Dr. Bangerter joined Imperial in February 2018 as Associate Professor of Bioengineering, where he specialises in medical imaging (specifically MRI, with a focus on ultra-high field imaging), AI and machine learning in healthcare, big data/data analytics, and signal processing. He is the Imperial lead on the London Collaborative Ultra-High Field Scanner (LOCUS) project, a joint venture for ultra-high field MR imaging between King's College London, Imperial, University College London, and the Institute for Cancer Research. He serves on the Future Leaders Panel for EFG Asset Management in London as their Artificial Intelligence expert, and is affiliated with the Imperial Artificial Intelligence Network and the Computation, Cognitive, and Clinical Neuroimaging Laboratory.
Dr. Bangerter received a Bachelor's degree in Physics from U.C. Berkeley, and received his Master's and Ph.D. degrees in Electrical Engineering from Stanford University. He holds adjunct or courtesy appointments in executive education at INSEAD, the Department of Radiology at the University of Utah, and the Department of Electrical Engineering at Brigham Young University, and recently spent a year as a visiting scholar at the University of Oxford.
Dr. Bangerter brings a broad variety of experience in both industry and academia to Imperial. He has deep technical expertise in healthcare, medical imaging, machine learning, big data, signal processing, and software development, and significant management and strategy experience from his work in industry.
He spent several years as a software developer for metrology company Wilcox Associates prior to graduate school, and co-founded data visualization software company Visualize in 1996. His doctoral work at Stanford focused on the development of new fast imaging techniques using MRI. After graduate school, Dr. Bangerter worked at management consulting firm McKinsey & Company, transitioned into a senior business development and strategy role at Microsoft, and then served as Vice President of Product Management for advertising technology company Reactrix. He returned to academia in 2006 as a researcher in Stanford's Radiological Sciences Laboratory, and spent a decade as faculty in Brigham Young University's Department of Electrical and Computer Engineering prior to joining Imperial. While at BYU he was awarded the prestigious David Evans Chair, founded BYU's Medical Imaging Research Center, and helped build the ground-breaking cross-faculty Crocker Innovation Fellowship Program pioneered in Brigham Young University's Marriott School of Management.
His current academic interests include the development of novel pulse sequences for magnetic resonance imaging at ultra-high magnetic field strengths, the application of machine learning to a variety of problems in medical imaging and healthcare, and the promises and limitations of artificial intelligence and machine learning techniques in the biosciences, healthcare, and other industries. He was instrumental in setting up the U.K. Biobank Neuroimaging study (a massive big-data health research effort), and has active research collaborations with groups at Stanford University, Oxford, Cambridge, University of Utah, Brigham Young University, University of Utah, Kings College London, and Siemens Healthcare.
In addition to his research and teaching interests, Dr. Bangerter is passionate about digital transformation, innovation, and the practical applications of artificial intelligence. He regularly teaches executive education courses at INSEAD, helping executives bridge the gap between deep technical concepts and digital strategies for their businesses. He serves on the scientific advisory boards for a number of companies, and regularly consults and advises in the areas of artificial intelligence, data analytics, innovation (with an emphasis on healthcare), and intellectual property strategy.
et al., 2018, Feasibility of diffusion tensor and morphologic imaging of peripheral nerves at ultra-high field strength, Investigative Radiology, Vol:53, ISSN:0020-9996, Pages:705-713
et al., 2017, Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank., Neuroimage, Vol:166, ISSN:1053-8119, Pages:400-424
et al., 2016, Multimodal population brain imaging in the UK Biobank prospective epidemiological study, Nature Neuroscience, Vol:19, ISSN:1097-6256, Pages:1523-1536
et al., Deep Learning Super-FOV for accelerated bSSFP banding reduction, 27th Annual ISMRM conference, May 2019
et al., Synthetic Banding for bSSFP Data Augmentation Using Machine Learning, 27th Annual ISMRM conference, May 2019