PhD (Queensland University of Technology), BSc(Hons) (University of Queensland), BSc (University of Queensland)
James M. Hogan is Associate Professor and Academic Lead for Learning and Teaching in the School of Computer Science at QUT. He holds an honours degree in mathematics from the University of Queensland and a PhD in computer science from QUT. He has received close to $2M in research funding from national competitive grants and industry and has over 100 publications, mainly in Bioinformatics, Visualisation, Machine Learning and Software Engineering and Software Engineering Education. He is a co-author of Recruitment Learning (Springer, 2010).
Details of Expertise, Research Interests and open Undergraduate and Postgraduate project topics are provided below.
Dr Hogan has had a central role in the creation of new undergraduate and postgraduate programmes at QUT over many years. In the early 2000s, he led work to modernise software engineering education to work with Agile Methodologies, and co-founded the Brisbane Agile Methodologies SIG. In 2010, he introduced what was almost certainly Australia’s first subject on Cloud Computing, with ongoing involvement of people from Amazon Web Services and Microsoft Azure, and he has more recently modernised the teaching of Web Computing. He has extensive experience as an industry presenter in Software Engineering, advanced Software Development and more recently in Cloud Computing. His Agile Software Development course material has been used in industry to train hundreds of software engineers. He has won numerous awards for teaching at university and national level, including the QUT Distinguished Teaching Award and one of the inaugural Carrick Institute Student Learning Citations from the Federal Government.
He has supervised seven research students to completion as principal supervisor.
Areas of Expertise
- Bioinformatics Visualisation
- Machine Learning and cognitive applications of Neural Networks
- Software Engineering – especially Agile Methodologies and I18N
- Web and Cloud Computing
Dr. Hogan's research interests relate mainly to Sequence Analysis and Visualisation in Bioinformatics, usually covered under the umbrella title of Making Sense of Sequences.
Much the work on Sequence Analysis is focused on representation of molecular sequences using sparse encodings and subspace embeddings to enable very rapid comparison, learning and inference. Other work has focused on rapid clustering of diverse genomic sequences using these representations. See for example:
Dhananjay Kimothi, Pravesh Biyani, James M. Hogan, Akshay Soni, Wayne Kelly.
Learning supervised embeddings for large scale sequence comparisons
PLOS One, March 2020. https://doi.org/10.1371/journal.pone.0216636
Chappell T, Geva S, Hogan J, Huygens F, Rathnayake I, Rudd S, Kelly W, Perrin D, (2018)
Rapid analysis of metagenomic data using signature-based clustering,
BMC Bioinformatics, 19, pp. 79-93
Visualisation work has focused on understanding biological network relationships at scale and using novel representations to facilitate comparison across large numbers of sequences. Dr. Hogan led a recent ARC Linkage project with the CSIRO and Microsoft Research entitled Visual Analytics for Next Generation Sequencing, which focused on understanding the language of bioinformatics visualisation. Recent publications include:
Seán I. O'Donoghue, Benedetta Frida Baldi, Susan J. Clark, Aaron E. Darling, James M. Hogan, Sandeep Kaur, Lena Maier-Hein, Davis J. McCarthy, William J. Moore, Esther Stenau, Jason R. Swedlow, Jenny Vuong, James B. Procter
Visualization of Biomedical Data
Annual Review of Biomedical Data Science 2018 1:1, 275-304.
Rittenbruch M, Vella K, Brereton M, Hogan J, Johnson D, Heinrich J, O'Donoghue S, (2021)
Collaborative Sense-making in Genomic Research: The Role of Visualisation,
IEEE Transactions on Visualization and Computer Graphics.
Some selected publications are linked below from the QUT's research data system and are rotated occasionally.
Most of my student projects relate to the Research Interests listed above. I am happy to supervise undergraduate students for individual student projects or as part of the capstone units. I have a range of projects suitable for Bachelor of Engineering thesis projects or as IT Honours research projects, and these are closely aligned with my research interests and MPhil and PhD projects.
Current project areas include:
- Novel representations for large scale sequence indexing
- Large scale clustering using parallel K-Tree and SigClust
- CAMDA Localisation and AMR Challenges
- F#-based Bioinformatics
- Visualisation and Sonification of Large-Scale biological networks.
- Exploring large collections of bacterial genomes - a 'social network' for pathogens using the Staphopia dataset
- Analysis of Squash matches - yes, squash matches like these: https://www.youtube.com/watch?v=WBLZ3-T8q7s
There is plenty of scope in these projects to find your own niche: if you are interested in creating and analysing new algorithms or finding estimates of error rates, we can do that; if you want to explore new data representations or visualisations, we can do that. And if you just want to make things run fast, we can also do that.
Please email me with your interests and academic background. Most of my students come from a strong CS or Electrical or Computer Systems Engineering background. But I am very keen to hear from students with expertise that crosses over discipline boundaries - students with skills in mathematics, fine arts and design, molecular biology and linguistics are always welcome as long as you have a good academic background and are able to think computationally. If this is you, please do get in touch.
Jim has collaborated with industry over many years with companies and organisations such as Microsoft, Red Hat, Amazon Web Services, Technology One, the CSIRO and others. He was co-founder of the Brisbane Agile Methodologies SIG and has extensive experience as an industry presenter in Software Engineering, advanced Software Development and Cloud Computing for a wide range of industry and government organisations. His Agile Software Development course material has been used in industry to train hundreds of software engineers.
- Diederich, J., Gunay, C. & Hogan, J. (2010). Recruitment Learning. Springer.
- Rittenbruch, M., Vella, K., Brereton, M., Hogan, J., Johnson, D., Heinrich, J. & O'Donoghue, S. (2022). Collaborative Sense-making in Genomic Research: The Role of Visualisation. IEEE Transactions on Visualization and Computer Graphics, 28(12), 4477–4489. https://eprints.qut.edu.au/212585
- Chappell, T., Geva, S., Hogan, J., Huygens, F., Rathnayake, I., Rudd, S., Kelly, W. & Perrin, D. (2018). Rapid analysis of metagenomic data using signature-based clustering. BMC Bioinformatics, 19, 79–93. https://eprints.qut.edu.au/129501
- Heinrich, J., Vuong, J., Hammang, C., Wu, A., Rittenbruch, M., Hogan, J., Brereton, M. & O'Donoghue, S. (2016). Evaluating viewpoint entropy for ribbon representation of protein structure. Computer Graphics Forum, 35(3), 181–190. https://eprints.qut.edu.au/220968
- Chua, X., Buckingham, L., Hogan, J. & Novichkov, P. (2015). Large scale comparative visualisation of regulatory networks with TRNDiff. Procedia Computer Science, 51(1), 713–724. https://eprints.qut.edu.au/100803
- Chan, C., Bernard, G., Poirion, O., Hogan, J. & Ragan, M. (2014). Inferring phylogenies of evolving sequences without multiple sequence alignment. Scientific Reports, 4, 1–9. https://eprints.qut.edu.au/82330
- Gordon, J., Towsey, M., Hogan, J., Mathews, S. & Timms, P. (2006). Improved Prediction of Bacterial Transcription Start Sites. Bioinformatics, 22(2), 142–148. https://eprints.qut.edu.au/7549
- Hogan, J., Diederich, J. & Finn, G. (1998). Selective Attention and the Acquisition of Spatial Semantics. New Methods in Language Processing and Computational Natural Language Learning, 235–244.