From the Life Sciences through to Mining and Resources, AI and Deep Learning across diverse industry sectors
8:30am - 1:00pm
Learn about optimised Technology in Artificial Intelligence and Analytics for diverse industry sectors
From the Life Sciences through to Mining and Resources, learn how the application of optimised Infrastructure and Technology can accelerate the application of Artificial Intelligence and Deep Learning across diverse industry sectors.
HPE is inviting you to come and hear from acclaimed AI luminary, Dr Ettikan Kandasamy Karuppiah, Director of Developers Ecosystem at NVIDIA. He will talk through the latest applications of the technology and give real-world use cases that articulate its value in a number of industries, from the Life Science right through to Mining and Resourcing.
Dr Karuppiah’s presentation will be followed by Maryan Mehdizadeh, CSIRO researcher, part of a team who is building and validating a novel artificial intelligence (AI) based disease grading and clinical decision support system for screening and telemedicine based diagnosis of sight-threatening condition diabetic retinopathy.
- 8:30 am Introductions
- 8:45 am Dr Ettikan Kandasamy Karuppiah – Nvidia
- 10:15 am Morning tea
- 10:30 am Dr Maryam Mehdizadeh – CSIRO
- 11:15 am Panel Discussion
- 12:00 pm Networking Session
Dr Ettikan Kandasamy Karuppiah – Nvidia Speaker:
Dr. Ettikan Kandasamy Karuppiah, Director of Developers Ecosystem at NVidia, Australia/New Zealand and South East Asia assists innovators, researchers and techno-entrepreneurs to accelerate GPU adaptation for their R&D and software solutioning needs. He has direct experience and passionate in accelerated computing/software research/deep learning, design and development covering end-to-end needs. He also has published numerous publications, patents and software libraries from his past work.
Dr Maryam Mehdizadeh (CSIRO) – Guest Speaker:
Maryam and her team are building and validating a novel artificial intelligence (AI) based disease grading and clinical decision support system for screening and telemedicine based diagnosis of sight-threatening condition diabetic retinopathy (DR). The proposed system will be integrated with any fundus cameras. It will also have real-time image quality control software for colour fundus images to produce sensitivity and specificity over 90% for DR grading. The AI system will be trained based on a large repository of images obtained from diabetic patients (patients with different retinal pigmentations. They will then evaluate the system in the field at a GP clinic. They are also evaluating the system on an additional set of images (multi-ethnic) from an International Collaborator.