Cheng Zeng

Cheng Zeng is an Experiential AI postdoctoral fellow working with the Engineering team at the Roux institute of Northeastern University, co-mentored by Nathan Post and Jack Lesko. His research lies at the intersection of materials science, data science and additive manufacturing.His research at Roux focuses on materials design using first-principles calculations and machine learning force fields. Other works include data analytic approaches in additive manufacturing and wind energy prediction.

Prior to joining Roux, he received a PhD in Engineering at Brown University. En route to his PhD degree, he also received a secondary Master’s degree in Data Science via Brown’s Open Graduate Education Program. At Brown, he worked at the Catalyst Design Lab, and his PhD thesis was focused on the development of machine learning methods and phenomenological models to accelerate rational catalyst design. A key highlight of his work is that a combination of developed machine learning and physical models pinpoints the nanoparticle alloys with the best possible electrochemical catalysis (e.g. Co–Pt nanoparticles for oxygen reduction reaction). Prior to Brown, he got a Bachelor and Master in Materials Science and Engineering from Tsinghua University. At Tsinghua, he worked on electrochemical methods for making oxide films resistant to hydrogen permeation.

Ph.D. Area: Chemical Engineering

Lead Mentor: Jack Lesko

Project Name: Data Analytic Approaches to Additive Manufacturing Build Quality Control and Quantification