Curriculum Vitae

Contact Information

Mohammad Ali Farmani

Personal Website

farmani@arizona.edu

Education

• Ph.D. in Hydrology, University of Arizona, Tucson, USA — August 2022 – Present
• M.S. in Data Science, University of Arizona, Tucson, USA — August 2024 – Present
• M.S. in Civil Engineering – Water Resources Management, Tarbiat Modares University, Tehran, Iran
• B.S. in Civil Engineering, Iran University of Science and Technology, Tehran, Iran

Selected Data Science & ML Projects

1. Bayesian Inference of 3D Line Parameters (Metropolis–Hastings MCMC)
   Implemented custom MCMC sampler to infer 3D line endpoints from noisy 2D camera images.
   Used Gaussian priors and multi-view likelihood to achieve accurate MAP reconstruction.
2. Fairness in Insurance Pricing & Vaccine Misinformation Ethics (Data Ethics)
   Analyzed independence, separation, and sufficiency criteria in ML fairness.
   Conducted ethical analysis on criminalization of vaccine misinformation using real-world scenarios.
3. Tucson Car Collision Dashboard (Data Visualization – Quarto, R)
   Built interactive dashboard visualizing traffic accidents (2018–2023).
   Used heatmaps, time series, and violation-based statistics to support road safety planning.
4. Crash Severity Prediction (Data Mining – ML)
   Trained and evaluated Random Forest, Decision Tree, KNN models on Massachusetts crash data.
   Achieved ~78% accuracy; identified environmental and demographic risk factors.
5. Global Temperature Trends (Time Series + ML)
   Analyzed NASA GISS data (1880–2023) using Mann–Kendall, regression, STL decomposition.
   Applied ML classifiers (XGBoost, RF) to detect and forecast warming anomalies.
6. Neural Networks & Codabench NLP Competition (Deep Learning)
   Built feedforward NN from scratch (NumPy); Developed Keras architectures (deep vs wide MLP, ReLU vs tanh).
   Implemented CNN and RNN models for MNIST and YouTube spam datasets; Built multi-label NLP classifier using BERT.
7. Smart Water Distribution and Optimization System (SWDOS) (SQL/DB)
   Designed conceptual–logical–physical database models integrating IoT sensor data.
   Implemented advanced SQL queries: JOINs, subqueries, CASE, aggregation. Built schema for real-time water monitoring and decision support.

Computer Skills

• Programming: Python (PyTorch, TensorFlow, JAX), RStudio, Fortran, HTML, SQL
• Hydrologic & Climate Modeling: Noah-MP, RAPID, WRF (exposure), SWMM, HEC-RAS, HEC-HMS
• Geospatial & ML Pipelines: xarray, GeoPandas, scikit-learn, rasterio, NumPy, Pandas, Dask
• HPC & Workflow: SLURM job arrays, Git/GitHub, Bash, parallel computing
• Cloud Exposure: GCP, AWS (basic)
• Visualization: Matplotlib, Cartopy, Seaborn, Plotly, Quarto, Tableau

Research Experience

Graduate Research Assistant — University of Arizona, Tucson, USA Aug 2022 – Present

• AI + Physical Modeling: Developed differentiable hydrology modules integrating deep neural networks with Noah-MP.
• Data Science & ML: Built scalable ML pipelines on HPC using PyTorch, xarray, GeoPandas, and Dask.
• Geospatial & Hydro Modeling: Enhanced Noah-MP hydrologic scheme; implemented RAPID routing; evaluated IMERG & ERA5 precipitation datasets.
• Climate Analytics & HPC: Processed petabyte-scale datasets with SLURM job arrays and automated model training & uncertainty quantification workflows.
• Selected Projects: ET Estimation using Penman-Monteith + DL; Streamflow Prediction; Soil Moisture Memory; Extreme Precipitation & Recharge; Noah-MP Process Representation.

Graduate Research Assistant — Tarbiat Modares University, Tehran, Iran

• Downscaled GCM precipitation using ML/DL approaches.
• Temporal disaggregation of rainfall using KNN-based methods.
• Hydrological and hydraulic modeling of extreme flood events (SWMM, HEC-RAS).
• DEM-based subcatchment characterization with ArcGIS; IDF curve generation and MATLAB-based urban flooding simulation.
• System dynamics modeling of water crisis management in Zayandeh-Rud basin.

Awards & Honors

Thank You

I’m passionate about advancing hydrology, climate resilience, and data-driven decision making. Feel free to reach out for research collaborations, data projects, or community outreach on groundwater and water policy.