About Me

I am a currently a researcher associated with the PUC-Behring AI Institute and soon will join the Departamento de Informática at PUC-Rio as a full professor.

Previously I was a researcher and manager at IBM Research Brazil, where I led multiple applied artificial intelligence research projects in areas spanning from subsurface characterization for oil&gas and mining, to earth observation, weather, and climate problems.

For a full academic and professional history, you can view my Curriculum Vitae.

Your Portrait

Research

My main area of focus is developing and analyzing machine learning methods that use geospatial data for climate & sustainability applications. Some research topics of current interest are:

  • Robustness: How can large models pre-trained on unlabeled geospatial data capture the different spectral, spatial, and temporal nuances required for various types of applications?
  • Active Learning and Annotation Efficiency: How can we direct limited labeling efforts to maximize model performance?
  • Interpretability and Generalization: How can geospatial priors and physical models improve transparency and robustness?
  • Benchmarking and Impact: How can evaluation metrics reflect real-world impact and enable a fair comparison between models?
  • Local vs. Global Models: Should we strive for a single global models or multiple local specialists, and can we automatically identify the scopes for transferability?
  • Multimodality: What are the benefits of multimodal pre-training for tasks that include multiple modalities, and what are the potential ways to incorporate additional data during the fine-tuning phase?

For a full list of papers please see my Google Scholar.

Teaching

  • Artificial Intelligence for Geospatial Data (Graduate level)
    2026.2 • PUC-Rio

    Concepts, models, and applications of the field known as GeoAI — artificial intelligence for geospatial data — preparing the student for future research on specific topics within the field.

  • Introduction to Artificial Intelligence (Undergraduate level)
    2026.2 • PUC-Rio

    Foundational course in AI with a particular emphasis on intelligent agents, problem-solving, logic, and automated planning.

Contact

Email: biancaz@gmail.com

Profiles: LinkedInGitHub