• Ph.D. (Mechanical Engineering), Northwestern University, 2012
  • M.S. (Mechanical Engineering), The University of Akron, 2008
  • B.S. (Mechanical Engineering), The University of Akron, 2005


Prof. Stebner works at the intersection of manufacturing, machine learning, materials, and mechanics. Prof. Stebner joined the Georgia Tech faculty as an Associate Professor of Mechanical Engineering and Materials Science and Engineering in 2020. Previously, he was the Rowlinson Associate Professor of Mechanical Engineering and Materials Science at the Colorado School of Mines (2013 – 2020), a postdoctoral scholar at the Graduate Aerospace Laboratories of the California Institute of Technology (2012 – 2013), a Lecturer in the Segal Design Institute at Northwestern University (2009 – 2012), a Research Scientist at Telezygology Inc. establishing manufacturing and “internet of things” technologies for shape memory alloy-secured latching devices (2008-2009), a Research Fellow at the NASA Glenn Research Center developing smart materials technologies for morphing aircraft structures (2006 – 2008), and a Mechanical Engineer at the Electric Device Corporation in Canfield, OH developing manufacturing and automation technologies for the circuit breaker industry (1995 – 2000).


  • Mechanics, Manufacturing, Machine Learning, Materials: additive manufacturing, micromechanics, alloys, composites, process-structure-property relationships, X-ray diffraction and tomography, neutron diffraction, data informatics, continuum thermodynamics

Prof. Stebner is known for cross-disciplinary work with a mechanical engineering core, such as developing new characterization and data analysis capabilities for studying deformation and manufacturing of materials in situ, and integrating data informatics and machine learning to accelerate discovery, development, and optimization of mechanics models and manufacturing processes. He is also known for incorporating the latest fundamental scientific discoveries into practical, usable tools for innovating engineering applications for companies and the government. His current research program has 3 thrusts:

  1. One of Stebner’s research mottoes in the field of additive manufacturing is “we solve data problems and we solve problems with data.” This includes developing machine learning platforms for part and process qualification, optimizing new materials for AM, and developing optimized process parameters for unique processes and raw materials. We also develop new data management platforms and high-throughput characterization capabilities to facilitate data quality and curation.
  2. Stebner’s “specialty” in mechanics is elucidating how low-symmetry and heterogeneous structures give rise to unusual thermomechanical behaviors. We transition that knowledge into predictive tools for industry to use in designing better materials, components, and manufacturing processes. These tools include new analysis software for studying mechanics, physics-based models of structure-property relationships spanning length scales from nanometers to meters, and also machine learning tools for statistically driven process-structure-property discovery, optimization and design.
  3.  While most of Stebner’s projects involve materials, in the field of shape memory alloys (SMAs), Stebner works on engineering new materials. Active thrusts in this area include development of new SMAs for additive manufacturing, tribology applications, morphing aircraft structures, and biomedical implants. These programs span basic research from the origins of hysteresis in materials to the applied optimization of compositions and processing of new materials for applications such as International Space Station ball bearings and artificial heart valve frames.

Representative Publications