Arijit Sehanobish
About Arijit Sehanobish
Arijit Sehanobish is a Senior AI Scientist specializing in Natural Language Processing at Covera Health in New York. He holds a Ph.D. in Mathematics from the University of Maryland and has contributed to significant research in AI and machine learning.
Work at Covera Health
Arijit Sehanobish has been employed at Covera Health as a Senior AI Scientist in Natural Language Processing (NLP) since 2022. In this role, he focuses on advancing AI technologies to enhance healthcare solutions. His work contributes to the development of innovative approaches in medical data analysis and patient care. Covera Health is based in New York, New York, where Sehanobish continues to apply his expertise in AI and NLP to improve healthcare outcomes.
Education and Expertise
Sehanobish holds a Doctor of Philosophy (Ph.D.) in Mathematics from the University of Maryland College Park, where he studied from 2009 to 2019. He also earned a Master's degree in Mathematics from the Indian Statistical Institute from 2007 to 2009 and a Bachelor of Science (B.Sc.) in Mathematics from the University of Calcutta from 2004 to 2007. His academic background provides a strong foundation for his research and applications in AI, particularly in the fields of NLP and machine learning.
Background
Before joining Covera Health, Sehanobish held several academic and research positions. He was a Postdoctoral Associate at Yale University School of Medicine from 2019 to 2021 and a Visiting Research Scientist at Paris-Sud University for eight months in 2012-2013. He also worked as a Graduate Student at the University of Maryland from 2009 to 2019. His diverse experiences in academia have equipped him with a broad skill set in AI and its applications.
Research Contributions
Sehanobish has made significant contributions to the field of AI through various research projects. He developed GLYPH + BERT, an enhanced version of BERT that improved Named Entity Recognition (NER) performance on Chinese datasets, which was accepted at AAAI-20. He has also collaborated with researchers from Columbia University and Google Brain on scalable attention mechanisms, with findings presented at ICLR-2022 and ICML-2022. His work on self-supervised learning and graph neural networks for disease prediction has been spotlighted at ICML GRL+ 2020 workshop and accepted at ACM CHIL-20 and AAAI '21.
Interests and Social Impact
Sehanobish is interested in applying mathematical concepts to address social justice issues, including gerrymandering. His focus on the intersection of mathematics and social equity highlights his commitment to using his expertise for broader societal benefits. This interest complements his technical work in AI, as he seeks to leverage data-driven approaches to tackle complex social challenges.