parallax background

Supervisor Database Search

Search for supervisors below. You can filter your search using the options and select
multiple fields by holding CTRL (Cmd on Mac) + clicking multiple options in a list.

The ICAT Supervisor list is reviewed annually by the partner universities and updated online in March/April each year. You can read the ICAT supervisor policy here.

Full NameDr Brian Mac Namee

Computer Science

University College Dublin

Email Address:Email hidden; Javascript is required.

Research Fields
  • artificial intelligence/machine learning/data analytics
Postgrad Medical Specialties
  • Radiology
Medical Subspecialties
  • Radiology
My Work

Dr. Brian Mac Namee received a BA (mod) and PhD in Computer Science from Trinity College Dublin in 2000 and 2004 respectively. In 2015 Brian joined the UCD School of Computer Science as a lecturer. At UCD Brian is the Centre Director for the SFI Centre for Research Training in Machine Learning ( and a Funded Investigator at the Insight ( and VistaMilk ( SFI research centres. Previously Brian was a founding Principal Investigator at the CeADAR centre ( and a Funded Investigator at the Insight centre .

Brian's research focuses on machine learning, predictive analytics, data visualisation, and augmented reality. Brian has published extensively in machine learning, predictive analytics, and information visualisation - a recent highlight is the textbook "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples and Case Studies" published with MIT Press in 2015 (

Prior to joining UCD Brian worked in industry as an R & D software engineer for Agilent Technologies, and at the School of Computing at Dublin Institute of Technology as a lecturer where he co-founded the Applied Intelligence Researcher Centre (, and developed DIT's successful MSc in Computing (Data Analytics) programme.

Potential Projects

I would be interested in collaborating with clinicians to bring machine learning techniques to bear on clinical challenges. In particular I am interested in interactive machine learning techniques.