Exploring the systems infrastructure that makes machine learning reliable at scale.
I'm a Senior at NYU graduating with a dual major in Computer Science and Data Science. I enjoy building systems and tools that help people, with a focus on machine learning and its applications.
My work is driven by a curiosity for how systems behave at scale and how strong infrastructure unlocks reliable machine learning applications. I'm most excited by problems that sit between systems engineering and applied ML.
Focus Areas
I'm drawn to learning systems that operate under physical and timing constraints, where decisions must be made quickly and imperfectly. My current work focuses on low-latency applications and distributed systems, and I'm increasingly curious about how similar constraints appear in robotics and embodied systems.
I'm interested in the engineering work that supports machine learning systems beyond the model itself. At Amazon, I worked on automating and hardening configuration workflows that supported ML-backed services, reducing setup time by 93% and removing operational bottlenecks for teams deploying at scale.
I care about building software that responds to people's needs. While working on Orbit, I designed systems that coordinate speech recognition, language models, and audio playback under tight latency constraints, so users receive feedback that feels timely and usable rather than delayed or fragmented.
Education
New York University
Dual Major in Computer Science and Data Science
Relevant Coursework: LLMs, Software Engineering, Machine Learning, Algorithms, Data Management, Operating Systems, Causal Inference