I indulged most of my childhood watching old Japanese sci-fi anime like A Farewell to Weapons and Ghost in the Shell in Beijing, my hometown. When I came to the United States at the age of 15, I became excited about the field of artificial intelligence from online communities like Kaggle. I was fortunate to study computer science at Carnegie Mellon University, where I learned rigorous problem-solving skills in computing. I’m interested in creating autonomous machines and studying how they may benefit us. I'm a member of the CMU automated learning systems group (Catalyst) and Recurse Center. I'm currently working on Praxis, the end-to-end platform for time-series workflows that focuses on investigative visualization tools, backtesting, and external data feeds.



I indulged most of my childhood watching old Japanese sci-fi anime like A Farewell to Weapons and Ghost in the Shell in Beijing, my hometown. When I came to the United States at the age of 15, I became excited about the field of artificial intelligence from online communities like Kaggle. I was fortunate to study computer science at Carnegie Mellon University, where I learned rigorous problem-solving skills in computing. I’m interested in creating autonomous machines and studying how they may benefit us. I'm a member of the CMU automated learning systems group (Catalyst) and Recurse Center. I'm currently working on Praxis, the end-to-end platform for time-series workflows that focuses on investigative visualization tools, backtesting, and external data feeds.
























Google Scholar    ORCID    Semantic Scholar

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

  • responsibly scaling autonomous systems in the real world
  • heterogeneity (communication, operator, backend, graph) in machine learning systems
  • curation of large-scale, multi-modal datasets
  • optimization techniques for DSL (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 with me, please send all correspondences to peiyuan [at] praxispioneering [dot] com.

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