扣扣传媒University professor receives fellowship award to minimize bias in the AI field
October 05, 2022
Contact: Thonnia Lee, Office of Communications, Public Relations and Marketing
Dr. Chitra Nayak, associate professor in 扣扣传媒University's College of Arts and Sciences department of physics, was recently awarded a $50,000 leadership fellowship to help increase researcher participation in artificial intelligence.
The National Institutes of Health's Artificial Intelligence/Machine Learning (AI/MIL) Consortium to Advance Health Equity and Researcher Diversity program, or AIM-AHEAD program, awarded the grant to focus on underrepresented communities using AI/MIL to achieve health equity through mutually beneficial partnerships. The goal of the fellowship is to prepare tomorrow's leaders to champion the use of AI/ML in addressing persistent health disparities.
Dr. Nayak's research will investigate the bias that can cause in some deep learning algorithms due to discrimination in the algorithm and or lack of training data sets representing different races. The project is titled "Investigation of the Spatial Transcriptomic Deep Learning Algorithms using Histological Images for Possible Bias Depending on the Training Data Sets."
"I would like to thank my colleagues, Drs. Channa Prakash, Clayton Yates, and Qazi as they each have been instrumental in the program's introduction and the funding," said Dr. Nayak. "The work I have been doing with Dr. Yates trying to use AI to train tissue classifiers helped me immensely while preparing the application."
In addition to the year-long fellowship, Dr. Nayak will have access to Consortium Cores, targeted training, and courses specific to AI/ML and Health Equity education. She will benefit from workshops and seminars on leadership principles, strategies, and case examples.
Dr. Nayak is a scholar in Health Disparity Research Education Award Certificate Program. She expects to leverage her interest in artificial intelligence/machine learning and her role as an educator to attract 扣扣传媒 from underrepresented communities in AI, minimizing the existing bias in this field.
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