Arijit Sehanobish is an Applied Scientist with a Ph.D. in Mathematics and eight years of experience translating advanced statistical theory into deployable ML and NLP solutions. He specializes in parameter-efficient fine-tuning and in-context learning for large language models, and has applied these skills to NER, classification, summarization, and multimodal medical vision-language problems across industry and academia. His research pedigree includes graph neural networks, normalizing flows, and few-shot learning with publications and awards at venues like ICML, AAAI, and NAACL, reflecting a strong bridge between theory and practice. At Kensho he builds expert LLMs for customer use cases using DeepSpeed and PEFT methods, and previously led medically focused self-supervised representation work at Covera Health. Comfortable with both deep math and production engineering, he brings a rare combination of number-theory rigor from his Ph.D. and hands-on model deployment experience in regulated domains. Located in Washington, D.C., he’s as likely to prototype novel model architectures as to optimize them for real-world, high-stakes applications.
8 years of coding experience
4 years of employment as a software developer
Master's degree, Mathematics, Master's degree, Mathematics at Indian Statistical Institute
The University of Maryland, College Park
Bachelor of Science (B.Sc.), Mathematics, Bachelor of Science (B.Sc.), Mathematics at University of Calcutta
Contributions:24 commits, 23 pushes, 1 branch in 1 year 5 months
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Arijit Sehanobish - Applied Scientist at Kensho Technologies