For NYU Abu Dhabi junior Juan Felipe Beltran, presenting at the 2013 International Conference on Pattern Recognition Applications and Methods (ICPRAM) in Barcelona last month provided a glimpse into a possible post-NYUAD academic path. As the Computer Science major said, "While still determining my future career, doing research in different fields is definitely giving me the information I need to slowly narrow it down. At the moment, machine learning and pattern recognition does seem like a fantastic and rich field to work in."

Beltran, who presented at the four-day conference with fellow NYUAD junior Xiaohua Liu and NYUAD Research Professor of Computer Science Godfried Toussaint, co-authored the accepted paper with Liu, Godfried, and NYUAD junior Nishant Mohanchandra. Written during the students' Artificial Intelligence course (taught by Toussaint), the paper was penned with the aim of submitting it to a conference, as well as an academic journal.

Along with students from roughly 100 other institutions, including MIT, Trinity College, the National Institute for Advanced Industrial Science and Technology, and Ain Shams University, the NYUAD team shared its ideas about new methods and applications for pattern recognition. Beltran, Liu, and Toussaint presented "Measuring Musical Rhythm Similarity — Statistical Features versus Transformation Methods," which analyzes rhythms through both structural and statistical features by placing them in a cyclic set. By analyzing through various methods how similar two different rhythms are to each other, the team hopes to better understand the inner workings of the human brain. "Studying musical rhythm analysis opens doors into understanding how our brains work," explained Beltran. "By figuring out the algorithm that seems to be used by our brains when processing music, we can get clues about the architecture behind the curtain."

For Beltran, this research combined his academic interests with personal pursuits. "At the end of the day, it personally allows me to practice computer science without leaving my interests in music behind," he said. "It's a great way to really get a look at how multidisciplinary computer science tends to be." Indeed, in speaking with other conference participants about their presentations, the NYUAD team learned about projects ranging from robots that could follow an individual around a city to a better way to track blood trajectories for forensics.

The conference experience brought clarity to Beltran and Liu, who received numerous questions about their presentation from interested researchers. "We are definitely ready for graduate school and the research that comes with it," said Beltran. "Being able to have these kinds of experiences allows us to realize that there is no giant wall between researchers and students — that we can take a subject we are interested in and try to find something new."