BisQue + Scalable Image Informatics Workshop (Feb 5-6, 2018)

BisQue+Scalable Image Informatics Workshop

February 5-6, 2018 - University of California, Santa Barbara

Engineering Sciences Building Room 1001


click here for the WORKSHOP AGENDA

Scientific imaging is ubiquitous: From biology to medicine, materials science to remote sensing, much of the big data science is image centric. Currently  interpretation of images is usually performed within isolated research groups either manually or as workflows over narrowly defined conditions with specific datasets. This workshop aims to bring together scientists generating and analyzing large scale image datasets with computational scientists developing machine learning and computer vision methods, towards addressing the next generation challenges in the curation, sharing, distribution and analysis of big image data. The workshop will include an introductory session on the BisQue image informatics platform (, and the upcoming new version that includes deep learning, workflows and module development. The sessions on computer vision and machine learning will explore the emerging needs in big image data science and sustainable software infrastructure for image informatics. The workshop will include invited talks and panel discussions with participants from academia, industry and various Government agencies.

Note: Limited travel support will be available to graduate students, post-doctoral scholars and junior faculty/researchers. This BisQUe workshop is supported by grants from the Division of Biological Infrastructure and the Office of Advanced Cyberinfrastructure of the  National Science Foundation. This invitation-only workshop will be held on campus at UCSB. If you are interested in participating in the workshop, please send an email to one of the organizers below, stating your area of interest.


B. S. Manjunath, UCSB (Bisque, Image Analysis, Deep Learning)
Nirav Merchant, U Arizona (Cyverse)
Robert Miller, UCSB (Marine Sciences)
Tresa Pollock, UCSB (Materials)
Amit Roy-Chowdhury, UC Riverside (Vision, Machine Learning)