Anthony Sirico

Anthony Sirico
United States Navy · Naval Aviation

Doctor of Philosophy
Lieutenant Commander, Department Head, and Instructor Pilot

About

6
Publications
358
Reads
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7
Citations
Introduction
A recent graduate from Colorado State University with a PhD in Systems Engineering, I am conducting research in my spare time until my time in the US Navy is complete.
Additional affiliations
January 2023 - present
VT-31
Position
  • Naval Aviator/Flight Instructor
July 2013 - July 2016
VQ-1
Position
  • Naval Aviator
July 2016 - February 2019
VUP-19
Position
  • UAV Pilot
Education
August 2020 - August 2024
Colorado State University
Field of study
  • Systems Engineering
July 2016 - September 2018
Naval Postgraduate School
Field of study
  • Systems Engineering
May 2007 - May 2011
United States Naval Academy
Field of study
  • Astrophysics

Publications

Publications (6)
Preprint
Full-text available
In this paper, we present Iterative Classification of Graph-Set-Based Design (IC-GSBD), a framework utilizing graph-based techniques with geometric deep learning (GDL) integrated within a set-based design (SBD) approach for the classification and down-selection complex engineering systems represented by graphs. We demonstrate this approach on aircr...
Thesis
Full-text available
Many complex engineering systems can be represented in a topological form, such as graphs. This dissertation introduces a framework of Graph-Set-Based Design (GSBD) that integrates graph-based techniques with Geometric Deep Learning (GDL) within a Set-Based Design (SBD) approach to address graph-centric design problems. We also introduce Iterativ...
Presentation
Full-text available
Final defense presentation for the degree of Doctor of Philosophy.
Conference Paper
Many complex engineering systems can be represented in a topological form, such as graphs. This paper utilizes a machine learning technique called Geometric Deep Learning (GDL) to aid designers with challenging, graph-centric design problems. The strategy presented here is to take the graph data and apply GDL to seek the best realizable performing...
Article
Many complex engineering systems can be represented in a topological form, such as graphs. This paper utilizes a machine learning technique called Geometric Deep Learning (GDL) to aid designers with challenging, graph-centric design problems. The strategy presented here is to take the graph data and apply GDL to seek the best realizable performing...

Questions

Questions (2)
Question
I am interested in talking with engineers, specifically Systems Engineers, who work in astrophysics and cosmology. I am looking for some potential overlap between the two other than the development of instrumentation.
Question
Does anyone know if it is possible to use these three in c++? I know tensorflow has an API for c, but what about the others?

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