Muhammad Luqman Naseem | AI Security | Best Researcher Award

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Mr. Muhammad Luqman Naseem | AI Security | Best Researcher Award

Mr. Muhammad Luqman Naseem | AI Security – Ph.D at Harbin Institute of Technology, China

Muhammad Luqman Naseem is a dynamic and forward-thinking researcher specializing in AI security and adversarial machine learning. With a strong academic foundation and a multifaceted professional journey across research institutions and the tech industry, he has established himself as a promising thought leader in the field of secure artificial intelligence. His work bridges the domains of machine learning, cybersecurity, and cross-media AI, contributing novel insights to adversarial defenses in real-world systems. Currently based in China, Luqman has gained recognition through his participation in international conferences, cross-border projects, and high-impact journal publications, demonstrating a commitment to excellence in science and innovation.

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🔹Education:

Luqman holds a Master of Science in Software Engineering from Northeastern University, China, where he focused on adversarial machine learning and ICT security. His thesis explored the detection of adversarial attacks in the problem space for Support Vector Machines, a topic of rising significance in secure AI systems. Prior to this, he completed a Bachelor of Science in Information Technology from the University of Education in Pakistan, where his undergraduate work included the development of online event management systems. Complementing his core degrees, he pursued a postgraduate diploma in Information Technology and Chinese language studies, enabling both technical and linguistic fluency vital for his international academic journey. His education reflects a well-rounded foundation in computer science, systems development, and advanced machine learning techniques.

🔹Experience:

Muhammad Luqman Naseem has accumulated hands-on experience through diverse roles in research and development. As a Research Assistant at the Research Centre for Cross-Media AI at Northeastern University, he worked extensively on adversarial AI, malware detection, and secure learning models using advanced platforms such as Ubuntu VM and NVIDIA Tesla K40. His earlier engineering internships involved backend and frontend development using technologies like Spring Boot and Vue.js, furthering his grasp on full-stack development. In industry roles, he has worked as an IT officer, network engineer, and application developer, handling networks, virtualization systems, SAP environments, and security firewalls. These varied experiences have shaped a tech-savvy researcher well-versed in both theory and implementation.

🔹Research Interest:

Luqman’s primary research interests lie in adversarial machine learning, AI security, and secure multi-label classification systems. His work focuses on detecting and mitigating vulnerabilities in intelligent systems, especially in high-stakes environments like healthcare, finance, and IoT. He is also deeply engaged with gradient-based attacks, black-box model inversion techniques, and scalable malware detection models. His interdisciplinary outlook allows him to integrate machine learning, systems programming, and cybersecurity practices to address real-world challenges in digital safety and trust in AI-driven decisions.

🔹Award:

Luqman has been recognized multiple times for his academic diligence and technical contributions. He received the prestigious Chinese Government Scholarship (CSC) to support his postgraduate research and was previously honored with Pakistan’s Prime Minister Laptop Award for outstanding performance. Additionally, his commitment to enterprise solutions was acknowledged through SAP Business One End-User Training Certification. His active participation in conferences, workshops, and collaborative seminars, including the Huawei Developer Conference and ICSI 2023, further reflects his contribution to global innovation communities.

🔹Publications:

📘 Trans-IFFT-FGSM: A Novel Fast Gradient Sign Method for Adversarial Attacks (2024), Multimedia Tools and Applications – An impactful contribution to fast adversarial model design.
📗 Showing Many Labels in Multi-label Classification Models: An Empirical Study of Adversarial Examples (2024), arXiv – Delving into vulnerabilities of multi-label classifiers.
📙 Comprehensive Comparisons of Gradient-based Multi-label Adversarial Attacks (2024), Complex & Intelligent Systems – A benchmarking study for multiple gradient attack strategies.
📕 C2fmi: Corse-to-Fine Black-box Model Inversion Attack (2023), IEEE Transactions on Dependable and Secure Computing – A study on reconstructing models from outputs in secure systems.
📒 How Deep Learning is Empowering Semantic Segmentation (2022), Multimedia Tools & Applications – A survey of DL methods for vision-based tasks.
📓 Fast and Robust Detection of Adversarial Attacks in the Problem Space using Machine Learning (2022) – Addressing adversarial defense using efficient learning models.

🔹Conclusion:

Muhammad Luqman Naseem exemplifies the qualities of an ideal recipient for the Best Researcher Award—analytical rigor, technical innovation, and collaborative engagement. His growing portfolio of research in adversarial AI, backed by real-world application experience, positions him at the forefront of tackling one of the most critical challenges in the AI domain: ensuring secure and trustworthy intelligent systems. His consistent publication record, international exposure, and scholarly recognition showcase not only his past achievements but also his potential for future contributions to the research community. Awarding him this honor would not only recognize his achievements but also inspire further advancement in secure AI research across global communities.

 

 

 

 

Dr. Nianyun Song | Crowdsensing | Best Researcher Award

Published on by Awards and Recognitions

Dr. Nianyun Song | Crowdsensing | Best Researcher Award

Dr. Nianyun Song | Crowdsensing – Student at Beijing Normal University, China

Nianyun Song is an emerging scholar in the fields of artificial intelligence, privacy-preserving computation, and collaborative sensing technologies. Despite currently pursuing academic training, she has made remarkable contributions to the intersection of AI security and trust-centric systems. Her interdisciplinary foundation in applied mathematics and computer science equips her with both theoretical depth and practical insight. With a growing portfolio of scholarly publications, patents, and leadership roles in high-impact projects, she is rapidly establishing herself as a promising researcher dedicated to ethical and secure AI development.

Profile:

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Education:

Nianyun has cultivated a strong academic background rooted in quantitative and computational disciplines. Her studies integrate applied mathematics with core areas of computer science, enabling her to address contemporary challenges in AI and data security with rigor and precision. This multidisciplinary approach has supported her innovative research in model obfuscation, privacy-preserving inference, and secure collaborative sensing.

Experience:

Nianyun’s research experience spans multiple national, provincial, and municipal research projects, where she has served in leadership and technical roles. Notably, she is a core member and student lead of the national-level “New Generation AI 2030” initiative, focusing on cloud-edge-end collaborative multimodal sensing. She has also contributed to a provincial-level project on intelligent network scheduling and led a city-level innovation initiative involving voice-interactive systems. Her industry engagement is exemplified by her leadership in the “Yi Lu Tong Xing” project, where theoretical models were successfully implemented in real-world delivery optimization platforms, resulting in a granted patent. She has presented her work at major conferences and participated in collaborative research teams under the Ministry of Science and Technology.

Research Interests:

Nianyun’s research interests focus on enhancing trust and security in AI ecosystems. Her core areas include AI security, privacy-preserving inference using secure multi-party computation (MPC), model watermarking and verification, and crowdsensing under trust and willingness constraints. Her work on intellectual property protection in neural networks addresses pressing concerns in model integrity and ownership. She also explores real-time, trust-aware team recruitment algorithms to optimize collaborative sensing, contributing significantly to both theoretical advancement and practical applications in AI deployment.

Awards:

While formal awards are still forthcoming, Nianyun’s achievements are reflected in her invitations to present at prestigious forums such as IWQoS 2024 and her selection as a reviewer for prominent journals. Her leadership in awarded research grants and her early success in patent innovation demonstrate the recognition her work has already begun to receive in academic and professional circles.

Publications 📚:

Nianyun has published a total of nine research papers, with four currently under review and five already published in respected journals and conferences. Notable publications include:

  1. 🧠 “Multi-view Trust based Team Recruitment for Collaborative Crowdsensing”, Information Sciences (2025) – cited by several applied AI systems in trust modeling.
  2. 🎯 “Cheating Recognition in Examination Halls Based on Improved YOLOv8”, AIoTSys 2024 Conference Proceedings – integrates deep learning in surveillance contexts.
  3. 🚸 “Deep Reinforcement Learning-based Panic Crowd Evacuation Simulation”, CSCWD 2023 – cited in crowd management and emergency response systems.
  4. 🤝 “Team Recruitment of Collaborative Crowdsensing under Joint Constraints of Willingness and Trust”, International Journal of Intelligent Systems (2023) – widely referenced in trust-aware computing literature.
  5. 📡 “Collaborative Teams Recruitment Based on Dual Constraints of Willingness and Trust for Crowd Sensing” – influential in the development of team optimization algorithms in distributed sensing.

Conclusion:

Nianyun Song exemplifies the rare combination of academic excellence, innovation, and early leadership that makes her an ideal candidate for the Best Researcher Award. Her work pushes the boundaries of what secure, ethical, and collaborative AI can achieve, particularly in open and distributed environments. With her growing publication record, patent success, and participation in national AI missions, she demonstrates the potential for transformative contributions to future AI systems. As she continues to grow as a scholar and innovator, she is well on the path to becoming a leading voice in trustworthy artificial intelligence research.