Gayatri Tyagi
UN Sustainability Goals
- Good Health And Well-Being
- Reduced Inequalities
Areas of Excellence
- Data Science/AI/Robotics
- Health
Global Challenge: Improved Health and Well-Being
Abstract
Looking back on my college experience, GLASS has played a bigger role than I could have imagined. Getting ready to graduate is bittersweet, as I'm saying goodbye to some of the closest friendships I've ever had and looking forward to the beginning of a career I love. The opportunities for research, travel, professional development, access to conferences, support, and community have been invaluable. I have been so fortunate to have access to so many courses, materials, and equipment that have allowed me to explore what I am truly interested in. I applied to GLASS because I wanted to learn to be a socially responsible engineer and to find some way to make a difference. I have gotten the resources and support to do that in a way I couldn't have ever expected.
Bio:
Gayatri Tyagi studied Computer Science at NYU Tandon with minors in Robotics and Mathematics. She graduated with her B.S. in Computer Science in May 2024 and will be starting as a software engineer on the Hugo Robotic-Assisted Surgery system at Medtronic in August!
Over her four years at NYU, Gayatri has worked on an undergraduate student team, in a research lab, in various teaching environments, in hackathons, and more! She has truly broadened her horizons, followed her passion for learning, and found new experiences. She is currently a teaching assistant for the Computer Architecture and Organization course and a robotics coach at Erasmus High School. Last summer, she had the opportunity to intern at Boeing on the Flight Test and Instrumentation team with the Data Systems team as a software engineering intern.
Over the last year, she has also been involved with the Break Through Tech AI program at Cornell Tech, through which she has taken a machine learning foundations course and worked on a machine learning project with a small company. She is currently working with a student team on a machine learning competition dealing with image classification.