About Me
Current Role#
As Head of AI Engineering at harrison.ai, I lead teams developing breakthrough AI solutions that transform healthcare delivery across radiology and pathology. My work has created impact at scale—from pioneering Harrison.rad.1 (radiology's first foundation model) to deploying AI systems serving millions of patients in over 1,000 healthcare facilities worldwide.
I believe that "Transforming a formidable vision into reality by serving the team with empathy, courage, and action is leadership." This philosophy guides my approach to solving complex challenges through collaborative innovation. Whether developing regulatory-cleared medical AI solutions, authoring "Deep Learning at Scale" (O'Reilly), or mentoring the next generation of technologists, I remain focused on creating technology that genuinely improves lives.
My career spans pioneering research, enterprise AI scaling at Nearmap, and now healthcare transformation—all united by a commitment to technical excellence, responsible innovation, and building diverse teams that challenge each other toward better outcomes.
Featured Publications & Books#
Books#
- Deep Learning at Scale: At the Intersection of Hardware, Software, and Data, 2024 by O'reilly Media
- Curious Cassie's Beach Ride Quest 2023
- IBM Redbooks: Creating Plugins for Lotus Notes, Sametime, and Symphony. IBM RedBook 2011
- Face-Off: collection of 21 self-composed poems. Poems, 2009
Key Publications#
- Can a Machine Learn from Radiologists' Visual Search Behaviour and Their Interpretation of Mammograms—a Deep-Learning Study. Journal of Digital Imaging 2019
- Missed cancer and visual search of mammograms: what feature-based Machine Learning can tell us that deep-convolution learning cannot. SPIE Medical Imaging 2019
- Can digital breast tomosynthesis perform better than standard digital mammography work-up in breast cancer assessment clinic? European Radiology 2018
- A deep (learning) dive into visual search behaviour of breast radiologists. SPIE Medical Imaging 2018
- Modeling visual search behavior of breast radiologists using a deep convolution neural network. SPIE Journal of Medical Imaging, 2018
Patents#
Education & Research#
Education#
- University of Sydney, 2019, Doctor of Philosophy. (Medical Image Optimisation and Perception/Breast Cancer/Machine Learning/Radiology)
- University of Sydney, 2015, Master of Applied Science (by research), Medical Image Optimisation and Perception.
- Harcourt Butler Technological Institute, Kanpur, India, 2007, Bachelor of Technology (BTech) Computer Science and Engineering.
Thesis#
Additional Publications#
Academic Publications#
- Modelling the interpretation of digital mammography using high order statistics and deep machine learning. University of Sydney, 2018
- Fixated and Not Fixated Regions of Mammograms A Higher-Order Statistical Analysis of Visual Search Behavior. Academic radiology 2017
- The role of digital breast tomosynthesis in the breast assessment clinic: a review. Journal of Medical Radiation Science, 2017
- Implementation and value of using a split-plot reader design in a study of digital breast tomosynthesis in a breast cancer assessment clinic. SPIE Medical Imaging 2015
Technical Publications#
- Automated voice marking of a data/voice streams basing on end users profile and related data. ip.com 2012
- Folksonomic approach to security systems. ip.com 2011
- System and method to automatically provide optimal content based on vision and eye movement. ip.com 2011
- Mechanism to conduct a whiteboard based conference session using gyroscopic enabled mobile devices. ip.com 2011
- Acceptability indicators in emails. ip.com 2011
- An optimized human face detection and feature extraction algorithm. ip.com 2010
Teaching & Courses#
Published Courses#
- Reproducible Deep Learning is published on O'reilly platform as an interactive katacoda scenario series. It is four parts course: