Dr. Yangyang Xu, assistant professor of mathematical sciences at Rensselaer Polytechnic Institute, has received a $250,000 grant from the National Science Foundation (NSF) to research challenges associated with distributed big data in machine learning. 

Machine learning algorithms allow computers to make decisions, predictions, and recommendations based on input training data without being explicitly told what information to look for in the data. This technique has been broadly used ever since data mining was envisioned, but its potential has not been fully realized yet. For instance, marketers use machine learning to provide shoppers with product recommendations, photo apps use it for facial recognition, and mapping and traffic apps use it to estimate commute times, but identifying highly complex relationships requires much more data and computing power.

Deep learning is machine learning on a larger scale, involving the input of massive amounts of data and the formulation of increasingly complex predictions. With vast amounts of data, the use of multiple networked computers is necessary: a distributed system. However, computational and mathematical challenges arise. Xu and his team, which includes undergraduate and graduate students, will use the NSF grant to address some of these challenges. 

Simply put, Xu’s team will develop groundbreaking algorithms that allow multiple computers to work efficiently together as one. They will also focus on maintaining the security of distributed personal information, and on methods to improve the speed and accuracy of deep learning. Decentralized algorithms will also be developed for solving optimization problems containing conditions restricting the behavior of the intelligent agents involved.

“The main goal is to design optimization algorithms that have fast convergence and low communication cost for solving large-scale distributed machine learning problems,” said Xu. “A few stochastic gradient-type methods will be designed for solving a few classes of problems by exploiting their structures. These algorithms will incorporate several features including acceleration technique to have fast convergence, compression technique to have efficient communication, and asynchronous computing to have high parallelization speed up.”

“Dr. Xu’s research will not only advance the scope and applicability of large-scale machine learning technology, but it will offer exceptional opportunities for Rensselaer’s graduate and undergraduate students,” said Curt M. Breneman, Dean of the School of Science. “At Rensselaer, undergraduate students are offered hands-on, project-based research opportunities early on in collaboration with seasoned graduate students and faculty, and this experience makes all the difference in terms of their big-picture thinking and future employability. Through this grant, Dr. Xu’s students will be able to contribute to a widely used, cutting-edge technology.”