Tamador Mohaidat is an Assistant Teaching Faculty member in Electrical and Computer Engineering at Florida State University Panama City. Her teaching and research interests include neural networks, artificial intelligence, FPGA design, hardware accelerators, embedded systems, and VLSI. She earned her master’s degree in computer engineering from the University of Mississippi and is completing her Ph.D. in the same field. Her research has been published in IEEE journals and international conferences, with an emphasis on efficient hardware implementations of machine-learning systems.
Before joining FSU Panama City, she served as a Research and Teaching Assistant at the University of Mississippi, where she supported courses in electric circuits, microprocessor systems, microprocessor laboratories, and testing of computing systems. She also worked as a Research Assistant focusing on energy-efficient and reliable neural-network hardware accelerators.
PROFESSIONAL PROFILE
Computer Engineering researcher with expertise in neural networks, FPGA design, hardware accelerators, and VLSI systems. Published in leading IEEE journals and conferences, with contributions to efficient AI hardware. Experienced Research and Teaching Assistant with a strong record of instructing and mentoring students and serving as a Lecturer and IT Supervisor in higher education. Committed to advancing the fields of Artificial Intelligence, Deep Learning, and Hardware Design, with a demonstrated ability to contribute to both academic research and practical applications.
EDUCATION
- The University of Mississippi, Oxford, MS, USA Expected Graduation: December 2026; Ph.D. in Engineering Science with emphasis in Computer Engineering, GPA: 3.96
- The University of Mississippi, Oxford, MS, USA Graduation: May 2024; M.Sc. in Engineering Science with emphasis in Computer Engineering, GPA: 4.0
- Yarmouk University, Irbid, Jordan Graduation: June 2010; B.Sc. in Engineering Science with emphasis in Computer Engineering, GPA: 3.13
WORK EXPERIENCE
Research and Teaching Assistant: August 2023 – Present; University of Mississippi, University, MS
Courses: Electric Circuit Laboratory, Microprocessor Systems, Microprocessor Systems Laboratory, and Testing of Computing Systems
• Assisted in teaching and grading for undergraduate courses.
• Supervised and guided hands-on experiments, showcased and instructed students on lab equipment, such as oscilloscopes and multimeters.
• Supported lab instruction by guiding students in hardware/software debugging, assembly programming, and microprocessor interfacing.
• Evaluated lab reports and course assignments; provided constructive feedback to enhance student learning.
• Conducted demonstrations on FPGA-based implementations and embedded system design.
• Designed and tested algorithms for deep learning models (CNN, ANN, and perceptron variants) tailored for hardware deployment.
• Provided research assistance in computer engineering, focusing on neural networks, hardware accelerators, and VLSI design.
Research Assistant: January 2023 – August 2023[ University of Mississippi, University, MS
• Conducted research on neural network hardware accelerators with a focus on FPGA-based implementations, energy efficiency, and reliability.
• Assisted in preparing manuscripts, technical reports, and presentations related to machine learning accelerators and VLSI design.
Full-Time Lecturer: March 2013 – June 2015; Deanship of the Preparatory Year, Prince Sattam Bin Abdulaziz University, KSA
Course: Computer Skills
• Taught Computer Skills course to undergraduate students, covering fundamental computing concepts, office productivity tools, and digital literacy.
• Supervised IT infrastructure and systems within the Preparatory Year program, including network maintenance, hardware/software troubleshooting, and lab management.
PUBLICATIONS
The following peer-reviewed publications reflect my research contributions in neural networks, hardware accelerators, VLSI, and applied machine learning. They include journal articles and international conference papers.
Journal Articles:
1. T. Mohaidat and K. Khalil, “A survey on neural network hardware accelerators,” IEEE Transactions on Artificial Intelligence, vol. 5, no. 8, pp. 3801–3822, 2024.
2. J. Eda, T. Mohaidat, S. Islam Niloy, Z. Niu, M.R.K. Khan, K. Khalil, “Artificial intelligence in healthcare: A comprehensive survey on AI-driven diagnosis and patient monitoring”, Applied Soft Computing, vol. 190, 114429, ISSN 1568-4946, 2025.
3. M.R.K. Khan, T. Mohaidat, K. Khalil, “ A Lightweight Modified Adaptive UNet for Nucleus Segmentation”, Sensors (MDPI), vol.26, no. 2, ISSN 1424-8220, 2026.
4. T. Mohaidat and K. Khalil, “ A Hardware-Oriented Federated GNN Approach for Wireless Localization and Misuser Detection”, Electronics (MDPI), vol.15, no. 14, ISSN 2079-9292, 2026.
5. T. Mohaidat and K. Khalil, “Efficient Hardware Implementation of GELU Activation Function using Curvature-Based Piecewise Linear Approximation Method”, Computers and Electrical Engineering (Elsevier) – Submitted (Under peer review )
6. T. Mohaidat and K. Khalil, “A Hardware-Efficient Multi-Stage RF Drone Detection Framework with FPGA-Based Front-End Screening”, IEEE Transactions on Circuits and Systems I: Regular Papers – Submitted (Under peer review )
Conferences Papers
7. K. Khalil, T. Mohaidat, and M. Bayoumi, “Low-cost hardware design approach for long short-term memory (LSTM),” in Proc. IEEE Int. Symp. Circuits and Systems (ISCAS), pp. 1–5, 2023.
8. K. Khalil, T. Mohaidat, M. Darwich, A. Kumar, and M. Bayoumi, “Efficient deep learning approach for breast cancer detection,” in Proc. IEEE Int. Conf. Omni-layer Intelligent Systems (COINS), 2024.
9. K. Khalil, T. Mohaidat, M. Darwich, A. Kumar, and M. Bayoumi, “Efficient hardware implementation of artificial neural networks on FPGA,” in Proc. IEEE Int. Conf. AI Circuits and Systems (AICAS), 2024.
10. M. R. K. Khan, T. Mohaidat, and K. Khalil, “Predicting acute myocardial infarction using machine learning algorithms,” in Proc. Int. Conf. Artificial Intelligence, Blockchain, and IoT (AIBThings), 2024.
11. T. Mohaidat, M. R. K. Khan, and K. Khalil, “Curvature-based piecewise linear approximation method of GELU activation function in neural networks,” in Proc. Int. Conf. Artificial Intelligence, Blockchain, and IoT (AIBThings), 2024.
12. K. Khalil, S. I. Niloy, T. Mohaidat, M. R. K. Khan, and M. Bayoumi, “Efficient deep learning approach for arthritis prediction,” in Proc. Int. Conf. Artificial Intelligence, Blockchain, and IoT (AIBThings), 2024.
13. K. Khalil, M. R. Khan, T. Mohaidat, and M. Bayoumi, “Enhanced heart attack detection with neural networks,” in Proc. Int. Conf. Intelligent Systems, Blockchain, and IoT, 2024.
14. T. Mohaidat and K. Khalil, “Advancements in perceptron hardware for efficient implementation in artificial neural networks,” in Proc. 3rd IEEE Int. Conf. Computing and Machine Intelligence (ICMI), 2024.
15. K. Khalil, T. Mohaidat, M. Darwich, A. Kumar, and M. Bayoumi, “An efficient hardware design of CoAP protocol for the internet of things,” in Proc. IEEE Dallas Circuits and Systems Conf. (DCAS), pp. 1–5, 2024.
16. T. Mohaidat, A. Syed, M. Alqodah, and K. Khalil, “Efficient FPGA implementation of feedback perceptron for hardware acceleration,” in Proc. 3rd IEEE Int. Conf. Computing and Machine Intelligence (ICMI), 2024.
17. T. Mohaidat, Z. Niu, A. Syed, and K. Khalil, “Enhancing hardware neural networks with self-healing perceptron design,” in Proc. 3rd IEEE Int. Conf. Computing and Machine Intelligence (ICMI), 2024.
18. K. Khalil, T. Mohaidat, A. Sherif, and M. Bayoumi, “Hierarchical fault-tolerant NoC architecture for reliable communication,” in Proc. IEEE Int. Symp. Embedded Multicore/Many-core Systems-on-Chip (MCSoC), 2024.
19. T. N. Mohaidat, Efficient design of neural network hardware accelerator for enhanced performance in deep learning systems, Master’s Thesis, University of Mississippi, 2024.
20. J. Eda, T. Mohaidat, S. I. Niloy, M. R. K. Khan, Z. Niu, and K. Khalil, “Diabetes management using machine learning and communication protocol technology,” in Proc. 4th IEEE Int. Conf. Computing and Machine Intelligence (ICMI), 2025.
21. T. Mohaidat, S. I. Niloy, M. R. K. Khan, J. Eda, Z. Niu, A. Sherif, and K. Khalil, “RF-based UAV detection and classification using ANN models,” in Proc. 4th IEEE Int. Conf. Computing and Machine Intelligence (ICMI), 2025.
22. S. I. Niloy, M. R. K. Khan, T. Mohaidat, Z. Niu, J. Eda, and K. Khalil, “Machine learning-based classification of kidney cancer subtypes using MiRNA-seq data,” in Proc. 4th IEEE Int. Conf. Computing and Machine Intelligence (ICMI), 2025.
23. M. R. K. Khan, T. Mohaidat, S. I. Niloy, Z. Niu, J. Eda, and K. Khalil, “An advanced machine learning framework for multi-label classification of heart disease complications,” in Proc. 4th IEEE Int. Conf. Computing and Machine Intelligence (ICMI), 2025.
24. Z. Niu, M. R. K. Khan, S. I. Niloy, T. Mohaidat, J. Eda, and K. Khalil, “A novel LSTM and emotion-driven multimodal model for stock price prediction,” in Proc. 4th IEEE Int. Conf. Computing and Machine Intelligence (ICMI), 2025.
25. D. Agboola, T. Mohaidat, M.R. K. Khan, S. Islam Niloy, Z. Niu, K. Khalil, “Survey on Privacy-Aware Federated Learning Frameworks for Energy Systems within Smart Grids”, in Proc. 5th IEEE Int. Conf. Computing and Machine Intelligence (ICMI), 2026.
26. Mohaidat and K. Khalil, “Graph Neural Networks for AI-Enabled Wireless and IoT Networks,” in Proc. Int. Conf. Artificial Intelligence, Blockchain, and IoT (AIBThings), 2026.
To explore my published work, please refer to my Google Scholar page:
https://scholar.google.com/citations?user=NbcebrYAAAAJ&hl=en
ACADEMIC AWARDS & DISTINCTIONS
• Graduate School Summer Internship Award, University of Mississippi — Summer 2026
• Outstanding Teaching Student Award, School of Engineering, University of Mississippi — 2025
• Certificate of Thanks and Appreciation from the faculty of Applied Medical Sciences in Sattam bin Abdulaziz University, Saudi Arabia.
• Certificate of Thanks and Appreciation from the Deanship of the preparatory year, Saudi Arabia — 2025
PROFESSIONAL SERVICES
I actively contribute to the academic community by serving as a Technical Reviewing Committee Member for international conferences in artificial intelligence, computing, and systems-on-chip design.
• 38th, 39th IEEE International System-on-Chip Conference (SOCC) - (Technical Program Chair)
• 3rd, 4th, and 5th International Conference on Computing and Machine Intelligence (ICMI)
• 1st, 2nd, and 3rd International Conference on Intelligent Systems, Blockchain, and Communication Technologies (ISBCom)
• 2nd IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)
• International Conference on Cognitive Computing, Intelligence, and Data Science Applications (CCIDSA)
PROFESSIONAL ASSOCIATIONS
I am a member of the esteemed professional associations, including:
• Phi Kappa Phi Honor Society
• Institute of Electrical and Electronics Engineers (IEEE)
• Jordanian Engineers Association
SKILLS
I possess a strong technical skill set, including expertise in:
• Programming Languages: C, C++, Python, PyTorch/TensorFlow
• FPGA Design & Tools: Vivado (VHDL/Verilog)