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Mr Ben Leighton

Software Engineer


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Ben maintains an enthusiasm for thinking about how the world works and appreciates the opportunity to channel this in his work in science and with CSIRO. Through science he hopes the world can become a more equitable, sustainable and fulfilling and he works towards this end. His background studies are in philosophy and biology. These days he works as a software engineer, data scientist and technical lead. Ben likes collaborating, and believe the value of what we do is maximized when it integrates a diversity of people, knowledge, skills and communities. At CSIRO he works to support science by researching and engineering solutions for scientific data and data workflows. His research includes: bridging structured and unstructured approaches to managing data, delivering high performance processing of large time series environmental sensor data, developing environmental imagery analysis pipelines and geospatial data science. Ben streamlines scientific data work flows with a focus on automation, reproducibility and novel knowledge discovery.

Research Highlights

Developed a high performance hybrid architecture for Time Series Databases delivery of rich metadata without compromising performance.

Developed Container based approaches to engineer rapidly reusable Scientific Data Processing Workflows.

Applied pretrained Word2Vec Machine Learning models to analyze metadata keywords within the environment scientific domain.

Troubleshooting and Developing Complex Geospatial Graph Database Queries


Ben has been fortunate to have worked across a wide variety of projects at CSIRO with many different colleagues.


In early 2019 Ben was brought onto the LOCI project during a critical delivery. He was pivotal in troubleshooting complex code issues and developed key aspects of queries geospatial based traversal of information resources. Apply his significant and diverse experience across geospatial and data science Ben quickly understood salient aspects of a large complex system and applied his knowledge to solve problems and reduce code complexity and adapt systems to match evolving data structures.


In the Energy Use Data Model project Ben developed previous approaches and technologies developed in AURIN to maximise value from existing science and help build a community around utilities electricity supply data exchange and processing. Using a combination of Python and Cloud PostgreSQL, Ben designed and developed a data workflow system helping the team quickly iterate on data transformations. Ben developed a novel high performance algorithms for time series analysis using canonical industry leading open source data technologies. Ben took a lead role in negotiating and developing Land and Water's role in the EUDM project in 2017.

Planned Burns

In the Planned Burns project Ben developed worked with the team to develop and employ Machine Vision and Learning to increase the effectiveness and efficiency of Landscape Management. He has developed highly literate code and processing pipelines for visualizing and quantitatively assessing the effectiveness environmental image segmentation algorithms. He created a cloud based high performance optimisation system for searching Machine Vision parameter spaces to optimise automated processing of post burn aerial imagery.

Oznome and DAMBusters

Ben was active as a SCRUM master coordinating software efforts as part of the Oznome for Land and Water and DAMBusters projects. Here, aligned with the broader Oznome initiative, he works to create a technology ecosystem to enable and improve a wide swath of data intensive Land and Water science.

Bioregional Assessments

Ben worked on a range of core Bioregional Assessments metadata and data systems. Ben coordinated the implementation of a system for managing complex business rules for licensing data. Additionally Ben has been key in investigating and solving critical technical issues meeting critical timelines to ensure the Bioregional Assessments systems are available and functional.


The eReefs project delivers a range of Great Barrier Reef datasets through an advanced geospatial portal. Ben worked on a data brokering component and developed a number of innovative features. The data brokering component provided a major advance in search capabilities by offering search across axes of "meaning", e.g allowing people to search for broader concepts from a narrow starting search term. Additional as part of this work he published well cited research investigating high performance time series processing.


Ben took a lead role in the Australian Water Resources Assessment project coordinating software developers and scientists across CSIRO and partner organisations in addition he was active in developing components of the software implementations of the AWRA models.


In the AURIN project Ben worked at a software engineer and assisted running the SCRUM process. He pioneered new approaches to reusable data analysis. Together with the team he succeeded in building a reliable and portable suite of data services for delivering standardized energy and water data to a broad researcher community. Through the AURIN work he became an early adopter and enthusiast of using Docker based approaches for reproducible, presenting this work in numerous forums including at the CSIRO CSS conference.

TIME and eWater Source

Early in his career at CSIRO he worked with scientists to implement terrain analysis algorithms in a general purpose scientific modeling environment. Extending this work he became a key software developer in the hydrological modeling platform eWater Source. This software has had major impact being used across Australia and, increasingly, internationally.


In addition to my roles across these projects as a Data Engineer, Technical Lead, and SCRUM master I maintain an role as an active Software Engineer, working with a diverse set of languages and technologies including,

  • Remote Sensing and Geospatial Data Science
  • Data Engineering
  • Linked Data
  • Python
  • Jupyter
  • PostgreSQL
  • Time Series Database
  • Docker
  • DevOps
  • Continuous Integration
  • React, JavaScript and Web Development

Other Interests

Ben is actively engaged in emerging areas at the intersection of Science and Information Technology.

Machine Learning

Ben is enthusiastic about the potential of Machine Learning to revolutionize science. Through his role as a data scientist he has researched and maintain an interest in time series processing.

Research and Science to Operations

Part of Ben's role is as as a DevOps Engineer facilitating the path through from development of software to it's successful deployment and use. Ben sees a role for a ResOps and SciOps extending the concept of DevOps to the successfully deliver of reproducible and reusable Science into operationalisation environments. Ben has a strong background in applying approaches through system containerisation.

Current Roles

  • Software Engineer

  • Software Engineer

  • Data Engineer
    Time Series Processing and Workflows

  • Environmental Data Analyst
    Spatial Data Assessments and Algorithms

  • Technical Coordinator
    Certified SCRUM Master

  • System Engineer

  • Client Liason
    Business Analysis and Development

  • Software Engineer
    Data Visualisation

Academic Qualifications

  • 2000

    BA (Philosophy)
    Australian National University

  • 2000

    BSc (Biology)
    Australian National University

  • 2015

    Machine Learning

Professional Experiences

  • 2001-2003

    Software Engineer
    Wizard Information Systems

  • 2003-2016

    Software Engineer

Achievements and Awards

  • 2016-2016

    CSIRO Medal for Impact from Science

  • 2012-2012

    Excellence in Support
    CSIRO Land and Water

  • 2007-2007

    Partnership Excellence
    CSIRO Land and Water

  • 2017-2018

    CSIRO Collaboration Award