Khondaker Tasrif Noor

Khondaker Tasrif Noor is a researcher at Deakin University with a focus on Deep Learning, Computer Vision, and Machine Learning. He is a recipient of the prestigious Deakin University Postgraduate Research Scholarship (DUPR). He has published numerous papers in top-tier journals and conferences. He is also a member of the Australian Pattern Recognition Society (APRS). He is a reviewer of several top-tier journals and conferences.

Curriculum Vitae of Khondaker Tasrif Noor

Profile

A highly competent researcher with a strong focus on deep learning, experienced in designing, implementing, and testing advanced AI models and systems. Working in a close team environment, I have developed strong communication and project planning skills. Currently pursuing a PhD with a focus on innovative neural network methodologies, I seek a role that will expand my research, technical, and design expertise within the broader sphere of artificial intelligence.

Education

Ph.D in Computer vision, Deakin University, 2025 (expected)
Master of Engineering in Electronics Engineering, Macquarie University, 2019
B.S. in Electrical and Electronics Engineering, BRAC University, 2016

Work experience

Casual Academic Staff

Firmware Engineer

Testing Engineer

Research and Publications

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.

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.

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.

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.

Additional Information

Successfully participated and completed Empowering Innovative Leaders Program, (2024) at Deakin University.
Certifications:
- 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 IOT (UCI, 2020).
- PCB Designing (Udemy, 2020).
Peer Reviewer: 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.