Scott Bouma
Verified Expert in Engineering
Chatbots Developer
Helena, MT, United States
Toptal member since February 25, 2016
Scott is passionate about improving and replacing expert systems using machine learning techniques (particularly NLP and CV). Do you have an existing business problem that's currently solved using a static expert system, which you'd like to augment, improve, or replace with a more intelligent solution? Scott would love to partner with you on developing that solution!
Portfolio
Experience
Availability
Preferred Environment
Windows, Linux, Node.js, C++, Scala, Java
The most amazing...
...system I've helped build used machine learning to organize photos and identify content. It could handle up to one million photos per user.
Work Experience
Bible Labs Member
Life Church
- Designed and implemented audio Bible tools for Alexa and Google Home.
- Developed NLP-based tools to improve voice and text Bible search.
- Researched novel methods of engaging users with scripture in a photo-centric culture.
Senior Software Engineer
SRI, Inc.
- Created deployable systems from research and development code.
- Led technical projects for small developer teams.
- Contributed to large and small teams on high-intensity projects.
- Provided expertise in AWS cloud deployment.
Software Engineer
VLS, Inc.
- Led a multi-year project to provide 3D feature extraction capabilities for clients.
- Applied machine learning techniques to develop feature extraction algorithms for EO and LIDAR imagery.
- Researched the use of genetic algorithms for pre-filtering image bands as a data reduction and feature enhancement technique.
- Integrated software with well-known digital image processing platforms, such as ESRI ArcGIS or ERDAS Imagine.
- Developed MVC plugins using Visual Studio C# .NET.
Experience
Program
This C# code is an example solution I developed within a few hours. The program takes a dictionary file and a text file containing potentially misspelled input words. For each input word, the program determines whether it is a valid word, suggests a similar word from the dictionary if possible, or outputs "UNKNOWN" if no suitable match is found.
The clever thing about my solution is the use of a hash map to store the dictionary. This approach ensures O(1) analysis time for any input word, avoiding the need for more time-consuming traversals of the dictionary.
To run the code, two arguments must be specified: a dictionary text file containing one word per line, for which any medium-sized English word dictionary text file will suffice, and another text file with input words that may contain duplicated letters or incorrect vowels.
Education
Master's Degree in Computer Science
University of Montana - Missoula, MT
Bachelor's Degree in Mathematics, Computer Science
Montana State University - Bozemen, MT
Skills
Libraries/APIs
Node.js, JOOQ, ArcGIS, Handlebars, jQuery
Tools
Jira, Jenkins, Git, Mercurial, Eclipse IDE, Esri, ERDAS, NGINX, IntelliJ IDEA, Visual Studio .NET, Android Studio, Artifactory, NuGet, Gradle
Languages
Scala, Java, JavaScript, C#, CSS, HTML, HTML5, Python, C++
Paradigms
REST, Continuous Integration (CI), Agile Software Development
Platforms
AWS Lambda, Amazon Alexa, Windows, MacOS, Amazon Web Services (AWS), Docker, Android, Linux, OS X
Frameworks
Play, .NET, Entity 6, Hadoop
Storage
MySQL, PostgreSQL, MongoDB, HBase
Other
Chatbots, Machine Learning, Machine Vision, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT)
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