Naeem Khoshnevis, PhD

Naeem is a research software engineer member of Research Software Engineering (RSE) at the FAS Research Computing (FASRC) department. He designs, builds and optimizes software applications for researchers across Harvard University. Naeem has a superior mathematical and numerical analysis background and has developed, documented, debugged, extended, and refactored numerous scientific software applications for research groups, helping them successfully carry out their funded research projects. Before joining FASRC, Naeem conducted research on large-scale ground motion simulations. 

Having an interdisciplinary educational background in Engineering, Applied Science, and Computer Science, he values the importance of good software engineering practices in reliable and reproducible scientific research.

In his free time, Naeem enjoys reading, running, cooking, and watching documentaries.


Select Publications:

Khoshnevis, N., & Taborda, R. (2019). Application of Pool-Based Active Learning in Physics-Based Earthquake Ground Motion Simulation. Seismological Research Letter, 90 (2A): 614-622.

Khoshnevis, N., & Taborda, R. (2018). Prioritizing Ground‐Motion Validation Metrics Using Semisupervised and Supervised Learning. Bulletin of the Seismological Society of America, 108(4), 2248-2264.               

Khoshnevis, N., Taborda, R., Azizzadeh-Roodpish, S., & Telesca, L. (2017). Analysis of the 2005–2016 Earthquake Sequence in Northern Iran Using the Visibility Graph Method. Pure and Applied Geophysics, 174(11), 4003-4019.

Khoshnevis, N., Taborda, R., Azizzadeh-Roodpish, S., & Cramer, C. H. (2017). Seismic hazard estimation of northern Iran using smoothed seismicity. Journal of Seismology, 21(4), 941-964.

Taborda, R., Azizzadeh-Roodpish, S., Khoshnevis, N., & Cheng, K. (2016). Evaluation of the Southern California Seismic Velocity Models through Simulation of Recorded events. Geophysical Journal International, 205(3), 1342-1364.