CV
Education
- Ph.D in Computer Science (Machine Learning), University of Queensland, 2007)
- B.Sc. (Incomplete) Mathematics and Molecular Biology, University of Queensland (2002-2003).
- Assoc.Deg in Information Technology, Southern Cross University, 2001
- B.A. (Honors I) Philosophy, Newcastle University, 1999
Work experience
- 2017: Data Scientist @ Channel Nine
- Building a large scale audience forecasting engine
- MapR Haddop Cluster
- Spark, H2O, Python, R, Java & Scala
- 2015-2016: Data Scientist @ CBA
- Building models for customer acquisition, retention and fraud
- Cloudera Hadoop Cluster
- R, Scala, Hive
- 2013-2014: Data Scientist @ Big Mobile
- Building click through optimisation model
- Statistical analysis of campaigns
- Design of data driven business processes
- 2011-2013: Developed & Founder @ Bailaqui
- Content Management System in PhP and Javascript
- Building App Management Platform
- App Development with Titanium Appcelerator
- 2009-2011: Bioinformatics Postdoc @ Biotechnologisches Zentrum (BIOTEC)
- Building Machine Learning Models for protein function prediction
- Designing protein structure search algorithms
- 2008-2013: Data Mining Consultant @ UQ Business School
- Extracting an cleaning data from multiple data sets
- Designing web applictaions for internet surveys
- 2007-2009: Postdoctoral Researcher @ UQ Institute for Molecular Bioscience
- Designing and Implementing Algorithms for DNA Promoter Region Detection
- 2007-2008: Postdoctoral Researcher @ UQ Arc Centre for Complex Systems
- Designing custom machine learning methods for classification of protein sequences
- 2007-2008: Data Mining Consultant @ Volterra Pacific
- Analysis of large, incomplete and poorly specified data sets.
- 2005-2007: Research Assistant @ UQ Arc Centre for Complex Systems
- Development of C++ Tool for iterated Game Theory Simulations
- 2002-2004: Developer @ Digicon Pty Ltd
- Designing Work Flow Engine
- Implementing custom content management driven websites
- Java, Cold Fusion and JavaScript
Publications
Identifying Novel Peroxisomal Proteins
Hawkins, J., Mahony, D., Maetschke, S., Wakabayashi, M., Teasdale, R. and Boden, M., (2007). "Identifying Novel Peroxisomal Proteins" Proteins: structure, Function, and Bioinformatics. 69(3); 606-616..
Predicting Nuclear Localization
Hawkins, J., Davis, L. and Boden, M., (2007). "Predicting Nuclear Localization" Journal of Proteome Research. 6(4); 1402-1409..
The Statistical Power of Phylogenetic Motif Models
Hawkins, J., and Bailey, T.L. (2008). "The Statistical Power of Phylogenetic Motif Models." RECOMB 2008 Proceedings; 112-126. .
Assessing phylogenetic motif models for predicting transcription factor binding sites
Hawkins, J., Grant; C., Noble, W.S., and Bailey, T.L. (2009). "Assessing phylogenetic motif models for predicting transcription factor binding sites." Bioinformatics 25, i339-i347..
Studies on the inference of protein binding regions across fold space based on structural similarities
Teyra, J., Hawkins, J., Zhu, H., and Pisabarro, M. Teresa. (2010). "Studies on the inference of protein binding regions across fold space based on structural similarities." Proteins: structure, Function, and Bioinformatics. 69(3); 606-616..
NFIA Controls Telencephalic Progenitor Cell Differentiation through Repression of the Notch Effector Hes1
Michael Piper, Guy Barry, John Hawkins, Sharon Mason, Charlotta Lindwall, Erica Little, Anindita Sarkar, Aaron Smith, Randal Moldrich, Glen Boyle, Shubha Tole, Richard Gronostajski, Timothy Bailey, and Linda Richards. (2010). "NFIA Controls Telencephalic Progenitor Cell Differentiation through Repression of the Notch Effector Hes1." The Journal of Neuroscience, July 7, 2010, 30(27):9127-9139..
Reduced False Positives in PDZ Binding Prediction using Sequence and Structural Descriptors
Hawkins, J., Zhu, H., Teyra, J., and Pisabarro, M. Teresa. (2012). "Reduced False Positives in PDZ Binding Prediction using Sequence and Structural Descriptors." IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM.
Rational Structure-Based Rescaffolding Approach to De Novo Design of Interleukin 10 (IL-10) Receptor-1 Mimetics
Ruiz-Gómez, Gloria., Hawkins, John., Philipp, Jenny., Künze, Georg., Löser, Reik., Fahmy, Karim., and Pisabarro, M. Teresa. (2016) "Rational Structure-Based Rescaffolding Approach to De Novo Design of Interleukin 10 (IL-10) Receptor-1 Mimetics" PLoS One. Apr 28;11(4)
Minimum Viable Model Estimates for Machine Learning Projects
Hawkins, John. (2020) "Minimum Viable Model Estimates for Machine Learning Projects " Proceedings of the 6th International Conference on Computer Science, Engineering And Applications (CSEA 2020), Dec 18~19; 10(18)
MinViME/Minimum Viable Model Estimator
Hawkins, John. (2021) "MinViME/Minimum Viable Model Estimator" Software Impacts, Aug 01; Volume 9
Estimating Gaze Duration Error with Eye Tracking Data
Hawkins, John. (2023) "Estimating Gaze Duration Error with Eye Tracking Data" Proceedings of the 2023 5th International Conference on Image, Video and Signal Processing Pages 70-75, Mar 25, 2023
OAGRE: Outlier Attenuated Gradient Boosted Regression
Hawkins, John. (2023) "OAGRE: Outlier Attenuated Gradient Boosted Regression" Proceedings of The Fifth International Conference on Artificial Intelligence and Computational Intelligence (AICI 2024) Hanoi, Vietnam
Talks
Evolutionary Game Theory with G-functions
Talk at School of ITEE, Unviversity of Queensland, Brisbane, Australia
The role of machine learning in modelling the cell
Talk at University of Mexico City, Mexico City, Mexico
A multi-agent simulation model of fishery fleet dynamics for the Queensland coral reef line fishery
Talk at AARES: Australasian Agricultural and Resource Economics Society, Sydney, Australia
Predicting Nuclear Proteins
Talk at ACB Bionformatics Student Symposium, Auckland, New Zealand
Evolving PTS2 Motifs
Talk at Congress on Evolutionary Computation, Vancouver, Canada
The Statistical Power of Phylogenetic Motif Models
Talk at RECOMB: Research in Computational Molecular Biology, Singapore
Can Comparative Genomics Improve Transcription Factor Binding Site Prediction
Talk at Institution Presentation for the Institute for Molecular Bioscience, Brisbane, Australia
Assessing Phylogenetic Motif Models For Predicting Transcription Factor Binding Sites
Talk at Intelligent Systems for Molecular Biology, Stockholm, Sweden
Protein Structure Search Strategies
Talk at BioTec Dresden, Germany., Dresden, Germany
Being Bayesian
Talk at Sydney Data Science Meet-Up, Sydney Australia
Building Model Factories with the DataRobot API
Talk at Sydney Data Science Meet-Up, Sydney Australia
DataRobot Vs The Red Queen
Talk at Chief Data and Analytics Officer Conference, Melbourne, Australia
Introduction to Bayesian Machine Learning.
Talk at Machine Learning & Deep Learning Day, 28 Nov 2019, Sydney., Sydney Australia
Modern Machine Learning Language Models
Talk at Selenium Day, 16 Oct 2020, Sydney., Sydney Australia
Minimum Viable Model Estimates for Machine Learning Projects
Talk at CSEA 2020, Sydney Australia
Analytics Problem Framing
Talk at Australian Graduate School of Management - Guest Lecture., UNSW - Sydney Australia
Data Science in Industry
Talk at Imperial College London - Guest Lecture., London - United Kingdom
Estimating Gaze Duration Error from Eye Tracking Data
Talk at MLHMI 2023 - Paper Presentation., Singapore
Brands, Verticals & Contexts: Coherence Patterns in Consumer Attention
Talk at DSCC 2023 - Paper Presentation., Chennai - India
Evaluating Ad Creative and Web Context Alignment with Attention Measurement
Talk at ICCDA 2023 - Paper Presentation., Guiyang - China
Teaching
Service and leadership
- Volunteer teaching in the NSW Schools Primary Ethics program.