My research primarily revolves around the utilization of cutting-edge techniques in deep learning and machine learning to analyze large seismic waveform datasets. The overarching objective is to gain a more profound insight into earthquake rupture mechanisms and their implications for seismic risks.

Ph.D., Geophysics, University of Nevada-Reno, 2024

M.S., Earth Sciences, University of Oregon, Eugene, 2021

Chatterjee, A., D.T. Trugman, G. Hirth, J. Lee and V.C. Tsai (2024). High-Frequency Ground Motions of Earthquakes Correlate with Fault Network Complexity. Geophysical Research Letters; doi: 10.1029/2024GL109418

Lee J., V.C. Tsai, G. Hirth, A. Chatterjee, and D.T. Trugman (2024). Fault Network Geometry Determines Large-Scale Earthquake Frictional Behavior. Nature; doi: 10.1038/s41586-024-07518-6

Saad O. M., I. Helmy, M. Mohammed, A. Savvaidis, A. Chatterjee, and Y. Chen (2024).Deep Learning Peak Ground Acceleration Prediction Using Single-Station Waveforms. IEEE Transactions on Geoscience and Remote Sensing; doi: 10.1109/TGRS.2024.3367725

Chatterjee, A., N. Igonin, and D.T. Trugman (2023). A Real-Time and Data-Driven Ground Motion Prediction Framework for Earthquake Early Warning. Bulletin of the Seismological Society of America; doi: 10.1785/0120220180

Mache, S., A. Chatterjee, K. Rajendran, and C.S. Seelamantula (2022). Hilbert−HuangTransform and Energy Rate Functions for Earthquake Source Characterization—A Study from the Japan Trench. Bulletin of the Seismological Society of America; doi: 10.1785/0120220099

• Outstanding Student Presentation Award, American Geophysical Union, 2024.
• Seismological Society of America Student Travel Grant, 2021.
• First-Year-Fellowship at University of Oregon, 2019.