I am a Consultant and Researcher with Pixel Scientia Labs, a company that I founded to provide custom solutions to image-based problems. I apply artificial intelligence to images to help science. I have more than 14 years of experience in data-driven image analysis and have worked on a variety of interdisciplinary R&D projects including digital pathology, planetary science, and automated inspection systems.
PhD in Computer Science, 2019
University of North Carolina at Chapel Hill
MS in Robotics, 2006
Carnegie Mellon University
BMath in Computer Science, 2005
University of Waterloo
I provide consulting services through my company, Pixel Scientia Labs, automating analysis of scientific image data sets. We apply cutting-edge research in image recognition and machine learning to develop custom solutions to unique and challenging image-based problems. We have extensive experience with interdisciplinary R&D projects and are focused on applications for scientific discovery such as digital pathology and planetary science.
These methods were developed for classifying larger images with intra-image heterogeneity. The class of each image is not dictated by some small to-be-identified region but by the overall appearance - perhaps the average class of smaller regions or some more complex relationship.
A Deeper Understanding of Breast Cancer
Jun. 27, 2019
AI finds new insights into molecular tumor properties using images of cells and tissue
The same technology that powers Siri and face recognition on your iPhone has also found success in medicine. By automatically analyzing microscopic images of breast tumor biopsies, artificial intelligence may one day help guide cancer treatments.
Scientists train a computer to classify breast cancer tumors
Nov. 19, 2018
In a study published in the journal NPJ Breast Cancer, researchers reported they used a form of artificial intelligence called machine learning, or deep learning, to train a computer to identify certain features of breast cancer tumors from images. The computer also identified the tumor type based on complex molecular and genomic features, which a pathologist can’t yet identify from a picture alone. They believe this approach, while still in its early stages, could eventually lead to cost savings for the clinic and in breast cancer research.