Biography
Dr. Joshua C. Agar is an Assistant Professor in the Department of Mechanical Engineering and Mechanics at Drexel University. Prior to Drexel, Joshua was an Assistant Professor in the Department of Materials Science and Engineering at Lehigh University. Joshua earned a BS from the University of Illinois at Urbana-Champaign, an MS from the Georgia Institute of Technology, and a PhD from the University of Illinois at Urbana Champaign in Materials Science and Engineering. Following his degrees, he was a postdoctoral scholar in machine learning at the University of California Berkeley.
Degrees / Education
- PhD Materials Science and Engineering, University of Illinois at Urbana Champaign, 2015
- MS Materials Science and Engineering, Georgia Institute of Technology, 2011
- BS Materials Science and Engineering, University of Illinois at Urbana Champaign, 2009
Research Areas
Research Interests
He has broad research interests spanning synthesis and characterization of multifunctional materials, multimodal characterization and spectroscopy techniques, physics-informed and constrained machine learning methods, and codesign of machine learning models for real-time machine learning on heterogeneous computing. He applies these techniques to design, understand, and fabricate functional materials with applications in sensing, energy conversion, and computing.
Select Publications
- Qin, S., Guo, Y., Kaliyev, A. T. & Agar, J. C. Why it is Unfortunate that Linear Machine Learning Models ‘Work’ so well in Electromechanical Switching of Ferroelectric Thin Films. Adv. Mater. e2202814 (2022) doi:10.1002/adma.202202814
- Deiana, A. M. et al. Applications and Techniques for Fast Machine Learning in Science. Front Big Data 5, 787421 (2022)
- Nguyen, T. N. M. et al. Symmetry-aware recursive image similarity exploration for materials microscopy. npj Computational Materials 7, 1–14 (2021)
- Agar, J. C. et al. Revealing ferroelectric switching character using deep recurrent neural networks. Nat. Commun. 10, 4809 (2019)
- Agar, J. C. et al. Highly mobile ferroelastic domain walls in compositionally graded ferroelectric thin films. Nat. Mater. 15, 549–556 (2016)