PNNL

Tegan Emerson was invited to be one of two plenary speakers at the first World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022), April 3–6. The event brought together stakeholders in a variety of academic, industrial, and government materials and manufacturing arenas to tackle central challenges and help pave the way for artificial intelligence in materials and materials-related manufacturing.

AIM 2022 was organized by The Minerals, Metals & Materials Society, a professional organization connecting scientists and engineers from around the world through conferences, publications, and other efforts. Emerson's talk, “Generating Realistic Material Microstructures Using Conditional GANs for Advanced Manufacturing,” was based on her team's work supported by Pacific Northwest National Laboratory's (PNNL's) Mathematics for Artificial Reasoning in Science (MARS) initiative.

Emerson, a senior data scientist in the National Security Directorate at PNNL, has a background in geometric and topological data analysis, including investigating deep learning and data science through a mathematical lens. She also leads PNNL's Mathematics of Data Science Dragontail team. Her research includes work at the intersection of topology, algebra, and geometry in data science with deep learning and artificial intelligence. She has explored applications in areas such as hyperspectral imaging, overhead image analysis, models of atmospheric turbulence, and now materials science.

In addition, Emerson is a lead researcher on the Artificial Intelligence Tools for Advanced Manufacturing Processes project alongside Henry Kvinge and Keerti Kappagantula.

“Being invited as a plenary speaker at AIM 2022 was very exciting, and I'm honored to shine a light on the achievements of our diverse and cross-disciplinary team,” says Emerson. “Despite being only a little over a year into this project, the team has produced more than a half dozen high-caliber, high-impact papers and taken steps to guide future experimentation in advanced manufacturing at PNNL.”

In addition to Emerson, the PNNL research team who contributed to the research highlighted in the plenary talk includes Scott Howland, Lara Kassab, Henry Kvinge, and Keerti Kappagantula. The team worked with data generated by Nicole Overman, Scott Whalen, Xiaolong Ma, Joshua Silverstein, Md. Reza-E-Rabby, Tianhao Wang, WoongJo Choi, and Scott Taysom.