I’m an assistant professor in the Accounting Department at the University of Connecticut. My research has thus far primarily focused 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 similar companies. While my past papers have used various types of machine learning and artificial intelligence approaches, more current projects move even more in the direction of data science and how it can be used to better understand and evaluate companies. You can find more information on my CV and LinkedIn page.
In my downtime, I enjoy photography, playing acoustic guitar, and anything outdoors.
Ph.D. in Business Administration, 2012
University of Florida
Master of Business Administration, 2007
University of North Florida
B.S. in Computer Science, 1997
University of North Florida
In Spring 2019, I will be teaching the following courses at the University of Connecticut:
I have previously taught the following courses:
I have around 20 years’ experience as a software developer, designing and building a wide variety of object-oriented libraries, databases, end-user applications, and back-end systems. Most of my work now is in direct support of my accounting research and data science activities.
Python | Especially Pandas, SciPy, and NumPy; around 75% of my machine learning/AI work is in Python, the rest is in R |
R | I use the tidyverse approach where possible; most of my R development is for statistical analysis and data visualization |
SQL | Extensive database development experience; in recent years pretty equally divided between traditional SQL backends and noSQL data stores |
JavaScript | Primarily to support web frontends |
C | For high-performance operations, less often for systems development |
Perl | Extensive use in earlier years, but less often now |
XML, JSON | Data representation |
HTML, CSS | Web development |
Analytical Approach | Machine learning and artificial intelligence; traditional statistics and econometrics |
Data Transformation | Processing, transforming, and merging large, semi-structured data; full extract-transform-load (ETL) process |
Data Types | Accounting, financial, operational, and natural language (NLP) |
Statistical Tools | Primarily R for traditional statistics; Python for machine learning; Stata occasionally |
Data Visualization | ggplot2 when possible; matplotlib when in Python |
Operating System | I feel most comfortable in a Linux environment, although I also use Windows regularly |
IDE | I use RStudio when coding R, vim for everything else |