Felix Hirwa Nshuti

About

I work at the intersection of computer architecture, optimizing compilers, and theoretical machine learning: building ML infrastructure that connects low-level systems engineering to high-level mathematical abstractions, using convex optimization to close the gap between algorithmic intent and architectural execution.

My focus is compiler infrastructure for AI workloads, and specifically how intermediate representations turn computation graphs into hardware-aware schedules. I also work on ML-driven signal processing, applying tensor-level optimizations for predictable performance in real-time inference. I am an active open-source contributor and maintainer. Before CMU I completed a BTech in Computer Science and Engineering at Pandit Deendayal Energy University.

In my free time I play football and share memes with friends.

Publications

• TransISA: A Static Transpiler for Migrating Legacy x86 Assembly to ARM via LLVM IR

  Felix Hirwa Nshuti, Shakti Mishra

  Proceedings of the 2026 Improving Scientific Software Conference, pp. 1–6. NCAR/UCAR (NCAR/TN-593+PROC), 2026.

  Details/Proceedings DOI/Slides/Code

Presentations

• TransISA: A Static Assembly Transpiler for Automating x86-to-ARM Migration in Scientific Computing

  Improving Scientific Software Conference, Boulder, CO — 2026

  Slides (PDF)/doi:10.5281/zenodo.19722586

Selected Projects

• Enhancing Prophet with Question-Aware Captioning for Knowledge-Based VQA

  Python, PyTorch, Transformers — Aug 2025 to Dec 2025

  • Improved knowledge-based VQA performance by integrating question-aware captioning.
  • Built a module generating contextually relevant captions to improve knowledge retrieval.

• Context-Aware Demand Forecasting in Pittsburgh's Bike Share System

  Python, XGBoost, CatBoost, statsmodels — Aug 2025 to Dec 2025

  • Incorporated temporal, weather, and event-based features to improve prediction accuracy.
  • Applied time series analysis to capture complex usage patterns.

• TransISA — Lightweight CISC-to-RISC Transpiler

  LLVM, x86, AArch64, C++ — Dec 2024 to May 2025

  • Built a compiler pipeline using the LLVM C++ API to translate x86 to AArch64 via LLVM IR.
  • Extracted and analyzed control-flow graphs to support instruction-level translation.

• Scaling Deep Learning Backends with sktime

  Python, PyTorch, TensorFlow, Darts — May 2024 to Aug 2024

  • Added GRU and GRU-FCNN classifiers to sktime using PyTorch.
  • Migrated classifier models from legacy sktime-dl into the main sktime repository.
  • Implemented the modular interface for darts regression models in sktime.