UC Berkeley MIDS Graduate
MIDS Capstone: mic-check.ai
mic-check.ai leverages machine learning techniques to build user profiles across a variety of major metropolitan areas with the goal of augmenting the research and design phase of social good programs. By compressing the Twitter graph into community-oriented user networks, this application provides program managers with a snapshot overview of how social issues are currently impacting communities across the United States.
| Web App | Final Presentation Slides | Video Demo |
Career Coach was my Applied Machine Learning course’s final project where my partner and I developed a recommendation system using unsupervised and semi-supervised learning methods to recommend new student questions to the professionals who were most likely to answer them.
An independent side project I worked on with a study group during my MIDS term where we each picked a dataset to analyze and a machine learning algorithm to apply.
Now that I’m graduated, I’m taking my data science knowledge and building a web app that will help people learn how to read and interpret tarot spreads. The core product, named Imaginary Friend, combines question answering and abstractive summarization to generate likely interpretations of tarot spreads for learners to check their answers against.