NYU was founded on the belief that a student’s identity should not dictate the ability for them to access higher education. That sense of opportunity for all students, of all backgrounds, remains a pa...

NYU was founded on the belief that a student’s identity should not dictate the ability for them to access higher education. That sense of opportunity for all students, of all backgrounds, remains a part of who we are today and a critical part of what makes us a world class university. Our community embraces diversity, in all its forms, as a cornerstone of the NYU experience. We would like to better understand how your experiences would help us to shape and grow our diverse community. Please respond in 250 words or less.

Parasparopagraho Jivanam. My parents instilled in me countless Jain mantras from a young age. The foundational phrase of Jainism, Parasparopagraho Jivanam, was one I had uttered innumerable times but never understood its meaning. It was not until freshman year that Dr. Mutha, my Sunday school teacher, explained to me the beauty of the religion I was raised in. Simple yet profound, this Sanskrit expression roughly translates to “the universe is bound together by mutual interdependence”. It suggests we should orient our lives in the service of others while having gratitude for the innumerable people on whose efforts we enjoy life. Jivanam refers to the living beings of our world, implying a universal appreciation for all. Following this philosophy, I am compelled to traverse my community with gratefulness and positivity. I hope to spread the contagious antidote of love at NYU through my daily social interactions. I find the smallest moments of benevolence, like a compliment, often have the most potential for good. This idea of inclusivity is heavily integrated with my perspective on engineering. At NYU, I aim to be a voice that introduces diversity into the solutions to great engineering problems. Artificial intelligence, my field of interest, has a significant issue of discrimination. Many AI developers utilize training datasets with inherent biases, resulting in models with the same faults. For example, job-screening AIs may disfavor those with disabilities. I hope to create a data-processing blueprint to eliminate these prejudices in AI models while maintaining the effectiveness of computer learning.