I indulged most of my childhood watching Japanese sci-fi anime like A Farewell to Weapons and Ghost in the Shell in Beijing, my hometown. When I came to the U.S. at the age of 15, I was attracted to the field of artificial intelligence on Kaggle. I was fortunate to study computer science at Carnegie Mellon University, where I learned rigorous problem-solving skills. I'm interested in mapping components of business decision-making processes to intelligent systems from the silicon up to complex software. I'm a member of the CMU automated learning systems group (Catalyst) and Neo. I'm currently working on Praxis, helping teams to integrate large, difficult external datasets.



I indulged most of my childhood watching Japanese sci-fi anime like A Farewell to Weapons and Ghost in the Shell in Beijing, my hometown. When I came to the U.S. at the age of 15, I was attracted to the field of artificial intelligence on Kaggle. I was fortunate to study computer science at Carnegie Mellon University, where I learned rigorous problem-solving skills. I'm interested in mapping components of business decision-making processes to intelligent systems from the silicon up to complex software. I'm a member of the CMU automated learning systems group (Catalyst) and Neo. I'm currently working on Praxis, helping teams to integrate large, difficult external datasets.
























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I’m interested in a wide range of topics in computing, including (but not limited to; in decreasing order of preference):

  • scaling responsible, resilient, and autonomous systems in the real world
  • curation of large-scale, multi-modal, structured datasets
  • heterogeneity (communication, operator, backend, graph) in machine learning systems
  • optimization techniques for domain-specific (deep learning, privacy-preserving computation) compilers
  • emergent techniques in generative modeling
  • geo-distributed, fault-tolerant model training and inference
  • refreshing, interdisciplinary uses of AI; an example

If you have any questions regarding our works or would like to collaborate, please send all correspondences to peiyuan [at] praxispioneering [dot] com.

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