Hey! My name is Meghan, and I am currently an MS-HCI student at Georgia Tech looking for a Summer 2018 UX internship.
How did I find myself at Georgia Tech studying Human Computer Interaction? I have always loved Math and logical thinking, and studied Mathematics for my undergraduate degree at the University of Massachusetts, Boston. During my studies I really enjoyed 'designing' logical structures, statistics and probability theory. During my final semester, I did an independent study of Bayesian Data Analysis, after learning the theory behind it (Bayes' Theorem), and believing Bayesian Inference was a more real world approach to statistical inference.
After graduation, I found myself at a crossroads. Should I continue further in graduate school with Mathematics, or should I look for work? I decided on the latter, and found a job as an interface designer at a company called New England Survey Systems in Brookline, Massachusetts.
At New England Survey Systems, I worked on a product called NEForm, a customizable e-form and workflow application. I was the lead designer on projects that were mostly in the clinical trial and research space. I would design the layout of the forms, and design and implement the workflow and page triggers. I also helped evolve our in-house design system to make it more user-friendly to new hires and in hopes that eventually clients could design using it.
Finally, I was introduced to the field of human computer interaction (HCI) and knew right away that was the direction I wanted to head. I wanted to deepen my skills in user experience, learn new research methods, and also do research in hot HCI fields like AR/VR and data visualization. Thankfully my hard work payed off and I got accepted to Georgia Tech, and am in the place I want to be!
At Georgia Tech I have worked on three team projects, and a solo data visualization project (see my portfolio). My plan for this semester is to continue my research project on visualizing uncertainty in data sets, and further develop my skills in qualitative research methods.