Chinmay Deshpande is a Member of Technical Staff at AMD with 11 years of systems-focused engineering experience and a PhD in Computer Science from UC Irvine. He specializes in compilers, program and binary analysis, and low-level code generation—currently contributing to the LLVM backend for the ROCm GPU compiler stack targeting ML, graphics, and general compute. His doctoral work advanced binary recompilation for multithreaded programs and recovered stack-based variables into LLVM IR, and he’s applied those skills to improve Binary Ninja’s API and User-Informed Dataflow features. Comfortable on both offensive and defensive security fronts and fluent in formal analysis and deep learning, he blends research rigor with production engineering. Based in San Jose, he’s moved from “lifting” binaries in research to “lowering” for GPUs in industry, bridging deep reverse-engineering insight with high-performance code generation.
11 years of coding experience
7 years of employment as a software developer
Bachelor of Technology (B.Tech.), Information Technology, Bachelor of Technology (B.Tech.), Information Technology at National Institute of Technology Karnataka
Public API, examples, documentation and issues for Binary Ninja
Role in this project:
Back-end Developer
Contributions:9 commits, 7 PRs, 35 pushes in 2 months
Contributions summary:Chinmay primarily focused on improving the Binary Ninja API, particularly in examples and the implementation of User-Informed Dataflow (UIDF) features. They fixed existing examples, ensuring proper core shutdown procedures. Key contributions include API updates and refactoring function names, as well as adding the required documentation for UIDF functionality. The user also implemented UI changes to support the new UIDF functionality.
Contributions:47 commits, 4 pushes, 2 comments in 8 months
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Chinmay Deshpande - Member Of Technical Staff at AMD