Edinburgh Research Explorer

Putting the Scientist in the Loop - Accelerating Scientific Progress with Interactive Machine Learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Oisin Mac Aodha
  • Vassilios Stathopoulos
  • Michael Terry
  • Kate E. Jones
  • Gabriel J. Brostow
  • Mark Girolami

Related Edinburgh Organisations

Original languageEnglish
Title of host publication2014 22nd International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages9
ISBN (Electronic)978-1-4799-5209-0
Publication statusPublished - 8 Dec 2014
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, United Kingdom
Duration: 24 Aug 201428 Aug 2014

Publication series

PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Electronic)1051-4651


Conference22nd International Conference on Pattern Recognition, ICPR 2014
CountryUnited Kingdom


Technology drives advances in science. Giving scientists access to more powerful tools for collecting and understanding data enables them to both ask and answer new kinds questions that were previously beyond their reach. Of these new tools at their disposal, machine learning offers the opportunity to understand and analyze data at unprecedented scales and levels of detail.
The standard machine learning pipeline consists of data labeling, feature extraction, training, and evaluation. However, without expert machine learning knowledge, it is difficult for scientists to optimally construct this pipeline to fully leverage machine learning in their work. Using ecology as a motivating example, we analyze a typical scientist’s data collection and processing workflow and highlight many problems facing practitioners when attempting to capitalize on advances in machine learning and pattern recognition. Understanding these shortcomings allows us to outline several novel and underexplored research directions. We end with recommendations to motivate progress in future cross-disciplinary work.

    Research areas

  • data analysis, feature extraction, learning (artificial intelligence), interactive machine learning, data labeling, data training, data evaluation, pattern recognition, Pipelines, Data models, Labeling, Training, Educational institutions, Feature extraction, Biological system modeling, computer vision, human-computer interaction, data visualization, ecology, biodiversity


22nd International Conference on Pattern Recognition, ICPR 2014


Stockholm, United Kingdom

Event: Conference

ID: 122750835