Dr. Binbin Cui is a seasoned finance and AI professional with over 20 years of experience in digital transformation, ERP systems, and data-driven innovation. Holding a PhD in Finance and CPA designation, Dr. Cui currently leads AI-powered financial planning and forecasting initiatives at the Royal Canadian Mint, integrating tools like Microsoft Dynamics 365. An editorial board member for several journals—including Journal of Artificial Intelligence General Science—Dr. Cui reviews manuscripts on algorithmic trading, fraud detection, and behavioral finance. His research explores AI applications in finance, supply chain management, and education, aiming to bridge academic theory with real-world digital innovation.

Dr. Chenxiao Xu is an applied mathematician and AI researcher specializing in machine learning, causal discovery, and multivariate time-series analysis. His work integrates deep learning, computational modeling, and large-scale data analytics to solve complex problems in finance, energy systems, and industrial forecasting. He has developed advanced causal graph learning frameworks, interpretable time-series models, and end-to-end AI pipelines for solar irradiance prediction, flight-engine anomaly detection, and household electricity forecasting. Dr. Xu has published extensively in top ACM and IEEE conferences and actively contributes as a reviewer in major AI journals. His research continues to focus on interpretable, fair, and scalable AI systems.

Research area: Machine Learning, Causal Discovery, Time-Series Analysis, Deep Learning, Industrial Forecasting, AI Explainability

Dr. Ahmed J. Obaid is an Assistant Professor at the Faculty of Computer Science and Mathematics, University of Kufa, Iraq. His research spans data mining, web mining, artificial intelligence, IoT-based smart systems, machine learning, cybersecurity, and intelligent healthcare technologies. Dr. Obaid has authored and co-authored numerous highly cited publications on AI-aided education, smart healthcare monitoring, deepfake detection, cloud computing, 5G privacy, and computational intelligence. His interdisciplinary contributions bridge theoretical modeling with real-world intelligent systems, advancing digital transformation and data-driven innovation. He is recognized internationally for impactful research in emerging technologies and intelligent decision-support systems.

Research area: Artificial Intelligence, Biomedical Innovation, Drug Discovery, Precision Medicine, Machine Learning, Therapeutic Development

Dr. Vishwanadham Mandala is a leading expert in big data, AI, machine learning, and data engineering, with significant contributions to data science and analytics. Currently serving as Enterprise Architect - Data at Cummins, he focuses on AI-driven solutions in automotive safety, manufacturing, and predictive maintenance. Dr. Mandala has authored numerous papers on topics like AI-based recommendation models, predictive failure analytics, and real-time engine failure prediction. His research also explores big data lake houses, data integration, and optimizing supply chains, making him a recognized figure in the field of advanced data analytics and artificial intelligence.

Rensi Chen is a Senior Analyst specializing in Revenue Management, with a strong academic and professional background in business intelligence and big data analytics. A verified scholar affiliated with the University of Rochester, Chen has contributed to interdisciplinary research at the intersection of data mining, AI, and economic forecasting. Notable works include the design of big data-driven analytics systems and the development of multi-modal AI models for macroeconomic prediction. With experience in both academic research and industry applications, Chen brings practical insight into leveraging large-scale data for strategic decision-making, performance optimization, and predictive modeling in complex business environments.

Dr. Y. Zhang is a prominent researcher at the University of Pittsburgh, specializing in artificial intelligence, machine learning, and intelligent systems. Dr. Zhang's work spans credit card fraud detection, robotic manipulation, gastrointestinal imaging, and AI-generated text authentication. Notable contributions include applications of transformer models, ensemble learning (LightGBM, XGBoost), and deep reinforcement learning. Dr. Zhang has authored high-impact studies addressing real-world challenges in credit risk prediction, robotics, and content moderation. Their interdisciplinary approach continues to advance innovation in computational intelligence and automation.

Research: credit card fraud detection, robotic manipulation, gastrointestinal imaging, and AI-generated text authentication