Bryce Chudomelka

Senior Software Development Engineer with expertise in machine learning, deep neural networks, and data science. Skilled in Python, AWS, Go and React, with a strong background in mathematics and physics. Experienced in developing innovative solutions for complex problems in areas such as computer vision, adversarial attacks, and differential equations.

Experience

Senior Software Development Engineer

Inspiren

• Reduced cloud service costs by 80% through database migration to Wasabi

• Led integration of Gemini Vision Pro for enhanced multi-modal model capabilities

• Developed HIPAA-compliant data anonymization system using AWS technologies

• Engineered privacy-focused object detection solution for IoT devices

November 2022 - Present

Software Consultant

Asigma Corporation

• Developed digital time-card system and payroll management interface

• Created inventory tracking and logging systems

• Launched customer-centric webpage using React

May 2021 - November 2022

Data Scientist

SiteZeus

• Provided location-intelligence services using geospatial and mobile data

• Optimized codebase and integrated multiprocessing for enhanced user experience

• Developed unit tests to improve code reliability

August 2021 - August 2022

Education

San Diego State University

Master of Science
Applied Mathematics - Dynamical Systems
May 2020

University of California, Santa Barbara

Bachelor of Science
Physics
June 2011

Skills

Programming Languages & Tools
Technical Strengths
  • Machine Learning & Deep Neural Networks
  • Data Science & Analysis
  • Cloud Computing (AWS, Azure, GCP)
  • Database Management (SQL, NoSQL)
  • Software Development & Engineering

Publications

  • Deep neural network for solving differential equations motivated by Legendre-Galerkin approximation (International Journal of Numerical Analysis and Modeling, 2023)
  • Search-and-Attack: Temporally sparse adversarial perturbations on videos (IEEE Access, 2021)
  • Robust Neural Networks inspired by Strong Stability Preserving Runge-Kutta methods (European Conference on Computer Vision, 2021)