Hamid Kamangir

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AI Research Scientist

View the Project on GitHub hkaman7/HamidKamangirPortfolio

🤖 AI Research Scientist

Pioneering research in AI for environmental science, specializing in climate forecasting, crop yield prediction, and generative models.

🎓 Education

💼 Professional Experience

Postdoctoral Research Fellow

University of California, Davis
🔹 AI research on the Gates Foundation’s Gemini Project.

CMAViT CMAViT: Climate and Managment Aware Vision Transformer Spatio-Temporal Crop Yield Prediction.

Research Intern

Microsoft
🔹 Created a Vision Transformer model for super-resolution of climate CMIP datasets using ERA5 datasets, focusing on global drought forecasting.

CMIP Super-Resolution Model Demo CMIP Super-Resolution Model. Left: CMIP T2m, Middle: ERA5 T2m, Right: Prediction T2m.

Research Associate II

Texas A&M University, Corpus Christi
🔹 Explainable Physics-Informed Vision Transformer for Coastal Fog Forecasting. 🔹 Built 3D CNN models for coastal fog forecasting and developed generative models for thunderstorm prediction.

Tokenization Climate and Meteorological Tokenization strategy.

FogNet-V2 FogNet-v2: Explainable Physics-Informed Vision Transformer for Coastal Fog Forecasting. —

🛠️ Technical Skills

📚 Publications

View on Google Scholar

  1. Kamangir, H., Krell, E., Collins, W., King, S.A. and Tissot, P., 2024. FogNet-v2. 0: Explainable Physics-Informed Vision Transformer for Coastal Fog Forecasting. ESS Open Archive eprints, 327, pp.172191653-32706065.
  2. Bafti, A.G., Ahmadi, A., Abbasi, A., Kamangir, H., Jamali, S. and Hashemi, H., 2024. Automated actual evapotranspiration estimation: Hybrid model of a novel attention based U-Net and metaheuristic optimization algorithms. Atmospheric Research, 297, p.107107.
  3. Kamangir, H., Sams, B.S., Dokoozlian, N., Sanchez, L. and Earles, J.M., 2024. Large-scale spatio-temporal yield estimation via deep learning using satellite and management data fusion in vineyards. Computers and Electronics in Agriculture, 216, p.108439.
  4. Krell, E., Kamangir, H., Collins, W., King, S.A. and Tissot, P., 2023. Aggregation strategies to improve XAI for geoscience models that use correlated, high-dimensional rasters. Environmental Data Science, 2, p.e45.
  5. Kamangir, H., Krell, E., Collins, W., King, S.A. and Tissot, P., 2022. Importance of 3D convolution and physics on a deep learning coastal fog model. Environmental Modelling \& Software, p.105424.
  6. Kamangir, H, Collins, W., Tissot, P., King, S.A., Dinh, H.T.H., Durham, N. and Rizzo, J., 2021. FogNet: A multiscale 3D CNN with double-branch dense block and attention mechanism for fog prediction. Machine Learning with Applications, p.100038
  7. Alizadeh, B., Bafti, A.G., Kamangir, H., Zhang, Y., Wright, D.B. and Franz, K.J., 2021. A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction. Journal of Hydrology, 601, p.126526.
  8. Kamangir, H., Collins, W., Tissot, P. and King, S.A. Deep‐learning model used to predict thunderstorms within 400 km2 of south Texas domains. Meteorological Applications, 27(2), 2020.

🎖️ Grants and Funding

New York Sea Grant (NYSG)

🏆 Honors and Awards