Hydrology • Groundwater • Data Science

Hello, and thank you for visiting!

My name is Moe Farmani, and I’m a Ph.D. student in Hydrology and Atmospheric Sciences with a minor in Data Science. This webpage is part of my mission to raise public awareness about water and groundwater issues—critical yet often overlooked topics that affect agriculture, ecosystems, and our daily lives.

Through this platform, I share not only hydrologic research, but also the computational and analytical methods behind it: physically based modeling, scientific machine learning, geospatial data workflows, and decision-support tools for water systems. Whether you're a student, researcher, engineer, or policymaker, I want the site to show how water science and data science can work together.

Let’s bridge the gap between science, computation, and society, starting with water.

Learn more about me Explore research projects View publications
Hydrologic Science Large-scale land modeling, recharge processes, soil moisture behavior, and streamflow prediction in water-limited regions.
Water Resources Flood resilience, groundwater sustainability, smart water systems, and hydrologic insight for real management problems.
Scientific ML Python, HPC, geospatial pipelines, differentiable modeling, deployed dashboards, and machine learning for environmental prediction.

Key Areas of Expertise

  • Hydrological Modeling: Enhancing Noah-MP and RAPID for improved infiltration, runoff, and soil-moisture dynamics.
  • Water Resources Systems: Studying groundwater recharge, urban drainage, and smart water distribution with management relevance.
  • Flood Management: Using SWMM to assess climate impacts on urban drainage and improve flood resilience.
  • Database Design: Building MySQL backends for smart water distribution and optimization systems.
  • Differentiable Hydrology: PyTorch-based hydrological processes for GPU acceleration and ML coupling.
  • Data Science & ML: Building predictive models, visualization tools, and analytical pipelines across environmental and broader applied datasets.

With proficiency in Python, R, SQL, HPC, and tools like SWMM, I work at the nexus of water resources, climate science, and data analytics—aiming to advance sustainable water management and climate resilience via hybrid physical + data-driven modeling.

I also enjoy collaboration and leadership, including service with the El Dia Student Symposium.

What This Site Shows

More than one lane

Hydrologist

Research pages on soil moisture memory, baseflow generation, process-aware rainfall-runoff modeling, and river routing show my core hydrologic identity.

Water Resources Researcher

Flood modeling, groundwater-focused questions, recharge dynamics, and smart water-system design connect the science to decision-making and infrastructure.

Data Scientist

My CV and projects include inference, prediction, visualization, database design, and statistical learning across environmental and public datasets.

ML Engineer

The site also shows model architecture design, PyTorch-based scientific ML, geospatial data pipelines, HPC workflows, and reproducible software outputs.

Site Guide

Explore the Website

I reorganized this site to make it easier to understand who I am, what I study, and where to find the most useful materials.

About

Read a concise overview of my research direction, interdisciplinary background, and the questions that drive my work.

Open About page →

CV

See education, research experience, awards, and technical skills in one place.

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Projects

Browse research themes and featured project pages spanning hydrology, water systems, and scientific machine learning.

Open Projects hub →

Software

Find the packages, dashboards, and computational tools I’m building to support hydrologic, geospatial, and data-intensive research workflows.

Open Software page →