Professor Karin Verspoor is Dean of the School of Computing Technologies at RMIT University in Melbourne, Australia.
Karin's research primarily focuses on the use of artificial intelligence methods to enable biological discovery and clinical decision support, through extraction of information from clinical texts and the biomedical literature and machine learning-based modelling.
Karin held previous posts as Director of Health Technologies and Deputy Head of the School of Computing and Information Systems at the University of Melbourne, as the Scientific Director of Health and Life Sciences at NICTA Victoria Research Laboratory, at the University of Colorado School of Medicine, and at Los Alamos National Laboratory.
She is also the Victorian Node lead and co-founder of the Australian Alliance for Artificial Intelligence in Health.
- Artificial Intelligence in Medicine
- Biomedical Natural Language Processing
- Computational Linguistics
- Health Informatics
- Health Data Analytics
- Computational Biology
- PhD, Cognitive Science and Natural Language, The University of Edinburgh (UK)
- MSc, Cognitive Science and Natural Language, The University of Edinburgh (UK)
- BA, Computer Science, Rice University (Houston, TX, USA)
- Intelligenesis/Webmind Corporation (New York, NY, USA)
- Applied Semantics (Los Angeles, CA, USA)
- Los Alamos National Laboratory (Los Alamos, NM, USA)
- National ICT Australia (Melbourne, VIC, Australia)
- He, E.,Albahem, A.,Cavedon, L.,Verspoor, K., et al, . (2021). ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents In: Frontiers in Research Metrics and Analytics, 6, 1 - 28
- Liu, J.,Capurro, D.,Nguyen, A.,Verspoor, K. (2021). Early prediction of diagnostic-related groups and estimation of hospital cost by processing clinical notes In: npj Digital Medicine, 4, 1 - 8
- Verspoor, C. (2021). The Evolution of Clinical Knowledge During COVID-19: Towards a Global Learning Health System In: Yearbook of Medical Informatics, 30, 176 - 184
- Cao, K.,Verspoor, C.,Chan, E.,Daniell, M.,Sahebjada, S.,Baird, P. (2021). Machine learning with a reduced dimensionality representation of comprehensive Pentacam tomography parameters to identify subclinical keratoconus In: Computers in Biology and Medicine, 138, 1 - 6
- Using artificial intelligence and novel technology to detect and monitor corneal disease (administered by University of Melbourne). Funded by: National Health and Medical Research Council (NHMRC) Project Grants 2017 (for funding commencing in 2018) from (2021 to 2023)
- Automated Assessment of Data Quality in Biological Knowledge Resources (Administered by University of Melbourne). Funded by: ARC Discovery Projects 2019 from (2021 to 2021)
- Meeting the challenges of invasive fungal infection: antifungal stewardship and effective surveillance in high-risk groups (Administered by University of Melbourne). Funded by: NHMRC Project Grant - via other university from (2019 to 2023)