Daniel Petti
Graduate Research Assistant
Cambridge, Massachusetts, United States
Summary
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Rockstar🎓
Top SchoolDaniel Petti is a PhD candidate in Computer Engineering and a Graduate Research Assistant at the University of Georgia, specializing in computer vision, machine learning, and robotics with a strong focus on precision agriculture and high-throughput phenotyping. He brings 12 years of hands-on experience spanning microcontrollers to multi-GPU clusters, and has deployed real-time C/C++ systems in both OS and bare-metal environments for medical and aerospace applications. Comfortable across the ML stack, he has contributed TensorFlow/Keras graph neural network layers and utilities (e.g., CensNet and incidence/line graph functionality) to open-source projects, demonstrating deep proficiency with NumPy and networkx. His industry background includes embedded software for diagnostic devices and a prototype robotic autopilot, reflecting an ability to bridge algorithmic research and rugged, real-world systems. Practiced in modern software practices like version control, unit testing, and agile workflows, he’s equally willing to “get his hands dirty” with hardware when needed. Based in Cambridge, MA, he combines academic rigor with production engineering discipline to move vision-based robotics from lab prototypes toward deployable solutions.
12 years of coding experience
2 years of employment as a software developer
University of GeorgiaBachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Rensselaer Polytechnic Institute
Stackoverflow
191reputation
10kreached
2answers
7questions
Github Skills (13)
keras10
graph-algorithms10
deep-learning10
graph-neural-network10
tensorflow10
python10
numpy10
recursion6
backtracking6
casting6
linux6
double6
char6
Programming languages (7)
Github contributions (5)
danielegrattarola/spektral
May 2022 - Jun 2022
Graph Neural Networks with Keras and Tensorflow 2.
Role in this project:
ML Engineer
ML EngineerContributions:2 reviews, 8 commits, 3 PRs in 28 days
Contributions summary:Daniel primarily focused on adding and refining layers and utility functions related to graph neural networks. Their contributions involved implementing the CensNet convolution layer, including both node and edge propagation steps. They also implemented and tested the binary transformation matrix, incidence matrix, and line graph functionality within the context of graph convolutions. The user demonstrated strong proficiency in TensorFlow and Keras, as well as libraries like NumPy and networkx.
pythondeep-learninggraph-deep-learningneural-graphneural-networks
djpetti/MALLARD
Jan 2021 - Mar 2025
Store image objects to MinIO object storage and metadata to MySQL database. Easily store and retrieve using metadata.
Contributions:214 pushes, 68 branches, 11 comments in 4 years 2 months
mysqlminiodatabasemetadatastorage
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