Mengjun Xu | Artificial Intelligence | Best Researcher Award 

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Ms. Mengjun Xu | Artificial Intelligence | Best Researcher Award 

University of Science and Technology of China | China 

Ms. Mengjun Xu is a researcher specializing in artificial intelligence and machine learning, with a focus on adversarial robustness and security in deep learning models. Her recent works include Efficient Large Margin Adversarial Training Based on Decision Boundaries for Adversarial Robustness (Neurocomputing, 2025) and Decreasing Adversarial Transferability Using Gradient Information of Attack Paths (Applied Soft Computing, 2025). Her research advances defense strategies against adversarial attacks, contributing to safer and more reliable AI systems.

Author Profiles

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Early Academic Pursuits

Ms. Mengjun Xu began her academic journey with a strong foundation in computer science and engineering, focusing on machine learning and artificial intelligence. During her formative years, she developed a keen interest in adversarial machine learning, robustness, and deep learning optimization. This early dedication to research provided her with the skills and motivation to publish impactful works in top international journals.

Professional Endeavors

Ms. Xu has established herself as an emerging researcher in the domain of artificial intelligence, with particular expertise in adversarial robustness and model security. She has collaborated with prominent scholars such as Ziqiang Li, Lei Liu, Pengfei Xia, and Bin Li, contributing to projects that address key challenges in deep learning and AI safety. Her professional endeavors include publishing in highly respected journals like Neurocomputing and Applied Soft Computing, which demonstrates her commitment to advancing secure and reliable AI systems.

Contributions and Research Focus

Her research contributions focus on adversarial training, transferability reduction, and the development of robust AI algorithms. In her 2025 Neurocomputing article, “Efficient large margin adversarial training based on decision boundaries for adversarial robustness,” she explored innovative methods to strengthen deep learning models against adversarial attacks. Similarly, in her Applied Soft Computing publication, “Decreasing adversarial transferability using gradient information of attack paths,” she addressed the critical issue of cross-model adversarial vulnerabilities. Both works highlight her strong research focus on the practical application of artificial intelligence in secure and trustworthy systems.

Impact and Influence

Ms. Xu’s publications have begun shaping ongoing discussions in adversarial machine learning and AI robustness. Her innovative frameworks provide the research community with new methodologies for building AI models that are not only accurate but also resistant to adversarial manipulation. As a result, her work is expected to have a long-term influence on the development of safe, secure, and ethical AI systems.

Academic Cites

Her journal articles have already gained recognition in the academic community, with citations that reflect the growing importance of her contributions to artificial intelligence. These citations underline the relevance of her research in addressing pressing challenges in adversarial robustness and demonstrate her rising reputation as a thought leader in the field.

Legacy and Future Contributions

Looking ahead, Ms. Mengjun Xu is poised to continue advancing the frontier of adversarial machine learning. Her legacy will likely include the development of novel frameworks for adversarial defense, bridging the gap between theoretical innovation and real-world AI applications. By mentoring young scholars and fostering collaboration, she is set to influence future generations of AI researchers and practitioners.

Publications

Efficient large margin adversarial training based on decision boundaries for adversarial robustness

Authors: Mengjun Xu, Ziqiang Li, Lei Liu, Bin Li

Journal: Neurocomputing

Year: 2025

Decreasing adversarial transferability using gradient information of attack paths

Authors: M. Xu, L. Liu, P. Xia, Z. Li, B. Li

Journal: Applied Soft Computing

Year: 2025

Deep-learning-based nanomechanical vibration for rapid and label-free assay of epithelial mesenchymal transition

Authors: W. Wu, Y. Peng, M. Xu, T. Yan, D. Zhang, Y. Chen, K. Mei, Q. Chen, X. Wang, ...

Journal: ACS Nano

Year: 2024

Conclusion

Ms. Mengjun Xu’s journey exemplifies a dedication to excellence in artificial intelligence research. From her early academic pursuits to her groundbreaking contributions on adversarial robustness, she has built a strong foundation for long-term impact. Her professional endeavors, academic citations, and future contributions signal a promising career trajectory, with her work serving as a cornerstone in advancing secure and reliable AI systems for the future.

Andy Anderson Bery – Machine Learning in Geophysics – Best Researcher Award

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Assoc. Prof. Dr. Andy Anderson Bery - Machine Learning in Geophysics - Best Researcher Award 

Universiti Sains Malaysia - Malaysia 

Author Profile

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🎓 Early Academic Pursuits

Assoc. Prof. Dr. Andy Anderson Bery embarked on his academic journey with a strong foundation in geophysics, gaining his undergraduate and graduate education from renowned institutions. His early academic pursuits were characterized by a deep interest in the intersection of geophysics, geology, and advanced technologies. His dedication to research and innovation led him to further specialize in applying geophysical methods to environmental and geological challenges, particularly focusing on subsurface imaging and modeling. These early academic pursuits laid the groundwork for his later contributions to machine learning in geophysics.

💼 Professional Endeavors

Assoc. Prof. Dr. Bery has had a distinguished career, serving as an academic leader and researcher. His role as a faculty member and researcher has been marked by a commitment to advancing geophysical methodologies, especially through the integration of machine learning in geophysics. Throughout his professional endeavors, Dr. Bery has served as a primary and co-supervisor for numerous Ph.D. and Master’s students, guiding them through complex research projects. His expertise spans various geophysical methods, such as electrical resistivity tomography, seismic refraction, and the application of machine learning in geophysics to enhance subsurface imaging techniques.

🔬 Contributions and Research Focus

Dr. Bery's research focus includes the application of advanced geophysical techniques, with a strong emphasis on integrating machine learning in geophysics. His contributions to improving seismic signal detectability, soil shear strength modeling, and the development of geophysical-geotechnical relationships have been pivotal. He has also contributed to significant advancements in subsurface imaging techniques, including the use of electrical resistivity tomography in identifying geological structures and landslide susceptibility mapping. His ongoing research continues to explore the potential of machine learning in geophysics to revolutionize the interpretation and analysis of geophysical data, making these techniques more efficient and accurate.

🌍 Impact and Influence

Assoc. Prof. Dr. Bery’s impact extends beyond his direct academic contributions; he has significantly influenced the development of new geophysical methodologies that incorporate machine learning in geophysics. His research has helped shape the direction of modern geophysics, particularly in the context of subsurface imaging and environmental assessment. Dr. Bery’s work has been widely recognized within the academic community, and he has presented his findings at numerous conferences, collaborating with international experts in the field. His influence continues to resonate in the field as emerging technologies such as machine learning in geophysics become integral to geophysical practices.

🏆Academic Cites

Assoc. Prof. Dr. Andy Anderson Bery’s academic contributions have been widely cited in top-tier geophysical and geological journals. His work, especially related to machine learning in geophysics, is frequently referenced by other researchers in the field, illustrating its importance and relevance. The impact of his research can be seen in the increasing number of citations and the use of his methodologies by both academic and industry professionals. His papers have laid a foundation for future research in geophysical applications and the use of machine learning to analyze geophysical data.

🌟 Legacy and Future Contributions

As a seasoned academic and researcher, Dr. Bery’s legacy is already well established, particularly in the integration of machine learning in geophysics. His future contributions promise to further push the boundaries of geophysical methods, especially in the areas of subsurface imaging and environmental monitoring. As he continues to mentor the next generation of researchers, his influence will persist in the development of cutting-edge technologies that merge geophysics with machine learning. His work is expected to play a central role in revolutionizing the ways geophysical data is interpreted and utilized.

📝Machine Learning in Geophysics

Assoc. Prof. Dr. Bery’s research in machine learning in geophysics has been a cornerstone of his academic career. His innovative approaches to integrating machine learning in geophysics have not only enhanced the accuracy and efficiency of geophysical methods but have also led to the development of novel geophysical models. The ongoing use of machine learning in geophysics in Dr. Bery's future work will continue to redefine the field, offering more advanced, scalable, and accurate solutions to geophysical challenges.

Notable Publication

📝Application of Electrical Resistivity Tomography and Induced Polarization for Pre-Construction Site Assessment in Ipoh, Perak, Malaysia

Authors: Musty, S.B., Bery, A.A.

Journal: BIO Web of Conferences

Year: 2024

Citations: 0

📝Integrated Geophysical Investigation using Aero-radiometric and Electrical Methods for Potential Gold mineralization within Yauri/Zuru Schist Belts, Kebbi State NW Nigeria | Investigación geofísica integrada de prospección aerorradiométrica y métodos eléctricos para definir el potencial de mineralización aurífera en el cinturón de esquistos de Yauri/Zuru, en el estado de Kebbi, en el noroeste de Nigeria

Authors: Augie, A.I., Salako, K.A., Bery, A.A., Rafiu, A.A., Jimoh, M.O.

Journal: Earth Sciences Research Journal

Year: 2024

Citations: 0

📝Surface–subsurface characterization via interfaced geophysical–geotechnical and optimized regression modeling

Authors: Akingboye, A.S., Bery, A.A., Aminu, M.B., Bala, G.A., Ale, T.O.

Journal: Modeling Earth Systems and Environment

Year: 2024

Citations: 1

📝A novel machine learning approach for interpolating seismic velocity and electrical resistivity models for early-stage soil-rock assessment

Authors: Dick, M.D., Bery, A.A., Okonna, N.N., Bashir, Y., Akingboye, A.S.

Journal: Earth Science Informatics

Year: 2024

Citations: 4

📝Subsurface Lithological Characterization Via Machine Learning-assisted Electrical Resistivity and SPT-N Modeling: A Case Study from Sabah, Malaysia

Authors: Dick, M.D., Bery, A.A., Akingboye, A.S., Moses, E., Purohit, S.

Journal: Earth Systems and Environment

Year: 2024

Citations: 0

📝Geometry Analysis of Penang Island Faults Based on Satellite Gravity Data

Authors: Pambayun, T., Hilyah, A., Lestari, W., Bery, A.A.

Journal: IOP Conference Series: Earth and Environmental Science

Year: 2024

Citations: 0