Sameeran Joshi is a PhD student and graduate research assistant at the University of Utah with eight years of experience specializing in compilers, high-performance computing, and programming models for novel computer architectures. He has industrial experience at AMD—contributing to CPU compiler optimization, LLVM/Flang, and AI accelerator backends—and internship work that improved vectorization and performance for VLIW/NPU/AI engines. His research blends dataflow programming models, domain-specific languages, and data-movement optimizations, and he has contributed backend support (e.g., GraphCore) to Data-Centric frameworks like DaCe. Equally comfortable in lab and industrial settings, he’s explored mapping HPC workloads onto a range of AI accelerators at Argonne’s AI Testbed, revealing practical compiler and software-stack challenges. Colleagues describe him as a pragmatic optimizer who turns architecture-aware theory into measurable speedups across multidimensional kernels.
8 years of coding experience
3 years of employment as a software developer
Bachelor of Engineering, Computer Science, Bachelor of Engineering, Computer Science at Pune Vidhyarthi Griha's College of Engineering and Technology Pune
Doctor of Philosophy, Computer Science, Doctor of Philosophy, Computer Science at University of Utah, USA
The LLVM Project is a collection of modular and reusable compiler and toolchain technologies. Note: the repository does not accept github pull requests at this moment. Please submit your patches at http://reviews.llvm.org.
Contributions:2 PRs, 40 pushes, 55 branches in 2 years 10 months
F18 is a front-end for Fortran intended to replace the existing front-end in the Flang compiler
Contributions:2 PRs, 61 pushes, 20 branches in 6 months
fortrancompilerfront-end
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