Equal Parts
Data Scientist | Software Engineer | Behavior Analyst
As a data scientist, I am passionate about creating value for organizations by analyzing and interpreting data, developing ETL/ELT pipelines, and using ML and AI to create predictive models. As a software engineer, I am passionate about bringing the best tools to the hands of behavior analysts and empowering people so they can make a positive impact on the world.
Project description: I used python to download and analyze over 10,000 unique journal articles from applied behavior analytic journals. Once downloaded, I prepared them for analysis by creating text documents from the PDFs. Once the data were in text format, I made a corpus of journal articles using natural language processing techniques. With this corpus, I analyzed the data to explore the prevalence of different words throughout the history of the journals, the various topics that the journals covered, and compared the journals to other peer-reviewed publications. You can find a link to the project here.
Packages Used for ProjectProject description: After analyzing open-text publications, I was curious to explore the network that makes up scientific publications. So, I created a data pipeline to gather all citations from published open-source articles and matched them to all other publications within the corpus. I then reviewed different centrality measures across all publications within the corpus. You can find a link to the project here.
Packages Used for ProjectProject description: One of my passions is to make science projects more accessible for everyone. Advancements in modern technology and the availability of technology have made backyard science more accessible than ever. In this project, I use readily available technology to create machine learning models to detect the friendly critters in my backyard, specifically squirrels. For example, a squirrel detection model to identify a squirrel's x and Y coordinates can unlock opportunities to run behavioral experiments involving food delivery, mazes, and other fun and exciting scientific adventures. In addition, automating the data collected from the experiments allows quick replication across species. This project is in its infancy stages, but I will present my findings at ABAI's 49th annual conference in Denver.
Packages Used for Project
Project description: I created a topic model for over 10,000 journal articles and used a dimensionality reduction technique called T-distributed Stochastic Neighbourhood Embedding (tSNE). Check out the final product here.
Packages Used for Project
I created a simple client side application to highlight how easy tailwind makes frontend. Explore it here.
Tech Used For Project