Quantum Machine Learning
Hardware-efficient quantum neural networks, quantum transfer learning, and hybrid quantum–classical models for high-dimensional data.
Research Scientist · CSIRO's Data61
Dr Amena Khatun
Working at the intersection of quantum machine learning, trustworthy AI, and real-world systems, for defence, healthcare, cybersecurity, transport, and critical infrastructure.
About
Biography
Dr Amena Khatun is a Research Scientist at CSIRO. She completed her PhD in Electrical Engineering and Robotics at QUT in 2021. Her research focuses on quantum machine learning, quantum optimisation, and trustworthy artificial intelligence for real-world applications across defence, cybersecurity, healthcare, and critical infrastructure. She develops secure, scalable, and hardware-efficient quantum AI approaches for deployment on emerging quantum technologies.
Amena is currently leading and contributing to multiple projects, including a Quantum Adversarial Machine Learning project through the Advanced Strategic Capabilities Accelerator, quantum optimisation for the Brisbane 2032 Olympic and Paralympic Games, and healthcare-focused quantum AI collaborations with industry partners. Her research has been recognised through multiple awards, including the Women in AI 2025 Award, CSIRO Early Career in Engineering Award 2025, Women in IT 2024 Emerging Tech Star Award, Quantum Australia 2025 Poster Prize, and the Postgraduate Research Thesis Excellence Award.
Her work contributes to strengthening Australia's sovereign capability in quantum technologies, supporting secure, and scalable quantum AI systems for real-world environments.
Research
Areas of Expertise
Bridging fundamental quantum AI research with industry-aligned outcomes across multiple national priority domains.
Adversarial & Robust AI
Quantum adversarial machine learning, classical–quantum distillation, and robustness for security-sensitive deployments.
Quantum Optimisation
Optimisation methods for transport, logistics, and large-scale planning — including work for the Brisbane 2032 Games.
Generative AI
Quantum generative models for high-resolution medical image generation and synthetic data for healthcare analytics.
Computer Vision
Person re-identification, attention-guided generation, and multi-stream deep architectures for identification systems.
Trustworthy AI Systems
Secure, scalable AI for defence, cybersecurity, healthcare, and critical infrastructure resilience.
Career
Experience & Education
A combined view of professional roles and academic training.
Professional Experience
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Research Scientist, Quantum AI
CSIRO's Data61, Australia
Lead and contribute to national research programs at the intersection of machine learning, quantum computing, and real-world systems — including ASCA-funded quantum adversarial ML, quantum optimisation for Brisbane 2032, and partnerships with MITRE, Cleveland Clinic, DSTG, and leading Australian universities.
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Research Fellow
SAIVT Laboratory, Queensland University of Technology
Applied research in computer vision, signal processing, and multimodal ML; contributions to high-impact publications in face recognition and person re-identification.
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Academic Instructor & Advisor
CSIRO Next Generation Graduates Program
Design and deliver postgraduate masterclasses in Deep Learning, CNNs, and Quantum Machine Learning; mentor HDR candidates.
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Lecturer
Daffodil International University, Bangladesh
Undergraduate teaching in Digital Signal Processing, Computer Networks, and Electrical Circuits; curriculum design and undergraduate thesis supervision.
Education
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PhD — Computer Vision & Artificial Intelligence
Queensland University of Technology, Australia
Thesis: Deep Learning for Person Re-Identification. Developed novel multi-stream deep learning architectures, attention-guided generative models, and domain-adaptive networks for person re-identification, with publications in IEEE TIFS, Pattern Recognition, CVIU, and WACV.
Supervised by Professor Sridha Sridharan, Professor Clinton Fookes, and Associate Professor Simon Denman at the Signal Analysis and Intelligent Vision Technology (SAIVT) Laboratory, QUT — completed under the Australian Government Research Training Program.
Awards: Doctoral Thesis Excellence Award, HDR High Achiever Award, QUT Top-Up Scholarship, and Research Training Program (RTP) Scholarship.
Technical Toolkit
Programming Python, C/C++, MATLAB
ML Frameworks PyTorch, TensorFlow, Keras, Scikit-learn
Quantum ML Qiskit, PennyLane
Data & Vision OpenCV, NumPy, Pandas, Matplotlib
Highlights
Awards, Grants & Recognition
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2025
Women in AI Award — AI in Quantum and Space
Recognition for contributions to quantum and space AI.
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2025
CSIRO Early Career in Engineering Award
For outstanding early-career engineering research.
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2025
Quantum Australia Poster Prize
Awarded at the national Quantum Australia conference.
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2024
Women in Technology — Emerging Tech Star
National recognition for emerging leadership in technology.
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2024
Quantum 2032 Challenge Grant
AUD 492,903, Queensland Government — Investigator.
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2021
Doctoral Thesis Excellence Award, QUT
Plus HDR High Achiever Award and QUT Top-Up Scholarship.
Featured Work
Selected Publications
A few recent papers across quantum AI, robust ML, and computer vision.
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Quantum generative learning for high-resolution medical image generation
Machine Learning: Science and Technology, 2025
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Advanced Quantum Technologies, 2025
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Classical autoencoder distillation of quantum adversarial manipulations
Physical Review Research, 2025
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Pose-driven attention-guided image generation for person re-identification
Pattern Recognition, 2023
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End-to-end domain adaptive attention network for cross-domain person re-identification
IEEE Transactions on Information Forensics and Security, 2021
Get in touch
Contact
Open to research collaborations, HDR supervision enquiries, invited talks, and industry partnerships in quantum AI.
- Email amena.khatun@data61.csiro.au
- Affiliation CSIRO's Data61, Australia
- CSIRO Profile people.csiro.au/k/a/amena-khatun
- LinkedIn linkedin.com/in/amena-khatun-phd
- Google Scholar scholar.google.com/citations