Indranil Bhattacharya on Muck Rack

Indranil Bhattacharya

As seen in: Wiley Online Library, MDPI, Firstpost, JAMA (The Journal of the American Medical Association), Scroll.in, Mary Ann Liebert, Inc., Dove Medical Press, PaymentsJournal, The Journal of Allergy & Clinical Immunology, Amazon Science and

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Indranil Bhattacharya’s Biography

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I am a filmmaker, academic and a cultural commentator, writing on film, television and streaming services.

Articles

RT2S: A framework for learning with noisy labels

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By Indranil Bhattacharya, Zack Ye, Kaushik Pavani, Sunny Dasgupta
| Amazon Science
We introduce Robust Training with Trust Scores (RT2S), a framework to train machine learning classifiers with potentially noisy labels. RT2S calculates a trust score for each training sample, which indicates the quality of its corresponding label. These trust scores are employed as sample weights during training and optionally during threshold optimization.
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Assessing the Perceived Diabetes Burden, Clinical Care Gap, and Intent

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By Robert J Heine, Indranil Bhattacharya, Maithri Singh
| Dove Medical Press
Back to Journals » Diabetes, Metabolic Syndrome and Obesity » Volume 19 Original Research Diabetes Prevention, Care and Management Authors Heine RJ, Bhattacharya I, Singh M Received 20 August 2025 Accepted for publication 30 December 2025 Published 12 February 2026 Volume 2026:19 547492 DOI https://doi.org/10.2147/DMSO.S547492 Checked for plagiarism Yes Review by Single anonymous peer review Peer reviewer comments 2 Editor who approved publication: Prof.
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Cybernaut: Towards reliable web automation

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By Ankur Tomar, Hengyue Liang, Indranil Bhattacharya, Natalia Larios Delgado
| Amazon Science
The emergence of AI-driven web automation through Large Language Models (LLMs) offers unprecedented opportunities for optimizing digital workflows. However, deploying such systems within industry's real-world environments presents four core challenges: (1) ensuring consistent execution, (2) accurately identifying critical HTML elements, (3) meeting human-like accuracy in order to automate operations at scale and (4) the lack of comprehensive benchmarking data on internal web applications.
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