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Education

  • Ph.D., The Pennsylvania State University, 2005
  • M.S. (Laurea), University of Padua, Italy, 2001

Teaching Interests

Professor Bassiri-Gharb’s teaching focuses at the undergraduate level on the fundamentals of thermodynamics for mechanical engineers, and at the graduate level on the structure-property relations in materials. She emphasizes interdisciplinary approaches, and critical thinking, integrating mechanical engineering principles with material science to prepare students for research and technological applications.

Research Interests

Professor Bassiri-Gharb’s research is centered on the design, fabrication, and characterization of multifunctional materials, particularly ferroelectric and piezoelectric thin films and nanostructures. Her work explores the coupling of mechanical, electrical, and thermal properties at micro- and nano-scales. She investigates novel material architectures for energy harvesting, sensing, and actuation applications, combining experimental and theoretical methods to understand complex material behaviors. Her research integrates machine learning approaches for statistical analysis of correlative behavior and reducing the ultimate lab-to-fab timeline for introduction of laboratory learning into fabrication processes.

Recent Publications

  • MH Haddad, V Lebedev, K Holsgrove, S Rivera-Cruz, S Stock, N Maity, ..., Chemical Compensation Challenges in Processing Antiferroelectric PbZrO3 Thin Films, ACS Omega, 2025.
  • N Afroze, H Fahrvandi, G Ren, P Kumar, C Nelson, S Lombardo, M Tian, ..., Atomic-scale confinement of strongly charged 180 degree domain wall pairs in ZrO2, arXiv preprint arXiv:2507.18920, 2025.
  • C Davel, N Bassiri-Gharb, JP Correa-Baena, Machine learning in X-ray diffraction for materials discovery and characterization, Matter 8 (9), 2025.
  • N Maity, S Lisenkov, A Valdespino, M Haddad, L Jones, A Kumar, ..., Relaxation approach to quantum-mechanical modeling of ferroelectric and antiferroelectric phase transitions, arXiv preprint arXiv:2511.10485, 2025.
  • Needle in a Haystack: Information Recovery in Low Signal‐to‐Noise Piezoresponse Force Microscopy Data,KN Williams, HS Yuchi, GK Ligonde, M Repasky, Y Xie, N Bassiri‐Gharb, Small Methods, 2500318.