Overview
My research reflectsÌýmy interests and training in fluid dynamics, remote sensing data and computation I work on numerical modelling, in addition to data-driven approaches, including deep learning, that maintain the fidelity of the physical system.
Examples of current projects include fluid flow modelling, fusing data from different sensors forÌýmultimodal learning, and using 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 groupÌý
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 flowÌý
Deep learning for image segmentation andÌýphysically inspired neural networksÌý
Remote sensing and prediction of environmental phenomena
Computational fluids, turbulence, stratified flows, internal flows
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 À¶Ý®ÊÓƵ, 2022
Distinguished Performance Award, University of À¶Ý®ÊÓƵ 2021
