Christian Peluso | Artificial Intelligence | Excellence in Ethical AI Development Award

Dr. Christian Peluso | Artificial Intelligence | Excellence in Ethical AI Development Award

Libero professionista | Consiglio Nazionale delle RicercheThis link is disabled | Italy

Dr. Christian Peluso is a researcher specializing in artificial intelligence with expertise in federated learning, deep learning, and cybersecurity, focusing on privacy-preserving systems for mobile and distributed environments. His research aims to develop intelligent models capable of processing complex and varied data while safeguarding user privacy and ensuring compliance with data protection regulations. Christian earned his Master’s degree in Artificial Intelligence from the University of Pisa with the highest distinction, presenting a thesis titled PrivNet: Advancing Mobile Security through Privacy-Preserving Federated Learning for Malware Detection, which introduced an innovative federated learning approach for mobile malware analysis using convolutional neural networks optimized for image-based data. He has actively collaborated with the Consiglio Nazionale delle Ricerche (CNR) and several academic and research institutions, contributing to projects that merge AI, cybersecurity, and data privacy. His publications, including “PrivNet: Advancing Mobile Security through Privacy-Preserving Federated Learning for Malware Detection” and “An Approach for Privacy-Preserving Mobile Malware Detection Through Federated Machine Learning,” reflect his deep involvement in advancing secure and decentralized AI solutions. He has also contributed to research on explainability-driven malware analysis using deep learning, aimed at improving model interpretability and aiding analysts in identifying malicious software components efficiently. Christian’s technical proficiency covers Python, machine learning frameworks, and reverse engineering methodologies, enabling him to design intelligent systems with strong analytical and practical impact. His academic achievements and professional experiences in software engineering, mobile application security, and AI-driven analysis demonstrate a consistent pursuit of excellence and innovation. His work not only strengthens theoretical understanding in federated machine learning but also delivers practical tools for protecting digital ecosystems. Through his commitment to research, collaboration, and ethical AI development, he continues to contribute meaningfully to the evolving landscape of artificial intelligence and data security. 17 Citations 3 Documents 2 h-index View h-index

Featured Publication

Iadarola, G., Casolare, R., Martinelli, F., Mercaldo, F., Peluso, C., & Santone, A. (2021). A semi-automated explainability-driven approach for malware analysis through deep learning. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE. Cited by: 19

Ciaramella, G., Martinelli, F., Mercaldo, F., Peluso, C., & Santone, A. (2024). An approach for privacy-preserving mobile malware detection through federated machine learning. In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024). SciTePress.
Cited by: 5

Peluso, C., Ciaramella, G., Mercaldo, F., Santone, A., & Martinelli, F. (2024). A federated learning-based Android malware detector through differential privacy. In International Conference on Computer Aided Systems Theory (EUROCAST 2024) (pp. 307–319).

Miao Cui | Artificial Intelligence | Best Researcher Award

Prof. Miao Cui | Artificial Intelligence | Best Researcher Award

Professor |Dalian University of Technology| China

Professor Miao Cui focuses on the fields of digital transformation, innovation management, and data-driven business strategy, with extensive exploration in enterprise and community digitalization practices. Her research emphasizes how organizations orchestrate resources to adapt to digital economies, manage transformation, and foster innovation across various sectors, including state-owned enterprises, traditional manufacturing, high-tech firms, service industries, and non-profit community organizations. She has conducted in-depth case studies on more than 50 enterprises such as Haier, P&G, Inspur, and BBMW, as well as over 30 rural communities across China, providing valuable insights into digital capability development and data-oriented strategic renewal. Through her work, Miao Cui examines the interconnection between big data strategy and organizational growth, focusing on how data analysis informs decision-making, enhances resilience, and drives innovation in dynamic environments. Her studies extend to the role of information systems in enabling business transformation, ecosystem governance, and e-commerce-based social innovation, contributing significantly to both theory and practice in management sciences. Miao Cui’s research achievements include numerous high-impact publications in leading international journals such as the International Journal of Information Management, Information Systems Journal, and Journal of Strategic Information Systems, recognized as top-ranked in their field. Her scholarly contributions have been repeatedly highlighted through ESI highly cited and hot papers, reflecting the global relevance and influence of her work. Additionally, she has authored and edited multiple academic monographs, developed widely adopted management cases for Ivey Publishing, and received several awards for excellence in research and social science innovation. Her work has been cited extensively and applied in organizational and policy contexts, contributing to global discussions on digital transformation and innovation leadership. Miao Cui has 625 Citations, 26 Documents, and an h-index of 9. View h-index.

Profile: Scopus 
Featured Publication

Author(s) unknown. (2025). Collaborative innovation network embeddedness and a firm’s technological impact: Does prior networking experience matter? Journal of Technology Transfer. Cited by 1

Author(s) unknown. (2025). An integrated approach to modeling the influence of critical factors in low-carbon technology adoption by chemical enterprises in China. Journal of Environmental Management. Cited by 2