Md Abul Bashar

About

I am a data scientist working on data-efficient deep learning: getting reliable models out of scarce labels, noisy multimodal inputs, and data distributions that shift underneath you. My research spans generative AI and large language models, text mining, and machine learning, with over 15 years of research and development experience and nine years post-PhD.

My work has been funded by the Australian Research Council, government agencies and private industry. Much of it has been industry-partnered, where the contracted deliverable is a working system rather than a paper — so a good deal of what I have built ships as a product instead of appearing in print. That has included a bias-detection model commercialised in the United States and used by Fortune 500 companies, a marketing-automation platform adopted by SAP and Oracle, and a system in operational use at QUT. Research from my group has been covered in more than 200 national and international media outlets.

I am currently a member of the QUT node of MACSYS, the ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems, where I work on biomedical relation extraction and multi-omics fusion for whole-cell modelling.

Education

Experience

Awards, Scholarships and Grants

• 2025Outstanding Achievement Award, School of Computer Science, QUT.
• 2023Commendation, EMCR Publication Excellence, QUT Centre for Data Science.
• 2021Early and Mid-Career Researcher Publication Excellence Award, QUT Centre for Data Science.
• 2019First place, HASOC'19 International Hate Speech and Offensive Content Identification Challenge.
• 2016Second-best paper award, IEEE/WIC/ACM International Conference on Web Intelligence.
• 2013–2017QUT Postgraduate Research Award ($86,285) and HDR Tuition Fee Scholarship.
• 2008New Telecommunications Service of the Year Award, Dubai (team award for the GRID).

Research income: $119,500 — eight competitive QUT Centre for Data Science seed grants, 2021–2026 ($80,000; four as lead investigator, four as co-investigator) and industry research contracts with Energy Queensland and Pallava Technologies/iShield.AI ($39,500).

Professional Activities

• Program Committee member, Australasian Data Mining Conference (AusDM) 2021 and 2025.
• Publicity Chair, Australasian Data Mining Conference (AusDM) 2020 and 2021.
• Editorial member and session chair, MIDAS 2021 (International Conference on Machine Intelligence and Data Science Applications).
• Invited session chair, IJCNN 2023 (International Joint Conference on Neural Networks), Gold Coast.
• External examiner, MPhil thesis, 2025.
• Panel member, QUT PhD confirmation and final seminars, 2017–2026.
• Reviewer for journals and conferences in machine learning and data mining.
• Guest lecturer, QUTeX executive education; online instructor for the IIITs and IIT India (GenAI for Information Extraction, 2025).

Research

Interests

• Generative AI and large language models information extraction, compliance analysis, question answering over organisational data.
• Multimodal and multi-omics fusion modality bias, uncertainty-guided meta-learning, deep kernel fusion.
• Text mining and NLP topic modelling, online abuse detection, cyber-threat intelligence.
• Anomaly detection and time series GAN-based methods for predictive maintenance.
• Label-efficient learning active learning, semi-supervised methods, transfer learning and domain adaptation.

Selected projects

Research into practice

Several of these engagements were delivered under non-disclosure agreements, so the systems exist but the papers do not.

• iShield.AI the first commercially deployed implicit bias-detection system; core model developed by me, included on the Slack App Directory and used by Fortune 500 companies.
• Robotic Marketer a world-first AI marketing-strategy automation platform, adopted by SAP, Oracle and Hungry Jack's.
• Abusive student feedback detection a system now in operational use at QUT.
• Energy Queensland Bayesian uncertainty-aware propensity-to-pay models guiding customer intervention strategies.

Publications

30 refereed journal articles, 20 refereed conference papers. Citation metrics from Google Scholar, October 2026.

Selected publications

1. Bora, A. E., Arodi, A., Zhang, D., Bannister, J., Bronzi, M., Tchango, A. F., Bashar, M. A., Nayak, R., & Mengersen, K. (2025). AIMSCheck: Leveraging LLMs for AI-assisted review of modern slavery statements across jurisdictions. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Long Papers), 9109–9135.
2. Bora, A. E., Zhang, D., Thinyane, H., Bashar, M. A., Nayak, R., & Mengersen, K. (2026). AIMS-QA: A framework for scaling the assessment of corporate modern slavery statements beyond compliance with AI-driven question answering. Data & Policy, 8, e53. Open access.
3. Bashar, M. A., & Nayak, R. (2020). TAnoGAN: Time series anomaly detection with generative adversarial networks. IEEE Symposium Series on Computational Intelligence, 1778–1785.276 citations
4. Luong, K., Nayak, R., Balasubramaniam, T., & Bashar, M. A. (2022). Multi-layer manifold learning for deep non-negative matrix factorization-based multi-view clustering. Pattern Recognition, 131, 108815.124 citations
5. Bashar, M. A., Nayak, R., & Suzor, N. (2020). Regularising LSTM classifier by transfer learning for detecting misogynistic tweets with small training set. Knowledge and Information Systems.63 citations
6. Zhang, D., Nayak, R., & Bashar, M. A. (2024). Pre-gating and contextual attention gate — a new fusion method for multi-modal data tasks. Neural Networks, 179, 106553.48 citations
7. Zhang, D., Bashar, M. A., & Nayak, R. (2025). A novel multi-modal fusion method based on uncertainty-guided meta-learning. Pattern Recognition, 158, 110993.38 citations
8. Bashar, M. A., & Nayak, R. (2025). ALGAN: Time series anomaly detection with adjusted-LSTM GAN. International Journal of Data Science and Analytics, 20(6), 5719–5737.38 citations
9. Cunningham, S., Laundon, M., Cathcart, A., Bashar, M. A., & Nayak, R. (2023). First, do no harm: Automated detection of abusive comments in student evaluation of teaching surveys. Assessment & Evaluation in Higher Education, 48(3), 377–389.36 citations
10. Bashar, M. A., & Nayak, R. (2021). Active learning for effectively fine-tuning transfer learning to downstream task. ACM Transactions on Intelligent Systems and Technology, 12(2), 1–24.30 citations
11. Zhang, D., Bashar, M. A., Nayak, R., & Cuttle, L. (2025). A novel modality contribution confidence-enhanced multimodal deep learning framework for multiomics data. BMC Bioinformatics, 26(1), 271.

Accepted, to appear

• Vu, T. L., Nguyen, M. L., Nguyen, T., Bashar, M. A., Ho, N., Nayak, R., & Drovandi, C. (accepted October 2026). Voronoi-calibrated order selection for Bayesian softmax-gated mixtures of experts. AXIOM: Foundations of Efficient Deep Learning, NeurIPS 2026 Workshop (short paper, poster). Not counted in the totals above.

Full list of all 50 peer-reviewed publications →
Also on Google Scholar · Code for several of these on GitHub.

Media and Public Engagement

Research from my group has been featured in more than 200 national and international outlets. The widest reach came from the misogyny-detection work, which was picked up across 208 distinct news domains — the large majority as AAP and IANS wire syndications of a single source story.

• Times of India
• Hindustan Times
• Brisbane Times
• Tech Xplore
• EurekAlert!
• Scimex
• Mirage News

Other coverage includes the Australian National University repatriation project, reported by QUT and the National Indigenous Times in February 2024; the abusive student-feedback work, covered by The Conversation and Campus Review in July 2022; and the MACSYS feature Bridging Biology and AI in April 2026, which describes the kinase and phosphorylation NLP pipeline.

I have also written for a general audience: Using artificial intelligence to combat misogyny on social media (Prothom Alo, June 2020, in Bengali).

Mentorship and Supervision

I have co-supervised seven higher degree research students since 2017, five of whom have graduated, two with university awards. I also mentor research assistants funded through my own grants.

I have also taught and tutored IFN580 Machine Learning, IFN509 Data Exploration and Mining, CAB330 Data and Web Analytics and INB302 IT Capstone Project at QUT.

Contact