Suneeta Mall


Director, AI Model Systems at Nearmap


Suneeta is passionate about solving real-world problems with engineering, data, science, and machine learning. She holds a Ph.D. in applied science and has a computer science engineering background. She offers extensive distributed, scalable computing and machine learning experience from IBM Software Labs, Expedita, USyd, and Nearmap. She currently leads the development of Nearmap’s AI model production system that produces high-quality AI data sets and deep learning models.

Talks and Events

2022 Talk: Leveraging Domain Knowledge For Deep Learning Based Computer Vision

Deep learning models require massive amounts of data to be perform accurately. As the world is inherently interconnected, we can leverage relationships amongst identifiable objects to improve Deep Learning. For example, a shingle roof can not be tile roof, but both are roofs. So, we set ourselves a challenge: “How can we leverage the knowledge and the relationship amongst the things we see in our world to improve our data, software systems and the deep learning model?”. In this presentation, we share our experiences with knowledge graphs as a technique to model domain knowledge and reason about it to derive an embedding. We have leveraged these embeddings in numerous applications to build a more scalable, reliable and efficient AI System. The applications includes improving quality and richness of our datasets, identifying gaps in annotators’ knowledge, utilising existing data to synthesis new objects on the fly, and also to increase efficiencies of deep learning models.