Azalia Misoheini

I am a member of technical staff at Anthropic, where I work on advancing large language models' helpfulness and reliability. Previously, I was a staff research scientist and manager at Google Brain, where I co-founded/led the Machine Learning for Systems Team, a larger research effort to automate and optimize the process of designing next generation computer systems and chips. Our deep reinforcement learning-based methodology for chip design was published in Nature and was used in the design of multiple generations of TPUs, the Google AI accelerator chips. I have published more than 50 peer-reviewed papers at scientific venues such as NeurIPS, ICML, ICLR, UAI, ASPLOS, SIGMETRICS, DAC, DATE, and ICCAD. I have a Ph.D. in Computer Engineering from Rice University. I have received MIT Technology Review's 35 Innovators Under 35 Award, Best Ph.D. Thesis Award at Rice University, and a Gold Medal in the National Math Olympiad in Iran. My work has been covered in various media outlets, including MIT Technology Review, IEEE Spectrum, Times, CNBC, The Verge, and WIRED.