Expertise: Statistical machine learning, deep learning and theoretical computer science

Yingzhen Yang became interested in engineering when he implemented fast-searching algorithms as a high school student.

“I chose to perform research in machine learning and deep learning because they are very interesting areas that combine the best of statistics and multiple areas of fundamental mathematics, physics and computer science,” says Yang.

He sees machine learning and deep learning as “the driving force for modern artificial intelligence,” he says.

“I want to teach courses in these areas because I hope more students will learn the basics and even pursue careers in these areas,” says Yang. “Machine learning and artificial intelligence are particularly important in science and engineering nowadays.”

At ASU, Yang will research principles and applications of deep learning, especially optimization and generalization of deep neural networks and compression of deep neural networks.

Research to advance the optimization and generalization of deep neural networks is fundamentally important to solving problems in deep learning, and can significantly enhance the understanding of deep neural networks and facilitate their broader application in various scenarios.

“I will also research AutoML, a hot topic in the deep learning community that automatically searches for optimal neural architecture and eliminates the need for designing neural architecture manually for each task,” says Yang. “AutoML models are being incorporated into my research in compression of deep neural networks.”

“I am deeply impressed by ASU’s strong commitment to research and innovation,” says Yang. “It is a great university for me to advance my research career.”

In his spare time, Yang loves traveling and playing badminton.