Mehnaz Tabassum | Computer Science and Artificial Intelligence | Women Researcher Award

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Dr. Mehnaz Tabassum | Computer Science and Artificial Intelligence | Women Researcher Award

University of Sydney | Australia

Dr. Mehnaz Tabassum is an accomplished researcher in Computational Neurosurgery and Health Innovation, with core expertise in medical image analysis, artificial intelligence, and brain tumor diagnostics. Her research integrates deep learning, radiomics, and neuroimaging to enhance the precision of tumor segmentation, classification, and recurrence prediction in neuro-oncology. Her scholarly contributions include 14 Scopus-indexed publications, with a total of 87 citations and an h-index of 4 (Scopus metrics). She has published in prestigious journals such as Cancers, European Radiology, and Neuro-Oncology Advances, and has presented her findings at leading international conferences including IEEE EMBC and IEEE ISBI. Dr. Mehnaz Tabassum’s recent research explores cross-modality medical image synthesis, MRI-to-PET generation using diffusion and GAN-based models, and meta transfer learning for brain tumor segmentation. Her innovative work advances computational solutions for precision medicine and AI-assisted neuroimaging. She has received multiple distinctions, including the Pro-Vice Chancellor’s Research Excellence Scholarship and the Henry Sutton Postgraduate Research Scholarship, alongside a Best Paper Award for excellence in scientific contribution. Her interdisciplinary research continues to impact the fields of AI-driven diagnostics, eye-tracking in medical imaging, and computational modeling for neurosurgical innovation, reflecting her commitment to advancing data-driven healthcare and translational neuroscience.

Profiles: Scopus | Google Scholar | ORCID | ResearchGate | Staff Page

Featured Publications

  • Tabassum, M., Suman, A. A., Suero Molina, E., Pan, E., Di Ieva, A., & Liu, S. (2023). Radiomics and machine learning in brain tumors and their habitat: A systematic review. Cancers, 15(8), Article 2034. https://doi.org/10.3390/cancers15082034

  • Ghose, P., Alavi, M., Tabassum, M., Ashraf Uddin, M., Biswas, M., Mahbub, K., … & Hassan, M. (2022). Detecting COVID-19 infection status from chest X-ray and CT scan via single transfer learning-driven approach. Frontiers in Genetics, 13, 980338. https://doi.org/10.3389/fgene.2022.980338

  • Moradizeyveh, S., Tabassum, M., Liu, S., Newport, R. A., Beheshti, A., & Di Ieva, A. (2024). When eye-tracking meets machine learning: A systematic review on applications in medical image analysis. arXiv preprint arXiv:2403.07834. https://arxiv.org/abs/2403.07834

  • Tabassum, M., Suman, A. A., Russo, C., Di Ieva, A., & Liu, S. (2023). A deep learning framework for skull stripping in brain MRI. Neurocomputing (Under review).

  • Afrin, F., Al-Amin, M., & Tabassum, M. (2015). Comparative performance of using PCA with K-means and fuzzy C means clustering for customer segmentation. International Journal of Scientific and Technology Research, 4(8), 70–74.

Jiajie Gao | Computer Science | Best Researcher Award

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Mr. Jiajie Gao | Computer Science | Best Researcher Award

Hebei University of Architecture | China

Gao Jiajie is a highly driven and innovative researcher currently pursuing a master’s degree at Hebei University of Architecture. In just two years, he has demonstrated exceptional research prowess by independently publishing three SCI Q3 journal articles, authoring 16 software patents, and leading a national-level project. His commitment to bridging theoretical AI innovations with real-world applications places him at the forefront of emerging research talent in occupational safety and intelligent detection systems.

Professional profile👤

Scopus

Strengths for the Awards✨

Gao Jiajie demonstrates exceptional research performance, particularly notable given his status as a master’s student. His achievements include publishing three SCI Q3 journal articles, securing 16 authorized software patents, and acting as principal investigator on a national-level project—a feat typically rare at this early career stage. His research focuses on applied artificial intelligence in safety-critical systems, such as railway worker compliance, power grid integrity, and smart retail. Gao’s innovation in improving YOLOv5-based detection systems—including integrating CBAM and MPDIoU modules—shows deep technical insight and a solutions-oriented mindset. Moreover, his ability to translate research into real-world industrial collaborations speaks to his applied impact, with outputs in railway safety, insulator fault detection, and retail inventory management. His role as a reviewer for a peer-reviewed journal further highlights his growing reputation in the academic community. 🔍

🎓 Education

Gao Jiajie is presently a master’s student at Hebei University of Architecture, where he is actively involved in cutting-edge research in artificial intelligence and its applications in safety systems. His academic journey is marked by a strong focus on practical innovations and scholarly contributions, showcasing an excellent balance between theory and implementation.

💼 Experience

In his short academic career, Gao Jiajie has already led one national-level project as the principal investigator and served as a key contributor in multiple collaborative initiatives. His leadership resulted in eight authorized software patents and several AI-based detection systems. He also actively participates in academic review processes as a reviewer for the Journal of Electronic Imaging (JEI).

🧠 Research Interest On Computer Science

Gao’s research centers on Artificial Intelligence, particularly in the development of advanced detection algorithms that enhance occupational safety and industrial automation. His innovative approach integrates attention mechanisms (CBAM), improved loss functions (MPDIoU), and real-time object detection models (YOLOv5/YOLOv8), delivering significant accuracy improvements in challenging environments such as railways, power grids, and retail sectors.

🏅 Awards

While still early in his academic journey, Gao Jiajie’s work has garnered national recognition. His role as Principal Investigator of a national-level project, along with his multiple patents and peer-reviewed publications, highlight his merit for the Best Researcher Award. His work stands as a testament to innovation, application, and academic excellence. 🏆

📚 Publications

Gao Jiajie has published three peer-reviewed SCI Q3 journal articles, each making notable contributions to AI-based visual detection systems:

  1. Gao, Jiajie, et al. Safety equipment compliance analysis for occupational safety, Signal, Image and Video Processing, Vol. 19, Article 720, 2025.
    📈 Cited by: Article recently published; citation data forthcoming.

  2. Gao, Jiajie, et al. Enhanced YOLOv8 for high-precision retail cabinet product recognition, Signal, Image and Video Processing, Vol. 19.7, 2025.
    📈 Cited by: 2 articles.

  3. Gao, Jiajie, et al. Research on the algorithm of detecting insulators in high-voltage transmission lines using UAV images, Signal, Image and Video Processing, Vol. 18, Suppl 1, 2024.
    📈 Cited by: 5 articles.

These publications emphasize Gao’s skill in applying deep learning techniques to complex detection challenges in infrastructure and retail environments.

✅ Conclusion

Gao Jiajie exemplifies the qualities of a forward-thinking researcher, blending deep technical knowledge with practical implementation in AI-based safety systems. His rapid output of publications, patents, and leadership in a national project underscores a rare level of maturity, innovation, and commitment at an early academic stage. With outstanding contributions in detection algorithm design, academic reviewing, and cross-sector collaboration, Gao is a deserving nominee for the Best Researcher Award.