Welcome! My name is Simran and I'm a PhD student in Computer Science at Stanford, where I am advised by Chris Ré. I am part of the
Hazy Research Lab and supported by a
Stanford Graduate Fellowship as the Sequoia Capital Fellow. I am also an advisor to
Cartesia AI and an academic partner of
Together AI.
My research is in AI systems. I focus on expanding the Pareto frontier between quality and efficiency, to unlock new AI capabilities, by considering AI algorithms, hardware, and applications in lockstep. Recently, I've looked at:
- Algorithms: How do we build AI architectures that scale efficiently?
(Zoology, Based, JRT, LoLCATS, Monarch Mixer) - Hardware: As the complexity of AI hardware increases, how can we make it easier to extract high utilization for new AI algorithms?
(ThunderKittens, Megakernels, KernelBench) - Applications: How do we bridge the efficiency gaps that emerge as models are deployed in new settings, such as data management?
(AMA for personal data, Evaporate, Data Wrangling)
Recently created / taught
systems for machine learning (CS 229s) at Stanford, materials are released.