Recent graduate spotlight: Ruby Jiang

As a graduate student in the materials science and engineering department, Ruby Jiang, M.S. ’18, Ph.D. ‘23 maximized opportunities to foster interdisciplinary connections. Having earned her undergraduate degree in materials science in China, she knew that she was interested in gaining experience at a school in the United States. The reputation of Carnegie Mellon stood out to her as she researched programs. 

While initially interested in completing a master’s degree and entering the workforce, she was enticed to continue toward earning a doctorate degree through the research she was conducting under her advisor, Tony Rollett. She was introduced to the concept of using deep learning to identify defects in an additive manufacturing lab class. Studying keyhole porosity and its correlation with additive manufacturing build processes drew her to seek out more streamlined methods to analyze large amounts of image data. 

“I started to get really interested in the application of artificial intelligence and machine learning (ML) in solving materials problems,” said Jiang, who was inspired to take machine learning courses in the computer science department in addition to her courses and research in materials science. 

I could almost immediately apply the knowledge learned in those classes to my research

Ruby Jiang, CMU MSE alumna

“What was really rewarding was that I could almost immediately apply the knowledge learned in those classes to my research,” she said. 

After completing her doctoral degree, Ruby went on to a postdoctoral position at the Lawrence Berkeley National Laboratory, where she continued her work connecting ML to materials science research. Fostering this connection has led Ruby to her current role as a research scientist at Meta, where she is working on optimizing the Threads application to draw new users to the platform by improving recommendations of relevant content.