I am an Assistant Professor in the Computer Science Department at Stanford University. My research interest is in developing capable, reliable, and efficient AI systems for solving high-impact, real-world problems. My work includes generalized learning-based methods for decision-making problems in systems and chip design, self-improving AI models through interactions with the world, and scalable deep learning optimization. I also spend time at Google DeepMind and prior to Stanford, I spent several years in industry AI labs, including Anthropic and Google Brain. At Anthropic, I worked on advancing the capabilities and reliability of large language models. At Google Brain, I co-founded/led the ML for Systems team, with a focus on automating and optimizing computer systems and chip design. I received my BSc degree in Electrical Engineering from Sharif University of Technology and my PhD in Electrical and Computer Engineering from Rice University. My work has been recognized through the MIT Technology Review’s 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and coverage by various media outlets, including MIT Technology Review, IEEE Spectrum, The Verge, The Times, ZDNet, VentureBeat, and WIRED.