Mr Lukah Dykes

Academic Status

College of Medicine and Public Health

place Flinders Medical Centre
GPO Box 2100, Adelaide 5001, South Australia

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 scalable systems infrastructure to support clinical care and research through the operationalisation of healthcare data and information. Our team at Health Systems Research following the direction of Professor Derek Chew has been successful in conducting cardiovascular clinical trials at a national scale and generating novel clinical research. Our approach to service implementation of these methods is to utilise best practices in both analytics and computational systems architecture. We hope to develop ourselves as core service providers and foster relationships with the healthcare system, academia, industry, and the greater community.

Qualifications

Bachelor of Science (Statistics)

Research expertise
Artificial intelligence and image processing
Cardiovascular medicine and haematology
Computer software
Information systems
Mathematics sciences
Oncology and carcinogenesis
Statistics
Stochastic Modelling
Research interests

My research interests include:

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

- Data architecture: Cassandra, MongoDB, Hadoop, Spark, Kafka, Akka Streams, database architecture, data transformation, data streaming, data redundancy and security, database replication and sharding methods.

- Software engineering and operations: Scala, Java, C++, C, Fedora, Akka, Vim, IntelliJ IDEA, Git, LaTeX, Overleaf, functional programming, declarative programming, statically typed languages.

- Application design: Play Framework, Scala.js, Node.js.

- 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.

Our team is enthusiastic about applying these and supporting techniques in a clinical and research context. Our current goal is to trial a decision support system at the clinical point of care for patients with potential acute coronary syndrome.

Supervisory interests
Artificial intelligence
Biostatistics and epidemiology
Computer systems engineering
Statistical methodology
Statistics
Publications

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