Overview 

I am highly motivated by problems related to climate change with emphasis on northern regions.  

My research reflects my interests and training in fluid dynamics, remote sensing data and computation I work on recent data-driven approaches, including deep learning, that maintain the fidelity of the physical system.

Examples of current projects include classifying fluid structures with unsupervised approaches, sensor fusion and multimodal learning, deep learning for space/time predictions.

I am open to different application areas and colloaborate with many others in the following groups

The vision and image processing group

The automation and intelligent systems group 

Fluid dynamics group in applied math

The remote sensing of environmental change group

I welcome students across all diversity groups and can adjust projects for various levels, from undergraduate to PhD or Postdoc

Research Interests

Computer vision, application to problems of heat and fluid flow 

Deep learning for image segmentation and physically inspired neural networks 

Remote sensing and prediction of environmental phenomena

Computational fluids, turbulence 

Teaching

ME 351 Fluid Mechanics

ME 664 Turbulence

SYDE 351 Systems Models

SYDE 621 Numerical Methods

SYDE 113 Engineering Mathematics

Reading courses on deep learning and remote sensing

Professional Service

IEEE GRSS Women-to-Women Mentor (2023 - present)

Associate Editor, AGU Journal of Machine Learning and Computation (2024 - present)

Awards

Outstanding Performance Award, University of Waterloo, 2022

Distinguished Performance Award, University of Waterloo 2021