Rishipal Singh Bhatia
Rishipal is a Senior ML Compiler Engineer at Google. He is a 2016 alumnus of Georgia Tech and has extensive experience optimizing compilers across various domains, including web technologies, computer graphics, machine learning, and artificial intelligence.
Prior to his current role, Rishipal was a Senior Software Engineer at Intel, Santa Clara, from 2016 to 2019. There, he focused on optimizing graphics and machine learning workloads for Intel's GEN Processor Graphics architecture as part of the Intel Graphics Compiler team.
From 2019 to 2025, Rishipal was a Senior Software Engineer on Google's TypeScript/JavaScript compiler team. In this position, he made significant improvements to build times for Google's JavaScript transpiler and optimizer, redesigned the JavaScript conformance checking infrastructure, and developed numerous compiler-centric tools. He also represented Google as a delegate for the ECMAScript TC39 committee, contributing to the definition of the ECMAScript 2026+ standard language specification.
Presently, Rishipal applies his expertise in compiler design to the TPU Compiler, where he explores innovative optimizations for Google's Tensor Processing Units.
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Source-to-source compilers may perform inefficiently by executing transpilation passes on scripts that do not contain the specific language features a pass is designed to transform, potentially leading to redundant processing. A compiler can analyze a script to generate a per-script feature map, for example, by identifying language features in its abstract syntax tree (AST). Before executing a transpilation pass, the compiler can check this map and may bypass the pass for that script if the specific feature targeted by the pass is not present. This feature map can also be dynamically updated throughout the compilation process as other passes transform the code. This method of conditional pass execution based on content-aware analysis may reduce redundant AST traversals, which could decrease overall compilation time and computational resource consumption.
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