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Research in areas ranging from visual analytics and self-organizing particle systems to machine learning and computational genetics has earned some international recognition for ASU students Alexandra Porter and Rolando Garcia.

They are among 37 undergraduate students from colleges and universities throughout North America to recently receive accolades from the Computing Research Association for demonstrating outstanding computing research skills.

The association promotes undergraduate research, provides resources to faculty to help them give students research training and encourages undergraduates to pursue graduate education and research careers in computing fields.

Porter is a senior in ASU’s Barrett, The Honors College, majoring in computer science and mathematics, with a minor in music performance.

She was a finalist for one of the top honors bestowed by the Computing Research Association’s 2017 Undergraduate Research Awards program in the category for women at institutions that grant doctoral degrees.

Garcia is a senior majoring in computer science. He earned an honorable mention in the men’s category.

computing research

Alexandra Porter

Both Porter and Garcia have been involved in work in research labs directed by faculty members in the School of Computing, Informatics, and Decision Systems Engineering.

Each has had support for their projects from the Fulton Undergraduate Research Initiative.

Porter’s area of interest is theoretical computer science, specifically the design, analysis and engineering of algorithms.

She worked on a nutrition visualization research project with Ross Maciejewski, an assistant professor of computer science, in his Visual Analytics and Data Exploration Research Lab. She created an Android app that enables users to record what they eat and then visualize data about their diets.

The project gave Porter the opportunity to coauthor a research paper titled “A Survey of Personal Nutrition in mHealth Nutrition Apps” that was presented at the international Person Visualization Workshop organized by the Institute of Electrical and Electronic Engineers.

She is currently working with Andrea Richa, a professor of computer science, on a project involving self-organizing particle systems.

The research focuses on particle systems that can self-organize to solve designated tasks without needing a central control mechanism.

The technique can be used for coating objects for purposes of monitoring and repairing the objects, for forming nanoscale devices used for surgery and for making molecular-scale electronic structures.

Porter coauthored a research paper based on this research that was published in the proceedings of the International Conference on DNA Computing and Molecular Programming.

computing research

Rolando Garcia

She also worked with Umit Ogras, an assistant professor in the School of Electrical, Computer and Energy Engineering, to create a simulation of an internet-connected self-driving wheelchair.

Porter says these varied accomplishments have made her confident that she is well prepared for her pursuit of a doctoral degree in computer science and a career in research.

Garcia is currently a research aide in Maciejewski’s Visual Analytics and Data Exploration Research Lab. He has been involved in developing technologies to help people understand and work effectively with large sets of data.

He has presented findings from his work at two national research symposiums and at an Institute of Electrical and Electronics Engineers workshop. For the workshop presentation, his research team earned the Visual Analytics Science and Technology Grand Challenge Award for Outstanding Comprehensive Submission.

Last summer Garcia was a research intern for the Computational Genetics Laboratory at the Institute of Biomedical Informatics at the University of Pennsylvania. He worked on projects involving technologies used in Deep Learning architectures. Deep Learning is one of a set of machine learning methods based on representations of data.

“My long-term research goal involves studying data structures for achieving fast machine learning at bigger scales,” Garcia says. “The aim is to find out how a computer could become capable of organizing and accessing information so that it can be at least as intelligent as we are.”