Dr Alina Bialkowski
Dr

Alina Bialkowski

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+61 7 336 53997

Background

Dr Alina Bialkowski is a computer vision & machine learning researcher developing interpretable machine learning models to increase the performance and transparency of Artificial Intelligence (AI) decision-making. Her research interests include quantifying and extracting actionable knowledge from data to solve real-world problems and giving human understanding to AI models through feature visualisation and attribution methods. She has applied these techniques to various multi-disciplinary applications such as medical imaging (including imaging strokes in the brain using the new sensing modality of electromagnetic imaging), modelling human attention in driving, intelligent transport systems (ITS), intelligent surveillance, and sports analytics.

Dr Bialkowski holds a PhD and BEng (Electrical Engineering) from the Queensland University of Technology, Australia. Her doctoral studies were in characterising group behaviours from visual and spatio-temporal data to enhance statistics and visualisation in sports analytics as well as intelligent surveillance systems. She spent a year at Disney Research Pittsburgh where she developed techniques to automatically analyse team sports, followed by 2.5 years as a postdoctoral researcher at the University College London, developing deep neural networks to better understand human perception and attention in driving, before joining UQ in late 2017.

The impact of her research is evidenced by the high number of citations to her work (>1600 citations and an h-index of 20 according to Google Scholar) and awards including a best paper prize in 2017 at WACV (a top computer vision conference). In addition to high impact journals and conferences, her work has resulted in 6 international patents filed with Disney Research, Toyota Motor Europe, University College London, and The University of Queensland.

Availability

Dr Alina Bialkowski is:
Available for supervision
Media expert

Fields of research

Information and Computing Sciences

Qualifications

  • Bachelor (Honours), Queensland University of Technology
  • Doctor of Philosophy, Queensland University of Technology

Research interests

  • Machine Learning

    Developing interpretable or explainable models to increase trust and transparency of computer-based decisions.

  • Computer Vision

    Developing models of visual information using image processing and feature representation learning approaches such as deep learning to perform predictions on data.

  • Sensors

    Utilising sensors such as cameras and antennas/electromagnetic sensors to enable remote sensing and non-invasive imaging.

Search Professor Alina Bialkowski’s works on UQ eSpace

51 works between 2009 and 2025
All (51) Journal Article (17) Other Outputs (3) Conference Publication (28) Book Chapter (3)

1 - 20 of 51 works

2025

Other Outputs

Older Australians’ experiences navigating digital identity security

Bingley, William, Bialkowski, Alina, Gillespie, Nicole, Haslam, Alex, Ko, Ryan, Liddle, Jacki, Worthy, Peter and Wiles, Janet (2025). Older Australians’ experiences navigating digital identity security. St Lucia, QLD, Australia: The University of Queensland. doi: 10.14264/1c5a3bb

Older Australians’ experiences navigating digital identity security

2025

Journal Article

Conditional synthetic signal generation for microwave head imaging using diffusion models

Lai, Wei-chung, Bialkowski, Alina, Guo, Lei, Bialkowski, Konstanty and Abbosh, Amin (2025). Conditional synthetic signal generation for microwave head imaging using diffusion models. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 1-12. doi: 10.1109/jerm.2025.3581576

Conditional synthetic signal generation for microwave head imaging using diffusion models

2025

Journal Article

Integrated boundary-overlap-size metric for local assessment of deep learning methods in medical microwave imaging

Xue, Fei, Guo, Lei, Bialkowski, Alina and Abbosh, Amin M. (2025). Integrated boundary-overlap-size metric for local assessment of deep learning methods in medical microwave imaging. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 9 (2), 229-239. doi: 10.1109/jerm.2024.3485250

Integrated boundary-overlap-size metric for local assessment of deep learning methods in medical microwave imaging

2024

Journal Article

Clutter removal for microwave head imaging via self-supervised deep learning techniques

Lai, Wei-chung, Guo, Lei, Bialkowski, Konstanty, Abbosh, Amin and Bialkowski, Alina (2024). Clutter removal for microwave head imaging via self-supervised deep learning techniques. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 8 (4), 384-392. doi: 10.1109/jerm.2024.3409846

Clutter removal for microwave head imaging via self-supervised deep learning techniques

2024

Conference Publication

Evaluation of fully convolutional networks for dielectric profile reconstruction in medical microwave imaging

Xue, Fei, Guo, Lei, Bialkowski, Alina and Abbosh, Amin (2024). Evaluation of fully convolutional networks for dielectric profile reconstruction in medical microwave imaging. 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), Firenze, Italy, 14-19 July 2024. Piscataway, NJ, United States: IEEE. doi: 10.1109/ap-s/inc-usnc-ursi52054.2024.10686390

Evaluation of fully convolutional networks for dielectric profile reconstruction in medical microwave imaging

2024

Journal Article

Clinical electromagnetic brain scanner

Abbosh, Amin, Bialkowski, Konstanty, Guo, Lei, Al-Saffar, Ahmed, Zamani, Ali, Trakic, Adnan, Brankovic, Aida, Bialkowski, Alina, Zhu, Guohun, Cook, David and Crozier, Stuart (2024). Clinical electromagnetic brain scanner. Scientific Reports, 14 (1) 5760, 1-16. doi: 10.1038/s41598-024-55360-7

Clinical electromagnetic brain scanner

2024

Journal Article

Markerless motion capture provides accurate predictions of ground reaction forces across a range of movement tasks

Lichtwark, Glen A., Schuster, Robert W., Kelly, Luke A., Trost, Stewart G. and Bialkowski, Alina (2024). Markerless motion capture provides accurate predictions of ground reaction forces across a range of movement tasks. Journal of Biomechanics, 166 112051, 112051. doi: 10.1016/j.jbiomech.2024.112051

Markerless motion capture provides accurate predictions of ground reaction forces across a range of movement tasks

2024

Conference Publication

CaMU: Disentangling Causal Effects in Deep Model Unlearning

Shen, Shaofei, Zhang, Chenhao, Bialkowski, Alina, Chen, Weitong and Xu, Miao (2024). CaMU: Disentangling Causal Effects in Deep Model Unlearning. 2024 SIAM InternationalConference on Data Mining (SDM'24), Houston, TX United States, 18 - 20 April 2024. Philadelphia, PA United States: Society for Industrial and Applied Mathematics Publications. doi: 10.1137/1.9781611978032.89

CaMU: Disentangling Causal Effects in Deep Model Unlearning

2024

Journal Article

Transfer deep learning for dielectric profile reconstruction in microwave medical imaging

Xue, Fei, Guo, Lei, Bialkowski, Alina and Abbosh, Amin M. (2024). Transfer deep learning for dielectric profile reconstruction in microwave medical imaging. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 8 (4), 344-354. doi: 10.1109/jerm.2024.3402048

Transfer deep learning for dielectric profile reconstruction in microwave medical imaging

2024

Conference Publication

Label-agnostic forgetting: a supervision-free unlearning in deep models

Shen, Shaofei, Zhang, Chenhao, Zhao, Yawen, Chen, Weitong, Bialkowski, Alina and Xu, Miao (2024). Label-agnostic forgetting: a supervision-free unlearning in deep models. 12th International Conference on Learning Representations, ICLR 2024, Vienna, Austria, 7-11 May 2024. Vienna, Austria: International Conference on Learning Representations, ICLR.

Label-agnostic forgetting: a supervision-free unlearning in deep models

2023

Journal Article

Training universal deep-learning networks for electromagnetic medical imaging using a large database of randomized objects

Xue, Fei, Guo, Lei, Bialkowski, Alina and Abbosh, Amin (2023). Training universal deep-learning networks for electromagnetic medical imaging using a large database of randomized objects. Sensors, 24 (1) 8, 1-8. doi: 10.3390/s24010008

Training universal deep-learning networks for electromagnetic medical imaging using a large database of randomized objects

2023

Conference Publication

Using social sensing to validate flood risk modelling in England

Joyce, Joshua, Arthur, Rudy, Fu, Guangtao, Bialkowski, Alina and Williams, Hywel (2023). Using social sensing to validate flood risk modelling in England. 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, 28 November - 1 December 2023. Singapore, Singapore: Springer Nature Singapore. doi: 10.1007/978-981-99-8391-9_8

Using social sensing to validate flood risk modelling in England

2023

Conference Publication

Beyond model accuracy: identifying hidden underlying issues in chest x-ray classification

Wainwright, Richard, Wang, Danny, Layton, Harrison and Bialkowski, Alina (2023). Beyond model accuracy: identifying hidden underlying issues in chest x-ray classification. 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, 28 November - 1 December 2023. Singapore, Singapore: Springer Nature Singapore. doi: 10.1007/978-981-99-8388-9_43

Beyond model accuracy: identifying hidden underlying issues in chest x-ray classification

2023

Journal Article

Enlarging the model of the human at the heart of human-centered AI: a social self-determination model of AI system impact

Bingley, William J., Haslam, S. Alexander, Steffens, Niklas K., Gillespie, Nicole, Worthy, Peter, Curtis, Caitlin, Lockey, Steven, Bialkowski, Alina, Ko, Ryan K.L. and Wiles, Janet (2023). Enlarging the model of the human at the heart of human-centered AI: a social self-determination model of AI system impact. New Ideas in Psychology, 70 101025, 1-12. doi: 10.1016/j.newideapsych.2023.101025

Enlarging the model of the human at the heart of human-centered AI: a social self-determination model of AI system impact

2023

Journal Article

An explainable deep learning method for microwave head stroke localization

Lai, Wei-chung, Guo, Lei, Bialkowski, Konstanty and Bialkowski, Alina (2023). An explainable deep learning method for microwave head stroke localization. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 7 (4), 336-343. doi: 10.1109/jerm.2023.3287681

An explainable deep learning method for microwave head stroke localization

2023

Conference Publication

The effect of training data quantity on Monte Carlo Dropout uncertainty quantification in deep learning

Cusack, Harrison and Bialkowski, Alina (2023). The effect of training data quantity on Monte Carlo Dropout uncertainty quantification in deep learning. 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, QLD, Australia, 18-23 June 2023. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ijcnn54540.2023.10191327

The effect of training data quantity on Monte Carlo Dropout uncertainty quantification in deep learning

2023

Journal Article

Where is the human in human-centered AI? Insights from developer priorities and user experiences

Bingley, William J., Curtis, Caitlin, Lockey, Steven, Bialkowski, Alina, Gillespie, Nicole, Haslam, S. Alexander, Ko, Ryan K.L., Steffens, Niklas, Wiles, Janet and Worthy, Peter (2023). Where is the human in human-centered AI? Insights from developer priorities and user experiences. Computers in Human Behavior, 141 107617, 1-8. doi: 10.1016/j.chb.2022.107617

Where is the human in human-centered AI? Insights from developer priorities and user experiences

2023

Journal Article

Stroke localization using multiple ridge regression predictors based on electromagnetic signals

Gao, Shang, Zhu, Guohun, Bialkowski, Alina and Zhou, Xujuan (2023). Stroke localization using multiple ridge regression predictors based on electromagnetic signals. Mathematics, 11 (2) 464, 464. doi: 10.3390/math11020464

Stroke localization using multiple ridge regression predictors based on electromagnetic signals

2023

Journal Article

Brain injury localization and size estimation using electromagnetic symmetric crossing lines method

Zhu, Guohun, Bialkowski, Alina, Crozier, Stuart, Guo, Lei, Nguyen, Phong, Stancombe, Anthony and Abbosh, Amin (2023). Brain injury localization and size estimation using electromagnetic symmetric crossing lines method. IEEE Transactions on Instrumentation and Measurement, 72 2521011, 1-1. doi: 10.1109/tim.2023.3295014

Brain injury localization and size estimation using electromagnetic symmetric crossing lines method

2023

Book Chapter

Words can be confusing: stereotype bias removal in text classification at the word level

Shen, Shaofei, Zhang, Mingzhe, Chen, Weitong, Bialkowski, Alina and Xu, Miao (2023). Words can be confusing: stereotype bias removal in text classification at the word level. Advances in knowledge discovery and data mining. (pp. 99-111) edited by Hisashi Kashima, Tsuyoshi Ide and Wen-Chih Peng. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-33383-5_8

Words can be confusing: stereotype bias removal in text classification at the word level

Current funding

  • 2024 - 2029
    BioMotionAi - Precision clinical care for people with musculoskeletal pain (MRFF NCRI grant administered by Griffith University)
    Griffith University
    Open grant
  • 2024 - 2027
    Next-Generation Solvers for Complex Microwave Engineering Problems
    ARC Discovery Projects
    Open grant

Past funding

  • 2021 - 2022
    AI Architectures for On-board Processing
    SmartSat CRC
    Open grant

Availability

Dr Alina Bialkowski is:
Available for supervision

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Available projects

  • AgriTwins Project 5: Implementation of Digital Twin Technology for Environmental Monitoring and Sustainable Land Use Management in Bega Valley

    PhD Project 5: Implementation of Digital Twin Technology for Environmental Monitoring and Sustainable Land Use Management in Bega valley

    With Bega Group & Regional Circularity Co-operative

    Description:

    This project aims to apply digital twin technology to map and monitor land use in the production region of Bega Valley focusing on greenhouse gas (GHG) monitoring and enhancing environmental sustainability. This could include, but not exclusively, monitoring of natural capital (carbon and biodiversity), soil moisture, river flows, sequestration, etc.

    Utilising data from satellite imagery, drones, sensors, weather stations and any other remote sensing techniques, the digital/quantum twin will integrate all forms of available data with land topography parameters, soil health data, and microclimate data to provide a dynamic and comprehensive representation of the region.

    This approach will enable better decision-making and environmental stewardship, enhancing both productivity and sustainability in the Bega Valley.

    Objectives:

    1. Develop a high-resolution digital twin of the region for land use classification.
    2. Monitor and analyse land topography, resource utilisation, and environmental impacts.
    3. Optimise land management practices to enhance sustainability and productivity.
    4. Facilitate with environmental regulations, to support stakeholder collaboration and informed decision-making.

    Deliverables:

    1. Comprehensive digital/quantum twin model of the region, incorporating detailed land use classifications.
    2. Real-time environmental monitoring dashboard providing up-to-date insights into resource utilisation and environmental impacts.
    3. Optimisation and decision support tools to aid in sustainable land management practices.
    4. Reports documenting alignment with environmental and regulatory standards.
    5. Training and engagement materials to educate stakeholders and promote active participation in sustainable practices.

    If interested please email me at alina.bialkowski@uq.edu.au with the subject containing AgriTwins

Supervision history

Current supervision

  • Doctor Philosophy

    Using deep learning to improve emergency response in natural hazard management

    Principal Advisor

  • Doctor Philosophy

    Robust Deep Learning Models for Microwave Imaging

    Principal Advisor

    Other advisors: Dr Lei Guo

  • Master Philosophy

    Experience Saturation: Quantifying Demotivation and Disengagement

    Associate Advisor

    Other advisors: Professor Julie Henry, Dr Nell Baghaei

  • Master Philosophy

    Non-Invasive Abundance Monitoring of Captive Mala (Lagorchestes hirsutus) Using Proximal Remote Sensing

    Associate Advisor

    Other advisors: Associate Professor Diana Fisher, Dr Lorna Hernandez Santin

  • Master Philosophy

    Explainable decision support for skin cancer detection using machine learning

    Associate Advisor

    Other advisors: Professor Tim Miller

  • Doctor Philosophy

    Domain Adaptation in Causality Views

    Associate Advisor

    Other advisors: Dr Miao Xu

  • Doctor Philosophy

    Electromagnetic Solver Using Physics-Guided Deep Neural Network

    Associate Advisor

    Other advisors: Dr Lei Guo, Professor Amin Abbosh

  • Doctor Philosophy

    Minimally Invasive Blood Analysis Using Data-Driven Electromagnetic Techniques

    Associate Advisor

    Other advisors: Associate Professor Konstanty Bialkowski, Dr Lei Guo, Professor Amin Abbosh

Completed supervision

Enquiries

Contact Dr Alina Bialkowski directly for media enquiries about:

  • Artificial Intelligence
  • Machine Learning

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