• Source: Scopus
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Personal profile

Research Biography

I am an analyst, computer scientist, and software engineer with a primary research focus in artificial intelligence and integrated health systems. My current research explores digital systems implementations to support clinical care and service innovation through the operationalisation of healthcare data and information. These approaches utilise best practices in both high-performance analytics and computational systems engineering. I am also the Managing Director of HeartAI Pty Ltd, a modern data and analytics platform provider that is currently operational within the South Australian health system. The HeartAI team hopes to develop ourselves as core service providers and foster relationships with the healthcare system, academia, industry, and the greater community.

Research Interests

My research interests include:

- Computational systems architecture: Lagom, Akka, OpenShift, Docker, Kubernetes, OpenStack, microservices architecture, reactive systems, actor-based systems, synchronous and asynchronous messaging, containerised systems, virtualised systems, distributed systems.

- Computational networks and security: OPNsense, Keycloak, OAuth2, OpenID Connect, Akka HTTP, secure design, identity management, access management, authentication and authorisation, access token systems, service isolation, network virtualisation.

- Data architecture: Cassandra, PostgreSQL, MongoDB, Hadoop, Spark, Kafka, Akka Streams, Slick, database architecture, relational and non-relational databases, data transformation, data streaming, data integrity, database replication and sharding methods, functional relation mapping.

- Software engineering and operations: Scala, Java, C++, C, Fedora, SLF4J, Vim, i3, IntelliJ IDEA, Git, LaTeX, Overleaf, functional programming, declarative programming, statically typed languages, test- and behaviour-driven design, capacity development planning.

- Application design: Play Framework, Shiny, Scala.js, Node.js, reactive web applications, dashboard design.

- Artificial intelligence systems: Causal probabilistic networks, generative models, learning theory, natural language processing, computational linguistics, deep learning, reinforcement learning.

- Statistical methodology: R, Stan, causal inference, decision theory, multilevel analysis, survival analysis, longitudinal analysis, missing data methods, spline methods, kernel methods, experiment design.

- Data science: Model creation and assessment, supervised and unsupervised learning, discriminative models, model validation and regularisation, model performance methods, data visualisation and reporting, tidyverse, ggplot2, knitr.


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