Himanshu Shekhar Sahoo

PhD Researcher NLP, Explainable AI, Fairness in AI

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"Artificial Intelligence will become a Mirror of Humanity."

I am PhD candidate at University of Minnesota advised by John Sartori. My research focuses on developing artificial intelligence (AI) based fair, interpretable and novel clinical decision support solutions around healthcare problems spanning multiple modalities (for example, audio, text, discrete and image data). With 6+ years of hands-on machine learning experience involving real world big data, I also serve as the technical AI lead for a cross-collaborative project designed to aid clinical decision support for some of the most rare and serious health-related diagnosis.

In the future, I hope to work in the field of research and development. Coming from a developing country, I’ve personally witnessed how limited life can be without technology, particularly in healthcare. This was especially true during the COVID-19 pandemic, when hospitals in India were severely understaffed, and COVID-19 positive patients were frequently spotted being treated on the streets by local citizens. The pandemic has also claimed the lives of some of my friends and family members. These experiences inspire me to put my creativity and expertise to good use and contribute constructively to society.

news

Dec 12, 2022 Our paper “Towards Fairness and Interpretability: Clinical Decision Support for Acute Coronary Syndrome” was presented at ICMLA held in Nassau, Bahamas!
Sep 23, 2022 Our short paper “Towards Fairness and Interpretability: Clinical Decision Support for Acute Coronary Syndrome” got accepted to International Conference on Machine Learning and Applications (ICMLA) 2022. This paper is a first of its kind addressing key issues around fairness and interpretability of ML based clinical decision support systems.
Jun 6, 2022 Joining Microsoft Health Futures as a PhD Research Intern!
Jun 7, 2021 Our project Ambiscribe has been funded by Cisco Tech for HealthCare Research Grant to develop AI powered clinical decision support tools addressing rare and serious diagnosis.