With over a decade of experience as a full-time software engineer and a background in computer
science, I've always enjoyed building solutions for complex problems. I founded LyraText in 2024 to combine my NLP research with my software expertise to help other researchers and businesses
solve the difficult challenges found in analyzing narrative data.
I was previously on the business school faculty of the accounting departments at Arizona State
University and the University of Connecticut, along with a visiting position at the University
of Florida. My research focuses on what we can learn from language in corporate disclosures
beyond what’s obvious to a human reader and how information about one company can tell us more
about other companies.
Research Interests
Corporate Disclosures
Natural Language Processing
Computational Linquistics
Artificial Intelligence
Deep Learning
Education
- Ph.D. in Business Administration
- University of Florida
- Master of Business Administration
- University of North Florida
- B.S. in Computer Science
- University of North Florida
Publications
Expertise
I have around 20 years’ experience as a software engineer, designing and building a wide
variety of object-oriented libraries, databases, end-user applications, and back-end
systems. Most of my software development work now is in direct support of other researchers
through my company, LyraText.
Programming Languages
| Python | Especially Polars, spaCy, PyTorch, Pandas, and scikit-learn; nearly all of my machine learning/AI work is in Python |
| SQL | Extensive database development experience; in recent years pretty equally divided between SQL and noSQL backends, often PostgreSQL and MongoDB |
| R | I use the tidyverse where possible; most of my R development is for ad hoc statistical analysis and data visualization |
| JavaScript | Primarily to support web frontends; preferred libraries/toolsets are Svelte, SvelteKit, and Tailwind CSS |
General Data Science
| Analytical Approach | Deep learning, machine learning; traditional statistics and econometrics |
| Data Transformation | Processing, transforming, and merging large, semi-structured data; full extract-transform-load (ETL) process; I generally prefer Polars to Pandas for dataframe work |
| Data Types | Accounting, financial, operational, and natural language |
| Statistical Tools | Primarily R for traditional statistics; Python for deep/machine learning; Stata occasionally |
| Data Visualization | ggplot2 in R; matplotlib in Python |
Development Environment
| Operating System | I have extensive Linux server administration experience and prefer Linux for both server and desktop use; I regularly use Fedora, Ubuntu, and Pop!_OS |
| IDE | Neovim for everything possible |