Juliane Mai (She/Her)

Juliane Mai
Research Associate Professor
Location: PHY 230

Biography

Juliane (Julie) Mai is an applied mathematician by training, and her scholarship is motivated by the desire of improving the performance of complex environmental models, such as hydrologic and land-surface models using methods like automatic model calibration, sensitivity and uncertainty analysis, and data assimilation. These methods are computationally expensive requiring advanced expertise in high-performance computing as well as big data science and management.

She is known for creating and maintaining data portals like CaSPAr (https://caspar-data.ca; since 2017) and HydroHub (https://hydrohub.org; since 2021) for providing widespread access to hydrologic and weather datasets. She also led two model intercomparison projects (2018-2022), where she led teams of more than 30 researchers to compare the performance of more than a dozen physically based with data-driven models across the Great Lakes Basin (https://hydrohub.org/mips_introduction.html).

Her contributions have led to more than 50 publications, an h-index of 24, around 2000 citations to date, and 10 invited talks since 2021. She is currently on the editorial board of two prominent hydrology journals, i.e., Water Resources Research and Journal of Hydrology, co-Editor of special issues in Hydrological Processes and Nature’s Scientific Data, member of the international group on Sensitivity Analysis of Model Output (SAMO), as well as member and incoming Chair of AGU’s Hydrologic Uncertainty Technical Committee.

Research Interests

  • Computational hydrology
  • Model analyses such as automatic calibration, sensitivity analysis, and uncertainty analysis
  • Large-scale data dissemination
  • Development of interactive data portals