Mengjun Xu | Artificial Intelligence | Best Researcher Award 

Published on by AMO Physics

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.

Farzeen Munir – Artifical Intelligence – Best Researcher Award

Published on by AMO Physics

Dr. Farzeen Munir began her academic career with a strong grounding in Artifical Intelligence and engineering principles. She earned her BS in Electrical Engineering from the Pakistan Institute of Engineering and Applied Sciences (PIEAS) in 2013, where she completed her thesis on motor control systems for humanoid arms—an early indication of her interest in robotics and intelligent systems. She went on to complete her MS in System Engineering from PIEAS in 2015, with a focus on “Spatio-Temporal Visual Object Tracking,” showcasing her growing expertise in visual data analysis and algorithm design. Her academic excellence culminated in a PhD in Electrical Engineering and Computer Science from the prestigious Gwangju Institute of Science and Technology (GIST), South Korea (2017–2022), where her research focused on “Dynamic Visual Perception for Autonomous Vehicles,” incorporating deep learning, representation learning, and Artifical Intelligence.

💼 Professional Endeavors

Dr. Farzeen Munir’s professional trajectory reflects her deep expertise in intelligent systems and machine perception. She is currently a Postdoctoral Researcher at the Mobile Robotic Group, Department of Electrical Engineering and Automation (EEA), Aalto University, Finland, where she is developing socially aware autonomous vehicles by modeling and predicting pedestrian behavior in urban environments. Previously, she held research positions at the Korea Culture Technology Institute and the Machine Learning and Vision Lab at GIST. Her work spans both theoretical and applied aspects of Artifical Intelligence, with notable achievements in deep learning applications, sensor fusion, human-computer interaction, and intelligent systems for robotics and autonomous vehicles.

🔬 Contributions and Research Focus

Dr. Munir’s core research lies at the intersection of Artifical Intelligence, deep learning, and autonomous systems. Her notable contributions include developing end-to-end perception systems for autonomous vehicles using novel sensors such as thermal cameras and dynamic vision sensors. She has designed advanced convolutional encoder-decoder networks for lane segmentation and proposed self-supervised contrastive learning frameworks for multimodal sensor fusion. Her work contributes not only to academic knowledge but also to practical advancements in intelligent transportation and human-centered automation. Her innovations in spatio-temporal object tracking and real-time wearable eye trackers further highlight her broad impact across diverse AI-driven applications.

🌍 Impact and Influence

Dr. Farzeen Munir has made significant impact in the field of Artifical Intelligence and machine learning, particularly through her interdisciplinary approach combining robotics, computer vision, and human behavior modeling. Her research on pedestrian-AI interaction is advancing safety protocols for autonomous vehicles, and her collaborative work with institutions like the University of Munich and Aalto University reflects her global academic footprint. She has played a key role in writing research funding proposals for major organizations such as the European Union and the Academy of Finland, demonstrating leadership beyond research in scientific funding and innovation strategy.

🏆Academic Cites

Dr. Munir’s work has been published in high-quality, peer-reviewed international journals and conferences, with citations growing steadily due to the novelty and practical value of her research. Her contributions, particularly in vision-based deep learning, autonomous driving, and sensor fusion, are frequently referenced by researchers in academia and industry. Her interdisciplinary impact spans not only Artifical Intelligence but also human-computer interaction, robotics, and embedded systems, illustrating the broad relevance of her scholarly output.

🌟 Legacy and Future Contributions

Dr. Farzeen Munir stands at the forefront of next-generation Artifical Intelligence research with real-world implications. Her legacy is built on pioneering human-centered autonomous vehicle systems, vision-based AI perception modules, and multimodal deep learning frameworks. Looking ahead, she aims to further develop socially aware robotics and intelligent transportation systems, while mentoring emerging researchers in the field. Her interdisciplinary outlook ensures that her contributions will shape the future of AI, robotics, and smart mobility for years to come.

Artifical Intelligence

Dr. Munir’s journey also emphasizes the value of cross-cultural research collaboration, with her experience across South Korea, Finland, and Pakistan enriching her approach to problem-solving in AI and robotics. Her ability to blend technical depth with real-world applicability, particularly in autonomous mobility and human-behavioral modeling, uniquely positions her to lead future breakthroughs in intelligent systems.