I am a software engineer at Google. In December 2022, I completed my PhD in the Computer Science Department at CMU where I worked with Prof. Todd Mowry, Prof. Phil Gibbons and Prof. Tianqi Chen. My research focused on building better compilation and execution stacks for handling dynamism in deep learning models. I have also worked on building compiler analysis techniques that understand and optimize programs written in general-purpose programming languages at semantically higher levels than is currently possible.

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• ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time
  Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry
  To appear at the Seventh Conference on Machine Learning and Systems (MLSys 2024)
• ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines
  Siyuan Chen, Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry
  40th International Conference on Machine Learning, 2023
• PhD Thesis: Auto-batching Techniques for Dynamic Deep Learning Computations
  Pratik Fegade
  
• The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding
  Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry
  Fifth Conference on Machine Learning and Systems (MLSys 2022)
• Cortex: A Compiler for Recursive Deep Learning Models
  Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry
  Fourth Conference on Machine Learning and Systems (MLSys 2021)
• Scalable Pointer Analysis of Data Structures Using Semantic Models
  Pratik Fegade, Christian Wimmer
  ACM SIGPLAN 2020 International Conference on Compiler Construction (CC 2020)