/Ara

A social-matching app for women in tech at the same university that reduces the social barriers to befriending fellow classmates.

Primary LanguageJava

Ara

We've all been there.

You walk into a lecture hall, a conference, or a meeting ... and realize you're one of the only women in the room. That's no accident. While more women are earning STEM degrees than ever before, the proportion of women earning computer science degrees has actually decreased steadily since the 1980s. Fewer women in the field means fewer role models for younger students, heightened levels of impostor syndrome, and stronger social pressures to drop out of the major. In fact, the dropout rate for women in computer science is a whopping 41%, which is more than twice the rate it is for men at 17%.

We want to do our part to stop this cycle. That's where Ara comes in.

What is Ara?

Ara is a social networking/matching app for women in tech majors at the same university. If you've ever wanted to become study buddies with that girl in your computer science class but can't find a socially-viable way to do so, or if you "know of" girls who have gone on to work at amazing companies but never gotten the chance to know them, then Ara is the perfect app to use. Ara reduces such missed opportunities by suggesting students who are "most compatible" with the user and providing a chat functionality for exchanging personal contact information. Compatibility is scored based on similarities in coursework, specializations, desired role in mentor relationships, and many other fields.

What is Ara built on?

Ara is built with Java in Android Studio and has a Google FireBase backend.

Future Steps

  • An "Alumni" tab where graduates can put the company they're currently working for, their major, technical courses they've taken, clubs they were involved in and their contact information. Users can use the information to guide their undergraduate coursework/experience, and reach out to Alumni to ask how they got to where they are. These connections could even end in a referral/recommendation!
  • Incorporate machine learning into our user matching process once we have accumulated a large enough training dataset.
  • Enter startup pitch competitions and generate funding with the hope of deployment at Georgetown as well as other universities.