Advancing the state of the art through innovative and collaborative research
Anomalous Behavior DiscoveryAdvanced machine learning and intelligent decision support systems View DetailsClinical Decision Support SystemBridging the gap between software engineering and clinical medicine with machine learning.View DetailsClinical Knowledge DisseminationAssisting physicians in underserved regions using Machine LearningView details
Our research focuses on the following research areas
Clinical Decision System
Building a community through research & innovation
We’re a multi-disciplinary research initiative on Information and System Intelligence research with the following aims:
- AIM 1 Foster collaborative multi-disciplinary projects that utilize artificial intelligence (AI), big data analytics, intelligent decision support systems (IDSS) and advanced human computer interaction techniques (VR & AR) in different application domains
- AIM 2 Initiate a “data-intelligence-as-a-service” (DIaaS) infrastructure to encourage participation. This infrastructure will provide cloud-based open API (application program interface) services as sample AI-driven solutions to be used in different projects.
- AIM 3 Attract researchers and industries to collaborate in major projects and expose graduate and undergraduate students to real-world research problems and enhance their critical thinking and practical skills.
An Expressive Event-based Language for Representing User Behavior Patterns. H. Sharghi, K.Sartipi. Journal of Intelligent Information Systems (JIIS). Pages 1-25. Springer DOI: 10.1007/s10844-017-0456-5 (2017)
OpenID Connect as a Security Service in Cloud-based Medical Imaging Systems. W. Ma, K.Sartipi, H. Sharghi, D. Koff, P. Bak. Journal of Medical Imaging (JMI) 3(2), 026501 (2016), doi: 10.1117/1.JMI.3.2.026501. (SPIE Digital Library)
Security Middleware Infrastructure for Medical Imaging System Integration and Monitoring. W. Ma, K.Sartipi. ICACT Transaction on Advanced Communications Technology (TACT).Vol. 4, Issue 6, November 2015. Pages 736-744.
Synthesizing Scenario-based Dataset for User Behavior Pattern Mining. W. Ma, K. Sartipi. International Journal of Computer and Information Technology (IJCIT). ISSN: 2279-0764, Vol 04, Issue 06, November 2015. Pages 855-866.
Detecting the Insider’s Threat with Long Short Term Memory (LSTM) Neural Networks. E. Lopez, K. Sartipi.Archive: arXiv:2007.11956 .
Knowledge Discovery for Clinical Decision Support System in Patient Records, D. Budhathoki, K. Sartipi. IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS 2020). July 28-30, 2020. Mayo Clinic, Rochester, MN (on-line). (Submitted) (Abstract)
QRMine: A python package for triangulation in Grounded Theory. B. Eapen, N. Archer, K. Sartipi. Archive: xrXiv-2003.13519. 4 pages. Preprint, compiled March 31, 2020.
Can Language Help in the Characterization of User Behavior? Feature Engineering Experiments with Word2Vec. E. Lopez, K. Sartipi. The 32nd International Conference on Software Engineering & Knowledge Engineering (SEKE 2020). July 9-11, 2020, Pittsburgh, USA. pages 371-374.
FHIRForm: An Open-Source Framework for the Management of Electronic Forms in Healthcare. B.Eapen , A. Costa, N. Archer, K. Sartipi. IOS Press. doi:10.3233/978-1-61499-951-5-80, pages 80-85, 2019.
Areas of Research
AUGMENTED & VIRTUAL REALITY
DATA MINING & MODELLING
Our collaborating team come from research driven faculties
Interested in collaboration?
We’re a highly curious and collaborative research team looking to partner with like-minded researchers, industry professionals and industry partners.
A multi-disciplinary research initiative on Information and System Intelligence research.
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