Matt McCormick

Matthew McCormick received a B.S. in Biomedical Engineering with a focus on biomechanics from Marquette University in 2005. While at Marquette, he engaged in an internship at Boston Scientific Corporation working on peripheral vascular nitinol stents. He continued his studies in Biomedical Engineering in the doctorate program at the University of Wisconsin-Madison where his research covered not only on vascular mechanics, but also signal processing, medical imaging physics, and computing. His thesis focused on characterization of carotid plaque, a primary cause of stroke, with diagnostic ultrasound. The principal aim of this research was to develop algorithms to quantify local deformation in the plaque from raw ultrasound image data. The code developed for this purpose made extensive use of the Insight Toolkit (ITK) and the Visualization Toolkit (VTK).

While at the University of Wisconsin-Madison, Dr. McCormick was also an active member of The Hacker With, a peer-teaching organization intended to help graduate students, staff scientists, faculty, and undergraduate students get the open source, scientific computing skills they need to effectively perform research. He is currently an active participant in the scientific Python community, and has helped organize the SciPy conference. He serves as a reviewer for a number of international scientific journals.

Dr. McCormick joined Kitware, a collaborative open source software development company, in July 2011 and fosters collaborative community development of ITK. He facilitates the application and delivery of ITK through next-generation web technologies, lower the barrier to developer entry, enables rapid prototyping with scientific Python, and improves performance for Big Data applications.

Dr. McCormick's algorithmic research currently focuses on developing novel, physics-based ultrasound image formation and analysis software that leverages machine learning algorithms to create new imaging modes on low-cost, portable systems. while advancing high performance algorithms.