I'm currently an MSCS student at Stanford. I spent the past two summers building performant large-scale systems at Cartesia. I also recently wrapped up four awesome years at Purdue.

Here are a few questions that I care about these days (and hope to contribute to!):

• Since high-performance code is becoming cheap now, what does the future systems stack look like? Can we retire the intermediate abstractions, keep a high-level spec as the source of truth, and generate optimized code? Do we need to formally verify against the spec?
• What does the next generation of interfaces look like? How can we make malleable software ubiquitous? Are current efforts (one and two) steps in the right direction? What if we augment all deterministic workflows with intelligence?
• How can we improve intelligence per dollar (or watt)? We seem to be making progress on the frontier by biting the bullet on ridiculous orders of scaling; it is time for efficiency to follow. Does this come from better algorithms, better hardware-architecture co-design, or cheaper compute?

My past work includes GPU benchmarking systems at Tensara, research on optimizer dynamics, and self-supervised representation learning for healthcare at the CVIRL and Heinz labs. I also built BoilerClasses and Tempus. Even before that, I was really into competitive programming and linguistics.

Outside of all this, I love a 🌿 good pesto dish, 🚗 roadtrips, minimalist design and 🔪 whodunits. I also sometimes try to pick up my camera.