Professional History

My Experience

PhD Student

February 2016 - November 2019

• Completed PhD on the application of deep learning techniques to radio astronomy data

• Self-taught in machine learning, particularly neural networks and deep learning, using available

  online datasets and tools, in the Python language

• Development of software and publication of two papers in MNRAS and one submitted paper to

  Galaxies:
  1. Deep convolutional neural network to classify images from the Radio Galaxy Zoo
  2. Comparison between traditional deep neural network approach against more recently developed

  Capsule networks. Github link to code:

  https://github.com/vlukic973/RadioGalaxy_Conv_Caps/tree/master
  3. Using convolutional autoencoders for source-finding in simulated SKA radio astronomical data

  Github link to code: https://github.com/vlukic973/AutoSource

• Travel to London to meet with collaborators

• Presenting research in London AI in CERN and SKA workshop, GLOW in Jena and LOFAR

  meeting in Leiden

• Presenting results at meetings with PIER machine learning group

Research assistant

November 2011 - September 2015

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• Research Technician in Bioinformatics Division 2011-2014 (Dr Melanie Bahlo)

• Research Technician in Population Health and Immunity Division Jan 2015- Sep 2015 (Dr

  Melanie Bahlo)

• Implementing software algorithms and statistics to analyse genetic data such as MPS and SNP

  chip data

• Running NGS pipelines on mainly exome-sequencing data of individuals in pedigrees with rare

  diseases to identify rare potentially disease-causing variants

• Whole-genome sequencing projects

• Researching In silico gene prioritisation using Allen Human Brain Atlas data to identify

  potentially co-expressed genes. This resulted in the BrainGEP package being developed in

  R. http://bioinf.wehi.edu.au/software/BrainGEP/

• Presented at the WEHI Bioinformatics seminar on the topic of In-silico gene prioritisation

• Writing reports for collaborators and presenting results following the conclusion of data analysis

• Development of R code to prioritise variants following the conclusion of an MPS pipeline, as

  well as showing the segregation of variants of interest in a pedigree

• Presentations at multiple lab meeting talks on the analysis and future direction of projects

• Supervised a year 10 work experience student and co-supervised an intern

• Fortnightly lab meeting organisation

Lab demonstrator

March 2009 - November 2011

1. 2009 – 2011 University of Melbourne

   • Laboratory Demonstrator

   • Demonstrating in the areas of mechanics, rotation, waves, fluids, optics and radiation

   • Teaching theory of each individual practical every week - writing outline with equations on

     whiteboard and addressing it to the class

   • Showing students how to set out a scientific practical (Aim, apparatus, method, results,

     discussion, conclusion)

   • Explaining theory in more detail, answering student questions, checking that results are

     appropriate, assist students with experimental setup and operation of equipment

   • Marking labs and provide feedback, submit results online

   • Preparing material for each weeks lab class