Working with Machine Learning often requires large complex data sets. Understanding these data sets and the results of applied Machine Learning requires a more visual, and sometimes spatial, approach than traditional numbers can provide.
My research is designed to explore methods for visualizing the results of applied machine learning.
Machine Learning in Visual Sentiment Analytics
One area of my research focuses on how to integrate Machine Learning with Sentiment Analysis to process, classify and visualize data for more meaningful analytics. By combining traditional Machine Learning techniques with advanced visualization techniques, users and consumers can more easily understand the implications of the data.
Visualizing data helps researchers to more easily understand and discuss results. Visualization of data also makes data more accessible to people outside of research.
I hope my research into the combination of Machine Learning and Visual analytics will help make data–once considered abstract and inaccessible–more clear and accessible.
Collaboration
While I enjoy research for the pursuit of knowledge, one of my favorite aspects of researching at PFW is the ability to provide students with collaborative research opportunities.
The desire of students to explore the frontier of knowledge and contribute their own ideas is a large contributor to my motivation to continue work in research.