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Zachary del Rosario '14, Ph.D.

Zachary del Rosario

Visiting Assistant Professor of Engineering

MH 329


PhD, Aerospace Engineering, Stanford University
MS, Aerospace Engineering, Stanford University
BS, Mechanical Engineering, Olin College

Select Courses Taught

Data Science


Uncertainty Quantification
Data Science

Structural Safety
Materials Informatics


Association of American Colleges and Universities (AAC&U) Patricia Cross Future Leaders Award (2020)
Stanford Vice Provost for Graduate Education (VPGE) Diversifying Academia, Recruiting Excellence (DARE) Fellowship (2018)
National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) (2015)


Zach graduated from Olin College in 2014 with the desire to become a professional educator. Six years later he earned a PhD in Aerospace Engineering and returned to Olin to start his faculty career. In his research, Zach helps scientists and engineers make decisions under uncertainty. In his PhD work he discovered long-standing probability errors in aircraft design that expose travelers to risk, and developed alternative design criteria with mathematically-provable safety guarantees. Zach also consults with scientists from other disciplines: He has worked with material scientists to use machine learning for accelerated materials R&D.

Presently, Zach is developing a Grammar of Model Analysis to support the teaching and communication of model analysis under uncertainty.
In his teaching, Zach strives to cultivate an inclusive and supportive class environment. He works to help all students become self-directed learners, emphasizes communication alongside analysis, and encourages students to embrace uncertainty. Zach's classes tend to emphasize student-directed work and open discussion.

Select Publications

del Rosario, Z. (2020b). Grama: A Grammar of Model Analysis. Journal of Open Source Software 5(51), 2462. eprint:
del Rosario, Z., M. Rupp, Y. Kim, E. Antono, and J. Ling (2020). Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization. The Journal of Chemical Physics 153(2), 024112. eprint:
del Rosario, Z., G. Iaccarino, and R. W. Fenrich (2019). Fast Precision Margin with the First-Order Reliability Method. AIAA Journal. eprint:
Personal Website