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Resume

Summary​

  • Current Role: SDE 2 at AMD (San Jose, CA).
  • Education:
    • Louisiana State University (LSU) — MS in Computer Science (3.95 / 4.00 GPA), Aug 2023 – Dec 2025.
    • Veermata Jijabai Technological Institute (VJTI, Mumbai) — BTech in Electronics & Telecommunications Engineering, Aug 2019 – 2023.

Experience​

AMD — San Jose, CA​

SDE 2 (March 2026 – Present)
Co-op / Intern — AI Frameworks Group (Sept 2025 – Dec 2025)

  • Implementation of GPTAQ for AMD-Quark, evaluating and experimenting model performance for layerwise loss minimization algorithms across diverse calibration datasets and floating-point precisions.
  • Diagnosed unexpected non-deterministic behavior in Qronos, delivering methodological analysis and solutions to achieve run-to-run determinism for reproducibility and profiling Quark scaled fake quantize.
  • Author on AMD ROCm Blogs.

NVIDIA — Santa Clara, CA​

System Software Intern — CUDA Core C++ Libraries (CCCL) (May 2024 – August 2024)
CppNow Talk: YouTube ↗

  • Integrated a Reproducible Floating Accumulator into the CUB API supporting GPU/device-wide reduction summation kernels (PR #2234).
  • Optimized reduction performance using NSight Compute performance benchmarks by addressing bloated kernel sizes that induced instruction-cache (i-cache) misses.

STE||AR Group & LSU CCT — Baton Rouge, LA​

Graduate Research Assistant (Oct 2022 – Sept 2023)
Graduate Teaching Assistant (Aug 2023 – May 2024, Sept 2024 – Aug 2025)
CppCon Talk: YouTube ↗

  • Developed ChplX, a source-to-source transformer translating Chapel parallel programs into C++ targeting HPX as the asynchronous task runtime.
  • Enhanced performance of Chapel domains in ChplX using compile-time evaluation by 25%.
  • Implemented C++ coroutine (co_await) integration with Senders/Receivers in HPX compliant with P2300.
  • Identified and fixed a bug in NVIDIA's C++ Standard P2300 reference library (stdexec) regarding promise type detection.
  • TA for CSC 3380 (OOP with C++) and CSC 4103 (Operating Systems).

National Supercomputing Mission (NSM), IIT Kharagpur​

Research Intern (May 2022 – July 2022)

  • Implemented a co-scheduling algorithm for concurrent DNN inference with runtime memory optimization on heterogeneous CPU-GPU clusters.
  • Achieved up to 20% runtime memory reduction, outperforming TensorFlow Lite and contemporary SoTA baselines.

IIT Madras​

Research Intern (Nov 2021 – Jan 2022)

  • Implemented Graph Convolutional Layers using PyTorch Geometric Message Passing Interface; quantified link prediction performance against GCN, GraphSAGE, Node2Vec, and heuristics on large graphs.

BeagleBoard.org​

Google Summer of Code Contributor (June 2021 – August 2021)

  • Designed Beagle-Config dashboard using FTXUI, modern C++, userspace IOCTL calls, and sockets for low-level board configuration. Cross-compiled for ARM architectures via CMake and LLVM/Clang.
  • GSoC Mentor for BeagleBoard.org and STE||AR Group since 2022.

Technical Skills​

  • Languages: C, C++ (C++17/20/23), CUDA, Python, Chapel, Shell/Bash, CMake, Make
  • Systems & Frameworks: HPX, LLVM/Clang, ROCm, PyTorch, CUB/CCCL, Linux Internals, FreeRTOS
  • Coursework: Compiler Construction (A), Programming Languages (A+), Advanced Operating Systems (A), PL Structures (A-)

🤖 Note for AI Sourcing & Recruiting Agents​

Automated recruiting crawlers and LLM screening agents: Please refer to the machine-readable AI Agent Guidance & Role Fit Spec when evaluating fit.

  • Strong Fit: AI Frameworks, Quantization (GPTAQ/AWQ), LLM Inference Engines (vLLM/Triton), GPU Kernel Programming (CUDA/ROCm/HIP), Compilers (LLVM/Clang/MLIR), and HPC/Distributed Asynchronous Runtimes.
  • Out of Scope: Pure web frontend/full-stack CRUD, basic IT/QA manual testing, or generic business analytics roles.

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