Fabio Guzman

Fabio Guzman

On-device ML Engineer

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About Me

Experienced in developing and optimizing machine learning models on devices. Proficient in Python, Swift, CoreML, PyTorch, and MPSGraph, with expertise in iOS and macOS application development. Employed and optimized CoreML models for the Apple Neural Engine, integrating them with iOS and macOS applications. Developed tools for profiling Core ML models. Led deployment of fine-tuned LLM models with Nvidia GPUs and designed RISC processors. PhD in Engineering with a focus on Explainable AI.

Contact Information

fabio.guzmanf@upb.edu.co

fguzman82.github.io

fabioguzmanfigueroa

fguzman82

@FGuzmanAI

🤗 fguzman82

Featured Projects

CLIP Finder

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An iOS app for semantic offline searches of gallery photos using natural language descriptions or the camera, employing Core ML models optimized for the Neural Engine and MPSGraph for pre- and post-processing on the GPU.

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Core ML Profiler

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A visual profiling tool for macOS to analyze Core ML models, providing estimation times and validation messages for the Neural Engine.

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PhD Thesis - Explainable AI

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Developed attribution methodologies to accurately identify concepts in convolutional neural networks (CNNs), generating reliable explanations to enhance confidence, transparency, and security in decisions made by CNNs.

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Natalius RISC Processor

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Designed and simulated a RISC processor, suitable for tape-out using SKY 130nm technology, including a hand-coded assembler-level compiler tool.

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Core ML Blog

Unlocking Core ML Potential: Mastering Async Batch Predictions

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Explore the async and batch prediction in Core ML and learn how to optimize your app's performance.

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Converting Models to Core ML

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Learn how to convert PyTorch models to Core ML format and deploy them on Apple devices.

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Profiling Core ML Models

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Explore techniques for profiling Core ML models, both with dedicated tools and on-device, to optimize performance across Apple devices.

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Introduction to MPSGraph

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Discover the power of Metal Performance Shaders Graph for accelerating machine learning and numerical computing on Apple Silicon GPUs.

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Education

Ph.D. in Engineering, Computer Vision - Explainable AI

Universidad Pontificia Bolivariana

2017 – 2022

Master of Advanced Studies in Embedded System Design

Università della Svizzera italiana - Switzerland 🇨🇭

2012 – 2013

M.Sc. in Engineering

Universidad Pontificia Bolivariana

2009 – 2011

B.Sc in Electronics Engineering

Universidad Pontificia Bolivariana

1999 – 2005

Skills & Expertise

Languages

  • Python
  • Swift
  • SwiftUI
  • C
  • C++
  • Verilog

Frameworks & Tools

  • CoreML
  • MPSGraph
  • PyTorch
  • Xcode
  • VSCode
  • Git
  • Linux shell scripting