Profile

A highly competent researcher with a strong focus on deep learning, specifically experienced in neural network architecture design, algorithm implementation, and advanced AI model and system testing. I have strong communication and project planning skills within collaborative team environments. My PhD research focuses on innovative neural network methodologies, and I am pursuing a career to leverage my research, technical, and design expertise within artificial intelligence systems to unlock new possibilities in the field.

Education

Ph.D in Computer Vision October 2025

Deakin University, Victoria, Australia

Earned a Doctor of Philosophy in Information Technology with a research focus on deep learning methodologies for image classification. Awarded the Deakin University Postgraduate Research Scholarship (DUPR) in recognition of academic excellence. Contributed to the advancement of the field through multiple peer-reviewed publications in internationally recognised conferences and journals. My research focused on developing novel neural network architectures and training algorithms to enhance the performance and efficiency of deep learning models in computer vision applications.

Master of Engineering in Electronics Engineering November 2019

Macquarie University, Sydney, Australia

Attained a Master of Engineering with a specialisation in Electronics Engineering, earning the Vice-Chancellor’s International Scholarship for outstanding academic performance. Developed comprehensive expertise in analogue and digital electronics, circuit theory, and electronic system design. Strengthened technical proficiency through extensive project-based coursework, involving the design, simulation, and testing of advanced electronic systems and embedded devices. The degree fostered a solid integration of theoretical principles with practical engineering applications.

B.S. in Electrical and Electronics Engineering December 2016

BRAC University, Dhaka, Bangladesh

Awarded a Bachelor of Science in Electrical and Electronics Engineering with a specialisation in Electronics Engineering. Acquired a rigorous grounding in circuit analysis, signal processing, and digital system design. Engaged in several applied research and development projects focused on embedded systems and digital electronics, which cultivated strong analytical, programming, and hardware prototyping skills. This foundational training established a long-term academic and professional interest in intelligent electronic systems and computational technologies.

Work experience

• Graduate Researcher Teaching Fellow September 2022 - Current
  School of Information Technology, Deakin University
  Geelong, Victoria, Australia
• Firmware Engineer March 2021 - October 2021
  EMVision Medical Devices Ltd
  Sydney, New South Wales, Australia
• Testing Engineer February 2020 - December 2020
  RF Technology
  Sydney, New South Wales, Australia

Research and Publications

1. Taxonomy-guided routing in capsule network for hierarchical image classification

   K. T. Noor, W. Luo, A. Robles-Kelly, L. Y. Zhang, and M. R. Bouadjenek, “Taxonomy-guided routing in capsule network for hierarchical image classification,” Knowledge-Based Systems, vol. 329, p. 114444, Nov. 2025, doi: 10.1016/j.knosys.2025.114444.

2. A consistency-aware deep capsule network for hierarchical multi-label image classification

   K. T. Noor, A. Robles-Kelly, L. Y. Zhang, M. R. Bouadjenek, and W. Luo, ‘A consistency-aware deep capsule network for hierarchical multi-label image classification’, Neurocomputing, vol. 604, p. 128376, Nov. 2024, doi: 10.1016/j.neucom.2024.128376.

3. H-CapsNet: A capsule network for hierarchical image classification

   K. T. Noor and A. Robles-Kelly, ‘H-CapsNet: A capsule network for hierarchical image classification’, Pattern Recognition, vol. 147, p. 110135, Mar. 2024, doi: 10.1016/j.patcog.2023.110135.

4. A Bottom-Up Capsule Network for Hierarchical Image Classification

   K. T. Noor, A. Robles-Kelly, L. Y. Zhang, and M. R. Bouadjenek, ‘A Bottom-Up Capsule Network for Hierarchical Image Classification’, in 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Nov. 2023, pp. 325–331. doi: 10.1109/DICTA60407.2023.00052.

5. A Capsule Network for Hierarchical Multi-label Image Classification

   K. T. Noor, A. Robles-Kelly, and B. Kusy, ‘A Capsule Network for Hierarchical Multi-label Image Classification’, in Structural, Syntactic, and Statistical Pattern Recognition, A. Krzyzak, C. Y. Suen, A. Torsello, and N. Nobile, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2022, pp. 163–172. doi: 10.1007/978-3-031-23028-8_17.

Skills and Expertise

Software and Technical Skills

• Documentation and Office Tools: Proficient with Microsoft Office (Word, Excel, PowerPoint, Access, Project) and LaTeX for professional documentation and record keeping.
• Machine and Deep Learning:
  • Skilled in classical ML (scikit-learn) for regression, classification, clustering, and dimensionality reduction.
  • Proficient in deep learning frameworks (Keras, TensorFlow, PyTorch) for building and training neural networks.
  • Strong theoretical grounding in optimization algorithms (SGD, Adam, AdamW, RMSprop, etc.), probability/statistics, backpropagation, and advanced loss functions.
• Data Analysis and Visualization: Experienced in data wrangling and feature engineering with Pandas, NumPy, and visualisation using Matplotlib or Seaborn.
• GPU Computing and Hardware Acceleration: Working knowledge of NVIDIA CUDA (or similar) for faster model training and inference.
• Version Control and Collaboration: Proficient in Git (GitHub, GitLab) and CI/CD workflows for collaborative software development.
• Programming Languages: Working knowledge of Java, C++, Python, and MATLAB for algorithm development, data analysis, and numerical computing.
• Embedded Systems and Microcontrollers/Processors: Programmed and prototyped solutions using Arduino and Raspberry Pi, integrating sensors, actuators, and peripheral modules.
• Hardware Prototyping:
  • Designed schematics and PCBs using Altium (including BOM, pick-and-place files, 3D models).
  • Oversaw PCB fabrication, component soldering/assembly, and conducted functional testing.
• Digital Electronics and FPGA Design:
  • Implemented digital logic with Xilinx ISE, Electric VLSI, and LTspice.
  • Prototyped and validated designs on FPGA boards for functionality and timing.
• Electronics Simulation, RF and Antenna Design: Modeled electronic systems with AWR, Proteus, PSpice, and PSim; designed/analysed antennas using CST Studio.

Professional and Interpersonal Skills

• Teamwork and Collaboration: Collaborated effectively in academic and workplace settings, balancing individual tasks and group dynamics to achieve project objectives.
• Leadership: Led multiple academic projects, guiding team members and ensuring successful deliverables for high-profile events.
• Public Speaking and Presentation Skills: Delivered numerous presentations in coursework and competitions, including research findings at international conferences and workshops.
• Adaptability and Quick Learning: Quickly acquired new technical skills and processes in various roles, adapting to new environments and challenges with ease.
• Problem Solving and Critical Thinking: Skilled in diagnosing and resolving complex technical issues, ensuring optimal performance and reliability.

Research and Industry Knowledge

• Research Skills: Proficient in advanced methodologies, experimental design, data analysis, and literature reviews.
• Electronics Test Equipment: Skilled in operating and analysing data from RF spectrum analysers, vector signal analyzers, high-speed oscilloscopes, and RF test sets.
• RF Implementation and Regulatory Compliance: Hands-on experience in designing, testing, and analysing RF modules, including regression testing and certification procedures to meet regional regulatory standards.
• Project Management: Proficient in planning, coordination, and execution of academic and professional projects, ensuring timely delivery and quality outcomes.

Achievements and Additional Information

• Successfully participated and completed Empowering Innovative Leaders Program, (2024) at Deakin University.
• Certifications:
  In addition to my academic qualifications, I have completed several certifications that enhance my expertise in various domains:
  • Mathematics for Machine Learning (2025)
  • Algorithms for Battery Management Systems (2024)
  • Professional Engineer (Engineers Australia, 2023)
  • TensorFlow Developer (DeepLearning.AI, 2022)
  • IT Automation with Python (Google, 2022)
  • AI Engineering (IBM, 2021)
  • Digital Systems (UAB, 2021)
  • Specialisation in Programming the Internet of Things (UCI, 2020)
  • PCB Designing (Udemy, 2020)
• Professional Badges:
  I have earned several professional badges that demonstrate my skills and expertise in various areas. Notable badges include:

• Peer Reviewer:
  I have reviewed papers for conferences such as KSEM, AICCSA, ECAI, PAKDD and journals such as Pattern Recognition, Information fusion, Neurocomputing, Neural computing and applications, and MethodsX. The updated list of my peer reviews can be found here.

References

• Dr. Wei Luo,
  Associate Professor, School of Information Technology, Deakin University, Australia
• Prof. Antonio Robles-Kelly,
  Adjunct Professor, School of Information Technology, University of Adelaide, Australia
• Dr. Mohamed R. Bouadjenek,
  Senior Lecturer, School of Information Technology, Deakin University, Australia
• Dr. Leo Zhang,
  Senior Lecturer, School of Information and Communication Technology,Griffith University, Australia