David W. Ludwig II, PhD

AI & Software Engineer · Ph.D. Computational & Data Science

Location: Nashville, TN
Email: davidludwigii@gmail.com
Phone: (615) 519-1790
Portfolio: davidludwig.dlii.tech/portfolio
GitHub: github.com/SirDavidLudwig
LinkedIn: linkedin.com/in/david-w-ludwig
ORCID: 0009-0001-5851-7964

Professional Summary

Computer scientist and Ph.D. in Computational & Data Science with 15 years of software engineering experience and 6 years of machine learning experience. Published first-author researcher with a best paper award and a track record of developing production-grade software, full-stack applications, and scalable machine learning systems. Combines strong research training with broad engineering experience to build reliable, high-performance software and AI solutions.

Education

• Ph.D. Computational & Data Science

  Middle Tennessee State University
  Aug 2021 – Aug 2025 · GPA: 4.0
  Dissertation: Deep Learning for High-throughput Sequencing and Multimodal Ecological Systems
  Advisor: Joshua L. Phillips

• M.S. Computer Science

  Middle Tennessee State University
  June 2018 – Dec 2020 · GPA: 4.0
  Thesis: A Neurobiologically-inspired Deep Learning Framework for Autonomous Context Learning
  Advisor: Joshua L. Phillips

• B.S. Computer Science

  Middle Tennessee State University
  Aug 2015 – Dec 2019 · GPA: 3.63

Skills

• Programming Languages

  • Expert: Python, JavaScript, TypeScript, C/C++, Java, PHP, HTML/CSS, SQL

• Machine Learning & Data Science

  • Expert: PyTorch, TensorFlow/Keras, Matplotlib, NumPy, Weights & Biases
  • Proficient: Scikit-learn, QIIME 2, Hugging Face

• Software Engineering & Frameworks

  • Expert: Node.js, MySQL, Qt, Express, WebSockets
  • Proficient: Laravel, Vue.js, Electron

• Tools & Platforms

  • Expert: Docker, Git/GitHub, Linux, Bash, Jupyter, Slurm
  • Proficient: CI/CD, Coveralls/NYC

Professional Experience

• Research Assistant

  Middle Tennessee State University · Aug 2021 – Aug 2025

  • Built the first transformer-based deep learning architectures for unstructured data targeting high-throughput sequencing and methodologies for modeling multimodal ecological systems
  • Developed a container format for efficient handling of high-throughput sequencing data in ML workflows
  • Scaled model training and data processing across HPC clusters with optimized distributed pipelines
  • Supervised undergraduates in NSF REU program in development of machine learning projects that led to publications
  • Invited to present research at Cumberland University (2023) and Oak Ridge National Laboratory (2025)

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• Research Assistant / Teaching Assistant

  Middle Tennessee State University · Jan 2019 – Dec 2020

  • Designed a reinforcement learning framework for autonomous task inference and context switching, enabling models to adapt to multiple tasks without needing explicit context input
  • Graded assignments, provided tutoring, and led lab sessions for Computer Science I and Computer Organization

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• Independent Software Engineer Contractor / Consultant

  Renova Health · Jul 2018 – May 2019

  • Delivered a proprietary healthcare plan management platform aligned with client specifications in a contractual engagement using a full-stack architecture
  • Supported follow-on development of the platform and provided technical onboarding for new developers

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• Full-Stack Web Developer

  Freelance / Self-Employed · May 2014 – Dec 2021

  • Delivered custom full-stack web applications for local businesses, featuring responsive admin dashboards, scheduling tools, online payments, and SEO-tailored architectures to enhance discoverability and streamline operations

Selected Projects

Machine Learning & AI

• SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing

  Transformer-based deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing data. Published in Bioinformatics (2025).
  Tech: Python, PyTorch, PyTorch Lightning, NumPy, Pandas, Matplotlib, TensorFlow/Keras, Weights & Biases, Slurm, dnadb
  Role: Lead researcher & first author

  • Designed the first foundation model architecture for high-throughput sequencing (HTS), trained on many millions of DNA sequences from ~280k 16S rRNA samples across 970 studies
  • Built a custom container format (dnadb) for fast random-access I/O and trained across 5 compute nodes orchestrated with Slurm
  • Integrated explainability via attention attribution and deployed pretrained models to Hugging Face

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• DNAGAST: Generative Adversarial Set Transformers for High-throughput Sequencing

  Transformer-based generative adversarial model for synthetic high-throughput sequencing data. Published in The International FLAIRS Conference Proceedings (2025).
  Tech: Python, PyTorch, NumPy
  Role: Lead researcher & first author

  • Created the first generative model capable of producing full, realistic high-throughput sequencing samples

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• Context Learning (cTL): A Neurobiologically-inspired Deep Learning Framework for Autonomous Context Learning

  Neurobiologically inspired deep learning framework enabling autonomous context inference and task switching in both supervised and reinforcement learning settings. Published at IEEE ICTAI (2021) and winner of the Bourbakis-Ramamoorthy Best Paper Award.
  Tech: Python, PyTorch, Reinforcement Learning
  Role: Lead researcher & first author

  • Designed an architecture that dynamically infers task context without explicit context input through feedback
  • Integrated into a reinforcement learning framework with demonstrated multi-task adaptability

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Software

• Logisim Emulator

  Programmatic emulator for running Logisim circuit files, designed as a backend for automated grading systems.
  Tech: TypeScript, Node.js, Mocha, Chai, NYC, Coveralls, Travis CI
  Role: Solo developer

  • Achieved 95% unit test coverage with full CI/CD
  • Correctly emulates digital logic circuits assembled through Logisim

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• jimmiegreid.com

  Full-stack SPA portfolio website for a master carpenter based in Atlanta, GA.
  Tech: Laravel, PHP, MySQL, Vue.js, JavaScript, Sass
  Role: Full-stack developer

  • Designed MySQL-backed CMS and dynamic Vue.js frontend
  • Built responsive SPA with clean visuals and integrated contact form

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• StarBlasters

  2D cooperative arcade game inspired by Asteroids, created at a hackathon. Awarded 2nd place in the Hacker’s Choice Award.
  Tech: Python, Pygame
  Role: Team lead & co-developer

  • Led a team of 5, designing the software architecture and orchestrating development
  • Implemented the game engine logic and integrated custom art

Publications

• SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing

  David W. Ludwig II, Christopher Guptil, Nicholas R. Alexander, Kateryna Zhalnina, Edi M.-L. Wipf, Albina Khasanova, Nicholas A. Barber, Wesley Swingley, Donald M. Walker, Joshua L. Phillips
  Bioinformatics (Vol. 41, No. 7), Jul 2025
  DOI: 10.1093/bioinformatics/btaf370

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• DNAGAST: Generative Adversarial Set Transformers for High-throughput Sequencing

  David W. Ludwig II, Joshua L. Phillips
  The International FLAIRS Conference Proceedings (Vol. 38), May 2025
  DOI: 10.32473/flairs.38.1.139043

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• Leveraging Fine-Scale Variation and Heterogeneity of the Wetland Soil Microbiome to Predict Nutrient Flux on the Landscape

  N. Reed Alexander, Robert S. Brown, Shrijana Duwadi, Spencer G. Womble, David W. Ludwig II, Kylie C. Moe, Justin N. Murdock, Joshua L. Phillips, Allison M. Veach, Donald M. Walker
  Microbial Ecology (Vol. 88, No. 1), Apr 2025
  DOI: 10.1007/s00248-025-02516-1

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• Effects of snake fungal disease (ophidiomycosis) on the skin microbiome across two major experimental scales

  Alexander S. Romer, Matthew Grisnik, Jason W. Dallas, William Sutton, Christopher M. Murray, Rebecca H. Hardman, Tom Blanchard, Ryan J. Hanscom, Rulon W. Clark, Cody Godwin, N. Reed Alexander, Kylie C. Moe, Vincent A. Cobb, Jesse Eaker, Rob Colvin, Dustin Thames, Chris Ogle, Josh Campbell, Carlin Frost, Rachel L. Brubaker, Shawn D. Snyder, Alexander J. Rurik, Chloe E. Cummins, David W. Ludwig II, Joshua L. Phillips, Donald M. Walker
  Conservation Biology (Vol. 39, No. 2), Nov 2024
  DOI: 10.1111/cobi.14411

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• STrXL: Approximating Permutation Invariance/Equivariance to Model Arbitrary Cardinality Sets

  Kendra Givens, David W. Ludwig II, Joshua L. Phillips
  The International FLAIRS Conference Proceedings (Vol. 37), May 2024
  DOI: 10.32473/flairs.37.1.135568

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• A Neurobiologically-inspired Deep Learning Framework for Autonomous Context Learning

  David W. Ludwig II, Lucas W. Remedios, Joshua L. Phillips
  IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), Nov 2021
  Winner of the The Bourbakis-Ramamoorthy Best Paper Award
  DOI: 10.1109/ICTAI52525.2021.00022

Invited Talks & Presentations

• David W. Ludwig II and Joshua L. Phillips (May 23, 2025)
  DNAGAST: Generative Adversarial Set Transformers for High-throughput Sequencing
  Presented at The International FLAIRS Conference (Vol. 38), Daytona Beach, FL

• David W. Ludwig II (Feb 10, 2025)
  Building Explainable AI for High-Throughput Sequencing and Cross-Domain Integration
  Invited talk at Oak Ridge National Laboratory

• David W. Ludwig II and Joshua L. Phillips (Nov 9, 2022)
  Building AI for Big Data Biology: Computational Models for High-throughput Sequencing Data
  Invited talk at Cumberland University, Lebanon, TN

• David W. Ludwig II, Lucas W. Remedios, and Joshua L. Phillips (Nov 1, 2021)
  A Neurobiologically-inspired Deep Learning Framework for Autonomous Context Learning
  Presented at the IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI 2021)

Awards

• MT Hack (Hackathon): 2nd Place (2024), 3rd Place (2023)
• The Bourbakis-Ramamoorthy Best Paper Award — IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), 2021
• Paul Hutcheson Outstanding Graduate Scholarship (2019)
• International Collegiate Programming Contest Mid-Central USA Regional: 3rd Place (2017)