Curriculum Vitae

Curriculum Vitae

Mohammad Saeedi is a water resources engineer and computational hydrologist bridging hydrological process physics with advanced machine learning to develop next-generation Earth observation systems for precipitation retrieval and soil moisture estimation. His research is grounded in physical rigor rather than purely data-driven approaches — designing innovative physics-informed deep learning architectures that embed well-established hydrological formulations, including infiltration, routing, and soil-water balance models, directly into learned, interpretable systems. His research spans the full arc from algorithm development to global-scale deployment: self-calibrating regionalization frameworks, inverse hydrological inference for precipitation reconstruction, and multi-sensor fusion of active and passive satellite observations (SMAP, ASCAT, AMSR2). His current flagship project develops a global daily precipitation model fusing multiple physics-based expert systems through a regime-aware gating network — with direct potential to complement and extend the GPM mission by exploiting synergies with upcoming NASA Earth observation missions and expanding reliable precipitation estimates to data-sparse regions worldwide.

Education

2024 - 2028 University of Virginia, VA, USA
Ph.D. in Civil & Environmental Engineering

Awards and Honors

• Research Grant Awardee, Virginia Space Grant Consortium (VSGC) / NASA (2026)
• Selected as a 2024 Vadose Zone Journal Outstanding Reviewer for exceptional peer-review contributions

Invitation to Join Editorial Board/Serve as a Reviewer

Scientific Review Committee Member (Reviewer), IEEE GRSS IGARSS 2026
Transactions on Geoscience and Remote Sensing
Science of the Total Environment
Journal of Hydrology
Scientific Data
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Water Resources Management
International Journal of Climatology
PLOS ONE
American Journal of Remote Sensing
Vadose Zone Journal