CV

Education

• The University Of Queensland, B.Engg. Electrical and Computer Engineering, 2019
  • Graduated with Honours Class I
  • GPA: 6.438/7.0
  • Weighted GPA: 6.652/7.0
  • Graduated as an EAIT Scholar: Status awarded to the top 5% of the engineering cohort.
• Dalian Neusoft University of Information, Winter semester abroad, 2017
• The National University Of Singapore, semester abroad, 2016

Research experience

• CSIRO - Data61 - Robotics and Autonomous Systems Group: Research Engineer, July 2019 - Present
  • Developing self-supervised deep learning algorithms for cross modality and spatio-temporal datasets.
• The Queensland Brain Institute: Research Software Engineer, June 2019 - Present
  • Software engineering, information technology and implementation support for a team with 3 neurosci-entists. Our research focus is early detection of neuro-degenerative diseases.
• The University Of Queensland & CSIRO - Data61: Honours Thesis Student
  • Developed a deep learning leaf segmentation framework leveraging domain randomisation to generatesynthetic data to overcome the lack of labelled read datasets in agriculture applications.
  • Currently my published results are the state of the art in the international Leaf Segmentation Challenge(LSC).
• CSIRO - Data61 - Robotics and Autonomous Systems Group: Research Intern, November 2016 - June 2019
  • Developed multi-modal and multi-spectral machine learning image analysis algorithms for early plantdisease detection. CSIRO external project with machine learning engineers and agricultural scientists.
  • Data science responsibilities: collecting and managing experiment data. Engineering responsibilities: maintaining the plant imaging data collection rig.
• IBM: Research Intern, August 2016 - November 2016
  • Data processing, analysis and machine learning development for retinal medical images. In a team of10 focusing on human retinal disease detection.
• National University Of Singapore: Research Software Engineer, Janurary 2016 - May 2016
  • Worked with medical researchers to develop bespoke Medical image (MRI) analysis and processing software. Part of the Growing Up in Singapore Towards Healthy Outcomes project.

Work experience

• Phyto Inc: Co-founder & Head of software engineering, October 2017 - August 2017
  • A GP, physiotherapist and I co-founded a digital health platform and information service which provides health tech solutions.
• Impedimed: Electrical Engineering Intern, May 2016 - July 2016
  • Worked in a team of 5 on medical device evaluation to acquire FDA clearance.
• IntelliDesign: Work Experience Electronics Engineer, July 2015 - November 2015
  • Operated a pick and place production line and automated processes in electrical device manufacturing.

Teaching experience

• The University Of Queensland: Casual Demonstrator, July 2015 - November 2015
  • Tutor for a first year project based engineering course. Key course goals include team work, criticalthinking core engineering concepts and helping students identify their engineering specialisation.

Skills

• Programming
  • Python
  • Java
  • MATLAB
  • C
  • TensorFlow
  • PyTorch
• Software & Tools
  • High performance computing
  • LaTeX
  • UNIX
  • Computer Aided Design (CAD)

Publications

Plant Disease Detection Using Hyperspectral Imaging

Moghadam, P., Ward, D., Goan, E., Jayawardena, S., Sikka, P. and Hernandez, E., 2017, November. Plant disease detection using hyperspectral imaging. In 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1-8). IEEE.

Deep leaf segmentation using synthetic data

D. Ward, P. Moghadam, and N. Hudson. Deep Leaf Segmentation Using Synthetic Data. In Proceedings of the British Machine Vision Conference (BMVC) Workshop on Computer Vision Problems in Plant Pheonotyping (CVPPP), 2018.

Scalable learning for bridging the species gap in image-based plant phenotyping

Ward, D., & Moghadam, P. (2020). Scalable learning for bridging the species gap in image-based plant phenotyping. arXiv preprint arXiv:2003.10757.

Temporally Coherent Embeddings for Self-Supervised Video Representation Learning

Knights, J., Vanderkop, A., Ward, D., Mackenzie-Ross, O., & Moghadam, P. (2020). Temporally Coherent Embeddings for Self-Supervised Video Representation Learning. arXiv preprint arXiv:2004.02753.

Scientific citizenship

• Volunteer Reviewer
  • IEEE IoT Journal
  • Elsevier Trends In Plant Science Journal
  • Elsevier Computers and Electronics in Agriculture Journal
  • The CVPPP Workshop at CVPR 2019