustness of the NLP pipeline. Additionally, the user added functions for calculating TF-IDF scores within the Lucene indexing and search framework, demonstrating an understanding of information retrieval techniques used in the project.
ellipsisverbextractionnatural-language-understandingpos-tagging
nitishgupta/char-encode-decode
Aug 2016 - Sep 2017
Contributions:27 commits, 1 push in 1 year
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