Google Research

Daniel Freeman


I'm an AI Resident working in the Brain team at Google. Prior to Brain, I received my PhD in Physics from the University of California at Berkeley, where I studied topological quantum error correction and tensor network methods. While still interested in Quantum Information Science, since joining Google, I've refocused on the emerging field of metalearning, as well as its burgeoning applications for model based reinforcement learning. I'm particularly interested in algorithms for learning representations that are generally useful for tasks, as well as algorithms that learn to learn representations that are generally useful, and algorithms that learn to learn to learn, and so on. The AI Residency struck me as an optimal balance between the research environment I was familiar with, versus the broader umbrella of world-facing engineering problems that are unique to a more corporate environment. Even better, I was delighted to find that the Residency program offers a tremendous amount of research freedom that exceeds what I encountered even in graduate school. Since joining, I've thoroughly enjoyed working with Google's talented engineers, and have been awed by the scale of research problems one can tackle when leveraging Google-scale resources. In my free time, I'm interested in the art of story telling--be that through books, film, games, web serials or anything else.

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