Kyle Beggs

Kyle Beggs

PhD candidate in Computational Science

University of Central Florida


Hi! I am a Software Engineer developing Computational Electromagnetics (CEM) and optics software at Metalenz. I am concurrently finishing up my Ph.D. at the University of Central Florida (UCF) working with Dr. Alain Kassab and Dr. Eduardo Divo on the development of meshless methods for fluid mechanics simulations, specifically hemodynamics. I am mainly interested in using next-gen computational tools to accelerate scientific computing.


  • Computational Physics
  • Scientific Machine Learning
  • Automatic Differentiation
  • High-Performance Computing


  • Ph.D. in Mechanical Engineering (Computational Mechanics), 2023 (expected)

    University of Central Florida

  • MS in Mechanical Engineering (Thermofluids), 2018

    University of Central Florida

  • BS in Mechanical Engineering (Thermofluids), 2016

    University of Central Florida



Software Engineer (Computational Physics)


Nov 2022 – Present Remote

Development of computational electromagnetics and optics software.

  • Work closely with the research/analysis engineers in assessing their needs for the in-house suite of simulation software.
  • Identify computational bottlnecks and ease them to help speed up product development.
  • Leverage next-gen computational tools to further accelerate design-simulation workflow.

Computational Analysis Engineer - Contractor


Apr 2020 – Present Orlando, FL
Computational analysis of a multiscale (from microns to meters) domain modeling a cryogenic system in vacuum. Setup and run simulations spanning all modes of heat transfer - radiative, conduction, and convection using the commercial software STAR-CCM+.

Graduate Research Assistant - Computational Mechanics Lab

University of Central Florida

Jan 2020 – Present Orlando, FL

Development of a meshless PDE solver for hemodynamics modeling. Simulating hemodynamics is a challenging problem due to complex geometries, moving boundaries, and arguably most importantly - boundary conditions that are coupled to solution of the domain which requires multiscale modeling.

  • Solve Navier-Stokes using the Radial-Basis Function Meshless Method (RBF-FD)
  • Implement a tightly-coupled iterative scheme for the complex hemodynamic boundary conditions
  • Building an easy to use and high-performance library to perform hemodynamics simulations using these methods

Machine Learning Engineer, Full-time Intern/Contractor

Dassault Systemes SIMULIA, Physics R&D Group

Jan 2019 – Jan 2020 Boston, MA

Development of a tool for use in cardiovascular diagnostics. My main task was to create a machine learning pipeline for automatic 3D segmentation and pre-processing of medical images for use in a Computational Fluid Dynamics (CFD) simulation.

  • Modified an existing Convolutional Neural Network (CNN) model achieving increased performance for our specific application
  • Aided in the development of a graphics tool to annotate the input data
  • Performed custom data augmentation along with inspecting, cleaning, and normalizing the dataset
  • Implemented an eigenvalue-based filter for volumetric images in Python (enhances tube-like structures in volume images)

Lab Instructor - Numerical Methods in Engineering (undergraduate)

University of Central Florida, Department of Mechanical and Aerospace Engineering

Aug 2018 – Present Orlando, FL

Run the lab section of a numerical methods course. Demonstrating how to efficiently translate algorithms in pseudocode to real code using MATLAB.

Topics covered:

  • root finding algorithms (e.g. Newton-Raphson)
  • linear simultaneous equations (e.g. Gauss Elimination, Gauss-Seidel)
  • curve fitting and interpolation (e.g. Least Squares)
  • solving ODEs (e.g. Euler’s Method and Runge-Kutta).

Lab Instructor - Computational Biofluids (graduate)

University of Central Florida, Department of Mechanical and Aerospace Engineering

Jan 2018 – Present Orlando, FL

Run the lab section of a computational biofluids course. Demonstrating how to setup and run CFD simulations in context of biofluids.

Topics covered:

  • geometry pre-processing / meshing
  • physics model selection and considerations
  • solver settings
  • post-processing and analysis

















UNIX-like systems


High-Performance Computing



POD-RBF is a Python package for building Reduced-Order Models using Proper Orthogonal Decomposition with Radial Basis Functions.

Selected Coursework (Graduate)

  • Numerical Methods
  • Continuum Mechanics
  • Computational Fluid Dynamics
  • Finite Elements
  • Deep Learning for Medical Images
  • Computational Biofluids
  • Optimization in Engineering
  • Fluid Mechanics
  • Biofluid Mechanics
  • Heat Transfer
  • Bioinstrumentation


  • 786 376 4477
  • 12760 Pegasus Dr, Orlando, FL 32816
  • Enter Engineering Building 1 and go to the hallway closest tothe atrium. Room 147.