More Projects

As a small group, we were tasked with creating a hyper-personal object for a person we hardly knew. We found Ed. His life was micromanaged and completely scheduled around school and his home life. Fitness was his way of destressing. His peers began asking for personalized workout sessions. What began as a one-on-one workout affair grew into something more, an intense weekly program for other graduate school students. However, Ed found a lack of motivation in many of his attendees. Three interviews and a workout session later, we created a giant timeclock for Ed. It would create a benchmark of sucess for his entire class.

It is well documented that emojis and emoticons can express meaning in our texting and social media posting in ways that simple words cannot. Why do some texts contain emojis and why do others not. Do emojis add semantic value? Can we accurately extract sentiment? These questions are all pertinent to our project's goal--to create an emoji tool that can assign the appropriate emoji to a given piece of text. We stripped all emojis from each document in our training set (ie, nearly 1 Million tweets from 2013) and treated them as our target labels. If our predictions proved accurate enough, we would pursue further work into inserting emojis into larger corpuses.

San Francisco experiences no shortage of crime. A mixture of growing social and economic inequalities, housing disparity, and urban complexity all add to this unique problem. The City of San Francisco, in an effort to gain insight into and fight against crime, has solicited public assistance by publishing nearly 12 years of crime reports onto SFOpenData and developing a Kaggle competion with now nearly 900 teams across the country. Our goal was to predict the category of crime that would occur given a location and time. We are currently ranked in the top 50 of nearly 900 other kaggle contestants.

Our team was given the freedom to build a RESTful web application using current web technologies. We were all motivated by the idea of creating a platform that integrated mobility in a city with fast querying abilities. After deliberation on how to scope our project, we decided to build a tool that queried for food options. Our team developed the "Find and Ride" app which enables users to locate nearby restaurants and/or Yelp­listed establishments and provides taxi transportation prices (Uber) to those destinations via a map­based (Google) interface.

Happy2Help was formulated to transcend beyond the recreational motives of a tetraplegic individual, instead aiming to dig deep and locate their fundamental requirement - to gain a meaningful identity within their community. While virtually experiencing fun activities like scaling mountains would provide them with temporary satisfaction, providing them jobs that utilize their assets would provide them with permanent empowerment.