About Me

Suneeta is passionate about solving real-world problems with engineering, data, science, and machine learning. She's a PhD in applied science with a computer science and engineering background. She has extensive distributed, scalable computing and machine learning experience from IBM Software Labs, Expedita, USyd, Nearmap and more recently harrison.ai.

She currently leads the AI Engineering division of harrison.ai, a clinician-led artificial intelligence medical technology company tackling some of the biggest issues in healthcare causing inequitable diagnosis today.

She believes in lifelong learning and is passionate about knowledge sharing. She is also an author for O'Reilly and writes technical blogs in her spare time.

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

• Modelling the interpretation of digital mammography using high order statistics and deep machine learning

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
• 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
• 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

Patents

• Display of information in computing devices, 2013.

Books

• Deep Learning at Scale: At the Intersection of Hardware, Software, and Data, 2024 by O'reilly Media
• IBM Redbooks: Creating Plugins for Lotus Notes, Sametime, and Symphony. IBM RedBook 2011
• Curious Cassie's Beach Ride Quest 2023
• Face-Off: collection of 21 self-composed poems. Poems, 2009

Published courses

• Reproducible Deep Learning is published on O'reilly platform as an interactive katacoda scenario series. It is four parts course:
  • Reproducible Deep Learning: Semantic Segmentation on Oxford Pets Dataset
  • Reproducible Deep Learning: Identifying the Reproducibility Challenge
  • Reproducible Deep Learning: Random Seeds and Process-Parallelism
  • Reproducible Deep Learning: Achieving 100% Reproducibility

Published blogs on external platforms:

• Suneeta@Towards Data Science