HASTE: Hierarchical Analysis of Spatial and Temporal Data
Title: HASTE: Hierarchical Analysis of Spatial and Temporal Data
DNr: Berzelius-2023-269
Project Type: LiU Berzelius
Principal Investigator: Carolina Wählby <carolina.wahlby@it.uu.se>
Affiliation: Uppsala universitet
Duration: 2023-11-14 – 2024-06-01
Classification: 10201
Homepage: http://haste.research.it.uu.se/


The HASTE project takes a holistic approach to new, intelligent ways of processing and managing very large amounts of microscopy images to leverage the imminent explosion of image data from modern experimental setups in the biosciences. One central idea is to represent datasets as intelligently formed and maintained information hierarchies and to prioritize data acquisition and analysis to certain regions/sections of data based on automatically obtained metrics for usefulness and interestingness. To arrive at such smart systems for scientific discovery in image data, we will pursue a range of topics such as efficient data mining in image data, machine learning models with quantifiable confidence that learn an object’s interestingness, and development of intelligent and efficient cloud systems capable of mapping data and compute to a variety of cloud computing and data storage e-infrastructure based on the quality and interestingness of the data.