John Hawkins is a full-stack Data Scientist and Machine Learning Engineer. He is the author of the forthcoming book “Getting Data Science Done” and a research affiliate with the Transitional AI Research Group out of UNSW. He has a background in computer science and philosophy and has been applying machine learning, statistics and other data mining techniques to a diverse range of data sets for the past 16 years.
John has built multiple python packages and a wide range of open source software. He has delivered batch scoring and real time predictive models for customer acquisition and retention, fraud, insurance claim behaviour, media viewership, ad unit interaction, protein and DNA behaviour for bio-medical science. He has deployed interactive dashboards and custom Internet applications using technologies like Python Flask, R-shiny, Docker, PhP and Java J2EE.
John’s current research project involves explainability of text and NLP machine learning models. He has an ongoing investigation into the use of Bayesian Neural Networks for time series forecasting. Previously he has developed methods for identifying functional motifs within 3D protein structures and prediction services using Neural Networks and Support Vector Machines to predict protein function and sub-cellular localisation.
Specialties: Machine Learning, Classification, Regression, Time Series and Clustering. Neural Networks, Natural Language Processing, Decision Trees, Random Forest, Gradient Boosted Methods. Bayesian Statistics, Data Mining, Predictive Models, Web Applications. Statistical Models. Python, Pandas, Numpy, JAVA J2EE. Scala. H2O. Spark.
