Steefan Contractor

Steefan Contractor

Post-doctoral Research Associate (Research Data Scientist)

UNSW Data Science Hub, School of Mathematics and Statistics, The Univesrsity of New South Wales

I am a research data scientist with a doctorate in climate science and nearly five years of post-doctorate experience as a trans-disciplinary statistical and deep learning consultant at the University of New South Wales (UNSW). I have experience with statistical modeling, computer vision, and AI applications in the fields of climate science and remote sensing, and also natural language processing of large texts in law and politics. I also like to make complex concepts simple by creating interactive dashboards. Refer to my list of featured projects for more details.

In my spare time I like to ride bicycles, climb rocks and ferment food and drinks.

Interests
  • Generative Artificial Intelligence
  • AI and Computer Vision Applications in Climate Sciences and Remote Sensing
  • Bayesian Modeling with Stochastic Variational Inference
  • Spatiotemporal Modeling and Timeseries Forecasting
  • Quantum Machine Learning
Education

  • PhD in Climate Science, 2019

    Climate Change Research Centre, The University of New South Wales

  • Honours in Physics, 2012

    ARC Centre of Excellence for Quantum Computation and Communication Technology, The University of New South Wales

  • BSc (Adv.) in Physics, 2012

    School of Physics, The University of New South Wales

Projects

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All Deep Learning Natural Language Processing Computer Vision Data Visualization
Sea Ice Segmentation

We use deep learning and computer vision to detect individual ice floes in aerial imagery with the aim to understand the frequency distribution of sea ice by their area.

Photo by Cassie Matias on Unsplash

Sea Ice Segmentation
Is It Hot Right Now?
Are temperatures in Australia right now anomalously warm or cold compared to the past? - A website that aims to demystify climate change by answering this question with simple visualisations and statistics. A personal initiative with fellow PhD colleagues Mat Lipson and James Goldie at the Climate Change Research Centre, UNSW."
Is It Hot Right Now?
Gap-filling of Ocean Temperature Timeseries
Probabilistic deep learning (bidirectional LSTMs) with monte-carlo dropout for filling up to three month long gaps in daily temperature observations around the Sydney harbour and the East coast of Australia. As part of a postdoctoral role in the UNSW coastal oceanography group, part of the Integrated Marine Observing System Australia.
Gap-filling of Ocean Temperature Timeseries
Open Politics

Leveraging large language models (BERT) for natural language processing of social media posts by Australian MPs to detect corruption. In collaboration with Sean Johnson of Openpolitics.

Photo by Aditya Joshi on Unsplash

Open Politics
Australian Legislation Amendment Frequency

An analysis of the frequency of amendments of the twenty largest Federal legislative bills since their inception. This project involved the automated acquisition of the documents and natural language processing of nearly twelve gigabytes of text data. The project was led by A/Prof Lisa Crawford of the Law School, University of Sydney.

Photo by Tingey Injury Law Firm on Unsplash

Australian Legislation Amendment Frequency
Kelvin Wake Detection

We use fluid dynamics to simulate realistic images of the ocean surface with the triangular wave patterns behind moving objects (kelvin wakes) in water overlayed on wind generated waves. A deep learning (u-net) algorithm was trained to detect the presence absence of the kelvin wakes in the images. The prediction skill is further improved with the use of diffusion models to improve the signal to noise ratio between the kelvin wakes and the wind generated waves. This project was in collaboration with Naval Group Australia.

Photo by Martin Adams on Unsplash

Kelvin Wake Detection
UK/Aus Space Bridge
A novel application of passive GPS signals (GNSS-R) picked up by cube satellites to detect the location and age of Antarctic sea ice. We conducted an intercomparison of explainable machine learning and deep learning (tabnet) algorithms to understand which GNSS-R variables are used by the models with the highest ice detection skill. In collaboration with Spire Global, thanks to the UK-Australia Spacebridge grant organised by SmartSat CRC.
UK/Aus Space Bridge

Contact

Feel free to get in touch via my socials or fill out the contact form below to send me an email.

  • UNSW Data Science Hub, Anita Lawrence Building H13, East Wing, Level 1, RC1050, UNSW Sydney, NSW 2052