Controlling Power

If generators could learn to preserve energy on their own, people wouldn't have to worry about losing power during hurricanes or bad storms, said Zhen Ni, Ph.D., assistant professor in the College of Engineering and Computer Science.

Ni uses artificial intelligence (AI) to train machines into learning specific patterns from previous disaster experiences to take action independently. For this work, he recently earned a $500,105 National Science Foundation (NSF) CAREER grant to create a new reinforcement learning framework that allows self-sufficient energy systems to handle disturbances.

For more than a decade, Ni's work revolved around the mechanics and principles in machines as well as reinforcement learning, which is machine learning where intelligent agents are required to act in an environment to earn rewards.

In 2010, he earned his bachelor's degree in control science and engineering from the Huazhong University of Science and Technology in Wuhan, China. Five years later he earned his doctoral degree in electrical, computer and biomedical engineering from the University of Rhode Island. After that, he took his first research position working in the department of electrical engineering and computer science at South Dakota State University.

That same year, Ni came to FAU to continue his research in computational intelligence and reinforcement learning. Now, as a principal investigator on multiple NSF grants, Ni plans to advance his research using AI machines to ensure people have power during severe disasters like hurricane season. Ni's research was also honored for other awards such as the Ralph E. Powe Junior Faculty Enhancement Award, the International Neural Networks Society award and the Aharon Katzir Young Investigator award.

"The unique aspect about this research," Ni said, "is that it has capabilities to reach a wide range of research disciplines including transportation, rehabilitation and robotics, while pushing forward the frontier in knowledge."