Behzad Vahedi

About Me

I am an applied machine learning scientist with a robust geospatial background and extensive experience in developing machine learning and deep learning solutions for spatial and temporal challenges. My expertise encompasses satellite image classification, COVID-19 forecasting, and Climate AI. My research has been featured in prestigious journals such as Nature Communications and Science of the Total Environment. Additionally, I have industry experience in climate AI model development and DevOps.

Currently, I am pursuing a Ph.D. in Geography at the University of Colorado Boulder. My research focuses on leveraging spatial principles to optimize the design and performance of deep learning models, particularly at the intersection of computer vision and remote sensing. I am developing deep learning models for classifying sea ice in remotely sensed radar imagery. I am also a member of the GeoHAI Lab, directed by Dr. Morteza Karimzadeh.

I hold an M.A. in Geography from the University of California, Santa Barbara, where I was advised by Dr. Werner Kuhn, as well as an M.Sc. in GIS Engineering and a B.Sc. in Geomatics Engineering from K.N. Toosi University of Technology.

I am open to industry positions in machine learning engineering or research, where I can contribute to innovative solutions and cutting-edge advancements.

Research Interests

• Computer Vision: image classification, semantic segmentation, computer vision for earth observation data
• Deep Learning: transfer learning, time-series forecasting, spatiotemporal deep learning

Skills

• General: Machine learning, Deep learning, Geospatial data science, CI/CD, DevOps, MLOps, Computer Vision
• Programming: Python (proficient), JAVA (intermediate), Haskell (intermediate), SQL, R
• Deep Learning: PyTorch, Tensorflow, Scikit-Learn, Hugging Face, Weights & Biases (wandb)
• Geospatial: GDAL, GeoPandas, RasterIO ArcGIS, QGIS, Google Earth Engine
• Other: Google Cloud Platform (GCP), Docker, Git, GitHub Actions and workflows, location data analytics

News

• [Jun. 2024] Our paper on Partial Label Learning for sea ice classification is accepted in IEEE JSTARS journal.
• [Feb. 2024] I finished my AI Residency at Google X, where I gained invaluable experience working on Climate AI modeling and DevOps. Read about our team here.
• [Aug. 2023] I finihed my internship at the Earth Lab at CU Boulder where I worked on FiredPy, a Python package for Fire Event Dilineation.
• [Sep. 2023] Our paper on Sea Ice Segmentation is published in IEEE TGRS. Read more here.
• [Dec. 2021] I received the Outstanding Student Presentation Award from AGU for my presentation at Fall Meeting 2021. Read more here.

Publications

Presentations

Services

Journal Reviewer

• IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS)
• PloS One
• Scientific Reports
• Annals of GIS
• International Journal of Digital Earth
• Atmospheric Research

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