Biography

Ryan Barron is a PhD student in Computer Science at the University of Maryland, Baltimore County (UMBC), where he also earned his MS (2023) and BS (2021) in Computer Science, alongside a BA in Political Science (2021). As a full-time researcher at Los Alamos National Laboratory, Ryan is at the forefront of advancing knowledge representation, robotics, tensors, and natural language processing. His work, marked by innovative research and practical applications, aims to bridge the gap between theoretical computer science and tangible technological advancements.

Ryan’s educational journey is marked by a distinctive combination of computer science and political science, fostering a multidisciplinary approach to addressing complex challenges in technology and societal issues. Beyond his academic pursuits, Ryan has practically applied his knowledge and insights as a congressional intern for Congressman Ruppersberger MD02 in 2018, where he gained invaluable experience in legislative processes and public service. Furthermore, his engagement extends to community and ethical leadership, as evidenced by his active membership in Door to Virtue Lodge #46 in Westminster, MD, since 2018, underscoring his commitment to contributing positively to society.

Interests
  • Knowledge Representation
  • Robotics
  • Tensors
  • Natural Language Processing
Education
  • PhD in Computer Science, Present

    University of Maryland, Baltimore County

  • MS in Computer Science, 2023

    University of Maryland, Baltimore County

  • BS in Computer Science, 2021

    University of Maryland, Baltimore County

  • BA in Political Science, 2021

    University of Maryland, Baltimore County

  • AS in Applied Science - Cybersecurity, 2018

    Carroll Community College

Skills

Technical
Python
C++
Neo4j
Hobbies
custom/language Language Learning
Cats
custom/motorcycle-solid Motorcycles

Experience

 
 
 
 
 
Graduate Research Assistant (GRA)
August 2022 – Present Los Alamos, New Mexico

Eperiences include:

  • Dense Domain-specific Knowledge graph construction for LLM knowledge bases
  • Training datasets for LLM fine-tuning
  • Matrix decomposition on large, irregular text patterns
 
 
 
 
 
Adjunct Lecturer
August 2022 – July 2023 Baltimore, Maryland

Classes taught:

  • Introduction to Computer Science I, Python
  • Advanced Programming, Python
  • Data Analysis and Structures, Python
  • Introduction to Data Science
  • Data Structures, C++
 
 
 
 
 
Graduate Teaching Assistant
January 2021 – January 2023 Baltimore, Maryland

Classes:

  • Principles of Computer Security (CMSC 426)
  • Natural Language Processing (CMSC 473/673)
  • Social and Ethical Issues in Information Technology (CMSC 304)
  • Operating Systems in the C Programming Language (CMSC 421)
 
 
 
 
 
Undergraduate Teaching Fellow & Assistant
August 2019 – December 2020 Baltimore, Maryland

Classes:

  • Data Structures, C++
  • Introduction to Computer Science I, Python

Publications

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(2024). Catch 'em all, Classification of Rare, Prominent, and Novel Malware Families. 12th International Symposium on Digital Forensics and Security 2024.

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(2024). Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization. 12th International Symposium on Digital Forensics and Security 2024.

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(2023). Interactive Distillation of Large Single-Topic Corpora of Scientific Papers. 2023 International Conference on Machine Learning and Applications (ICMLA).

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(2023). A Collaborative Building Task in VR vs. Reality. 18th International Symposium on Experimental Robotics (ISER 2023).

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(2023). Robust Adversarial Defense by Tensor Factorization. 2023 International Conference on Machine Learning and Applications (ICMLA).

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