Dr. Zhantao Chen joined us this Fall as an Assistant Professor in the Walker Department of Mechanical Engineering at The University of Texas at Austin, where he leads the Group for AI in Materials Modeling and Analytics (GAMMA). Prior to joining UT Austin in 2025, Dr. Chen was a Research Associate at SLAC National Accelerator Laboratory (2022–2025). He received his Ph.D. in Mechanical Engineering from the Massachusetts Institute of Technology in 2022. Dr. Chen’s research develops artificial intelligence and machine learning methods for materials modeling, characterization, and discovery, with the vision of enabling AI-driven autonomous research platforms. His long-term goal is to integrate design, synthesis, and characterization into closed-loop, intelligent workflows that accelerate materials discovery and advance fundamental materials science.
How did you become interested in Materials Science Engineering and your topic?
I initially followed the trend and took a Machine Learning course back in 2018, but I was quickly drawn in by imagining the promising scientific applications, such as modeling mechanical behaviors of materials. That excitement led me to pursue my future work at the intersection of machine learning and materials science.
What has made the biggest impact on your career?
My PhD advisor, Prof. Mingda Li, has had a tremendous impact. I am deeply grateful for the opportunity he gave me to join his group when I was transitioning into the emerging direction of machine learning and scattering spectroscopy. I learned most of my foundational research skills from him, and more importantly, he guided me toward a direction I feel genuinely passionate about. That passion still drives me today. I am also sincerely grateful to Dr. Joshua J. Turner, my postdoctoral supervisor at SLAC. He encouraged me to explore freely while providing insightful scientific guidance, and he was always incredibly supportive of my “bold” ideas. I gained a lot of my research confidence from Josh's encouragement and support. I am deeply thankful to both of them.
What advice do you have for other researchers or graduate students?
Try to build a big-picture understanding before diving into any project. Think about the unique contribution your work can make to the community, and consider timelines, required resources, and how all the pieces fit together. Another practical piece of advice: choose your research projects wisely. Balance short-term "low-hanging fruits" with longer-term, "think-big" projects that excite you. You may not achieve the ambitious ideas anytime soon, but they will gradually shape your unique research identity—and one day, when you've grown stronger, you’ll find those ambitious goals much closer to you.
What do you enjoy doing when you're not working?
Spending a solid block of uninterrupted time writing code and testing new ideas! (Most of them eventually fail, but it’s still fun.) I also enjoy watching detective TV shows. For me, a perfect vacation is a mix of binge-coding and binge-watching TV series.