The Machine Learning Center at Georgia Tech (ML@GT) is home to many talented students from across campus, representing all six of Georgia Tech's colleges and the Georgia Tech Research Institute (GTRI). These students have diverse backgrounds and a wide variety of interests both inside and outside of the classroom. Today, we'd like you to meet Lara Martin, a fifth-year Ph.D. student who is interested in teaching artificial intelligence agents to tell interesting and coherent stories. Tell us about your research interests. Where might people be impacted them in everyday life?

With "Eastward," the Shanghai-based developer Pixpil set out to make a Zelda-like game inspired by the Japanese role-playing game series EarthBound, and after more than five years of development, the quirky world set in a beautiful, retro 16-bit art style delivers on every stylistic inspiration. But the plot driving "Eastward" is confusing and drags in a few of the game's eight chapters. Some characters are complex and interesting; others are uncomfortable cliches -- like Jasper, the flamboyant one-man entertainer who's terrified of world around him. The result is a beautiful game with a lot of heart but no clear, coherent storyline and a few disappointing characters.

Sequencing is a task for children that aims to improve understanding of the temporal occurrence in a sequence of events, so that they sort various images (sometimes with captions) into a coherent story. Researchers from Virginia Tech and TTI Chicago have proposed the task of machine-learning sequencing – given a jumbled set of aligned image-caption pairs that belong to a story, the task is that the computer needs to sort them in a way that they form a consistent story. They have used stories from the Sequential Image Narrative Dataset where a set of 5 aligned image-caption pairs together form a coherent story, and given a jumbled input story, they have trained machine-learning models to sort them. They have proposed a task of visual story sequencing and implemented two approaches to solve the task. The first one is based on individual story elements to predict the position, and the other one is based on the pairwise story elements to predict the relative order of the story elements.