A’eashah Alhakamy | Data Visualization | Research Excellence Award

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Assoc. Prof. Dr. A’eashah Alhakamy | Data Visualization | Research Excellence Award

University of Tabuk | Saudi Arabia

Assoc. Prof. Dr. A’eashah Alhakamy is an academic leader and researcher in computer science whose work spans computer graphics, computer vision, imaging, visualization, and extended reality, with a scholarly record comprising 27 published documents, 271 citations from 250 citing documents, and an h-index of 9. She earned her Ph.D. and M.Sc. in Computer Science from Purdue University, specializing in illumination integration in dynamic augmented-reality environments, following a B.Sc. in Computer Science from Taibah University. She serves as Associate Professor and Department Chair at the University of Tabuk, holding key leadership roles in data governance, academic affairs, research centers, and national committees. Her research focuses on augmented and extended reality, human-computer interaction, data visualization, immersive technologies, and AI-driven sensing systems, contributing to reputable venues such as IEEE Access, Applied Sciences, Sensors, and Sustainability. She has received multiple awards for outstanding scientific research, excellence in teaching, innovation, and high-impact publications from the University of Tabuk and national academic bodies. In addition, she has led funded grants, supervised numerous postgraduate and undergraduate projects, and contributed to major institutional initiatives. Through research, leadership, and continuous academic service, she remains committed to advancing immersive technologies and impactful computing research.

Profiles : Scopus | Orcid

Featured Publications

Alhakamy, A. (2025). Intersecting Realms: Examining the Convergence of Vision and AI in Extended Reality Graphics. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3599337

Friedman, A., Tazike, M., Shelton, E. G., Patel, N. A., Alhakamy, A., & Cafaro, F. (2025). Puppeteers and Forecasters: How Children Engage. Conference paper. https://doi.org/10.1145/3694907.3765950

Alhakamy, A. (2025). Advancing SDG5: Machine Learning and Statistical Graphics for Women’s Empowerment and Gender Equity. Sustainability. https://doi.org/10.3390/su17219706

Friedman, A., Tazike, M., Shelton, E. G., Patel, N., Alhakamy, A., & Cafaro, F. (2025). “I’m a Fish!”: Exploring Children’s Engagement with Human–Data Interactions in Museums. Applied Sciences. https://doi.org/10.3390/app152111304

Alhakamy, A. (2024). Extended Reality (XR) Toward Building Immersive Solutions: The Key to Unlocking Industry 4.0. ACM Computing Surveys. https://doi.org/10.1145/3652595

Abdelouahad Achmamad | Engineering | Best Researcher Award

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Dr. Abdelouahad Achmamad | Engineering | Best Researcher Award

Universite De Mans | France

Dr. Abde Louahad Achmamad is an embedded software engineer and researcher specializing in intelligent systems, AI algorithms, biomedical signal processing, and advanced embedded technologies for electrical, biomedical, and IoT applications. He holds a PhD in Electrical Engineering, complemented by master’s degrees in biomedical engineering and electrical/electronic engineering, supported by earlier qualifications in electromechanics and electronics. His professional experience spans biomedical R&D, embedded system design, AI-based medical diagnostics, MRI segmentation, IoT health monitoring, wearable sensor development, and security applications for STM32 microcontrollers. He has worked with leading institutions and companies across France and Morocco, including STMicroelectronics, Akkodis, Diabtech, Université de Rouen, ENSIAS, and multiple research laboratories. His scientific output includes 102 citations by 95 documents, 16 publications, and an h-index of 6, reflecting contributions in EMG-based diagnostics, phonoangiography, neuromuscular disorder detection, sensor fusion, and few-shot learning for medical imaging. He has also reviewed more than 200 scientific manuscripts for major journals from Springer, Elsevier, and MDPI, demonstrating recognized expertise in signal processing and embedded systems. His research interests include embedded AI, biomedical instrumentation, wearable sensors, IoT-based healthcare, security-focused embedded design, and intelligent diagnostic systems. Dr. Achmamad remains dedicated to advancing high-impact engineering solutions that enhance healthcare, sensing technologies, and intelligent embedded platforms.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Achmamad, A., & Jbari, A. (2020). A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform. Bulletin of Electrical Engineering and Informatics.

Achmamad, A., Belkhou, A., & Jbari, A. (2020). Fast automatic detection of amyotrophic lateral sclerosis disease based on Euclidean distance metric. In 2020 International Conference on Electrical and Information Technologies .

Belkhou, A., Achmamad, A., & Jbari, A. (2019). Classification and diagnosis of myopathy EMG signals using the continuous wavelet transform. In 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science .

Belkhou, A., Achmamad, A., & Jbari, A. (2019). Myopathy detection and classification based on the continuous wavelet transform. Journal of Communications Software and Systems.

Abdelouahad, A., Belkhou, A., Jbari, A., & Bellarbi, L. (2018). Time and frequency parameters of sEMG signal–force relationship. In 2018 International Conference on Optimization and Applications .

Nicholas Otumi | Neuroscience Data Analysis | Best Researcher Award

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Mr. Nicholas Otumi | Neuroscience Data Analysis | Best Researcher Award

University of Rochester | United States

Nicholas Otumi is a biomedical imaging researcher with a strong foundation in diagnostic imaging and deep learning. He holds a BSc in Diagnostic Imaging Technology from the University of Cape Coast (Ghana) and an MS in Diagnostic Imaging (Bioengineering & Biomedical Engineering) from the University of Rochester (USA), where his coursework included deep learning, inverse problems in imaging, clinical imaging, and signal processing. He has held research and technical roles at multiple institutions: as a remote Research Assistant at Indiana University working on pharmaceutical pricing and market analytics, and at the University of Rochester contributing to segmentation (with UNet-R) and image analysis in brain CT. Previously, as a Research and Teaching Assistant at UCC, he supported imaging equipment courses, data analysis, and undergraduate research. His research interests center on medical image analysis, especially applying deep learning to brain MRI and CT segmentation, classification of disease from imaging, and imaging in resource-constrained settings. He has several manuscripts under review or published, including a multicentre survey on advanced brain MRI in low-resource settings and case reports in radiology.  He is a recipient of honors such as the Harry W. Fischer Research Funds (2025) and the HSEAS Dean’s Fellowship (2024) at Rochester, as well as campus leadership awards in Ghana. Nicholas is committed to advancing AI-driven neuroimaging and translational imaging research across low- and middle-income settings, aiming to bridge technology gaps and enhance equitable access to advanced diagnostic tools.

Profiles : Orcid | Google Scholar

Featured Publications

Piersson, A. D., Nunoo, G., Dzefi-Tettey, K., & Otumi, N. (2025). Utility of advanced brain MRI techniques for clinical and research purposes in a low-resource setting: A multicentre survey. Next Research.

Fiagbedzi, E., Arkorful, J., Brobbey, I. F., Appiah, E., Nyarko, S., Otumi, N., Piersson, A. D., Nimo, O., Ofori, I. N., & Gorleku, P. N. (2025). A case of ruptured infrapatellar bursa sac with Baker’s cyst. Radiology Case Reports, 20(1).

Fiagbedzi, E., Arkorful, J., Appiah, E., Otumi, N., Ofori, I., & Gorleku, P. N. (2024). A rare case of intussusception in a 6-month-old baby. Radiology Case Reports, 19(10).