Research Profile

Dr. John Hawkins is the Chief Scientist with Playground XYZ and a machine learning researcher affiliated with the Transitional AI Group out of UNSW.

He has worked across multiple industries applying machine learning to business problems and published academic research in the application and analysis of machine learning systems. His current research interests are in advanced text prcocessing, model interpretation, algorithmic reasoning and quantification of uncertainty. He continues to develop multiple open source libraries for these projects in a commitment to open and reproducible data science.

Projects

Uncertainty Quantification and Media Impact in Eye Tracking Studies

Multiple streams of research aimed at understanding and improving processes for measuring eye tracking based consumer attention on digital media.

Natural Language Processing for Systematic Review in Biomedical Research

An investigation into machine learning methods for automated filtering of research papers extracted from biomedical database seraches.

Minimum Viable Model Estimation

To what extent can we quantify in advance how good a machine learning system needs to be in order to solve a problem? In this work we are building tools to estimate the required performance levels of a model in order to meet business requirements specified in terms of the minimal ROI and the expected returns for predictions of differing levels of accuracy.

Interpretable Text in Machine Learning

Modern advances in interpretable machine learning are generally less applicable to text data sources. In this work we are exploring methods to render the contributions of unstructured text data more transparent regardless of how it is being processed or the machine learning model that is used.

Data Science Project Management & Integration

Data science projects require rapid experimentation and exploration of problem spaces. This is typically enabled by using loosely coupled tools and processes. In this work we are developing simple CLI tooling for connecting data and experimental resources so that results can easily be compared and managed without undue coupling and dependency creation.

  • Projit library and command line tool: PyPI or Github

Automated Bayesian Neural Networks for Time Series Forecasting

Exploring the design of an automated Bayesian forecasting system to automate the development of time-series forecasting in a manner that provides reliable quantifictions of forecast error.

Collaborators: Dr. Rohitash Chandra

  • Github
  • PROJECT IN PLANNING – DEVELOPMENT ON HOLD

Contact

John Hawkins