Hi, I’m Ciabhan Connelly.

I’m an undergraduate researcher at Indiana University. My fields of interest are Political Science, Computer Science and Informatics, and Data Science. In particular, I’m interested in technological and data driven approaches to humanities subjects, with a focus on world politics.

The programming languages that I am proficient in are Lisp (Racket, specifically, but the dialects translate well), Java, and Javascript/HTML. I also have experience using R, Weka, and Excel for statistics/data processing tasks. I have taken programming classes at the college level, and have worked on collaborative programming projects. I know about commenting etiquette and how to code in a team.

In the summer of 2015 I worked with Dr. L. Jean Camp on a project involving the qualitative analysis of survey responses in order to determine what defines expertise in computer security.

In 2016 I worked with Dr. Sriraam Natarajan and Devendra Dhami on applying a machine learning approach to analysis of posts on medical forums in order to predict drug interactions that are not covered extensively in existing literature.

I am currently working with Dr. Patrick Shih and Fernando Maestre on adapting the ARC method to the population of individuals living with HIV. We are designing around the challenges that come with the population (in particular, getting people in a stigmatized group to feel comfortable opening up) in order to ultimately work on co-designing technology to support their needs. In addition, I am applying a machine learning approach to posts on HIV support forums, attempting to emulate qualitative codes across a large number of posts in order identify relationships between tactics in seeking support and the response received.

I enjoy writing both creatively and academically, and spend much of my free time developing those skills. I am working my way through a novel as I get the time to write. I am also very passionate about research. When I’m not working on classes, or projects that I’m hired for, I have slowly been working out my approach for applying machine learning to the text of speeches of Cold War leaders to determine if linguistic themes can predict rises and falls in tension before they occur (where there isn’t a causal relationship between the content of the speech and a change in tension).