NYU Tandon's premier honors program for globally competent and socially responsible engineers. My journey through leadership, global experiences, and technical innovation.
Who I Am
My mission in computer engineering centers on mastering embedded systems, particularly their applications in automation, sustainability, and intelligent systems. I am especially interested in how embedded systems can drive efficiency and precision in real-world solutions, such as automated sorting systems for waste management.
By integrating theoretical knowledge with hands-on expertise, I aim to develop the ability to conduct experiments, analyze complex datasets, and leverage engineering judgment to draw insightful conclusions. Through practical application, I seek to design innovative solutions that address pressing global challenges, including sustainable production and waste reduction.
Technology is constantly changing, so I make it a priority to stay up to date with new advancements and find creative ways to apply what I learn. Beyond the classroom, I seek out hands-on experiences that challenge me to solve real-world problems and push my skills further.
The Program
The Global Leaders and Scholars in STEM (GLASS) program at NYU Tandon is a three-year honors program that cultivates globally competent, socially responsible engineers. Scholars engage with the NAE Grand Challenges and the UN Sustainability Development Goals, emerging as innovators ready to change the world.
NYU Tandon Research
The two research areas at NYU Tandon that align most closely with my mission and engineering goals.
Global Impact
The sustainability goals and engineering grand challenges that my work connects to.
Research & Writing
New York City's subway system serves approximately 3.4 million daily riders yet continues to struggle with chronic reliability deficits and information asymmetries. Despite an on-time performance of 82.2% in 2024, riders still experienced over 486,000 delayed trains throughout the year.
This paper presents SmartTransit NYC — a conceptually designed AI-powered prediction system that addresses the critical information gap between raw transit data and rider decision-making. By integrating real-time MTA data feeds, historical delay patterns, weather variables, and time-based usage signals, the system would generate probabilistic outputs across three dimensions: delay likelihood, crowding forecasts, and optimized alternative routing.
Environmental projections estimate that even a 2% increase in subway ridership attributable to improved predictive trust could eliminate approximately 100,000 car trips per day and reduce annual CO₂ emissions by roughly 30,000 tons.
The 5 Windows of Growth
GLASS scholars grow across five dimensions. Click a window to explore my experiences in each.