The convergence of Big Data and Artificial Intelligence is fundamentally reshaping our ability to perceive, understand, and predict complex phenomena across diverse scientific domains and real-world systems. This session brings together cutting-edge research that exemplifies this powerful synergy. We will explore how advanced AI techniques, ranging from deep visual understanding and reasoning to novel big data algorithms and models, are being leveraged to tackle intricate challenges. Specifically, the session focuses on extracting profound insights from multifaceted data sources, including visual scenes, brain activity (EEG), and dynamic network interactions, to build predictive models that illuminate hidden patterns, forecast future states, and ultimately accelerate scientific breakthroughs. The aim is to delve into the forefront of AI-driven discovery, where computational perception meets predictive power to unlock the secrets of complex systems and advance scientific frontiers.
大数据与人工智能的融合正深刻重塑我们感知、理解及预测跨学科领域与现实世界复杂现象的能力。本分论坛汇集了体现这一强大协同效应的前沿研究成果。我们将深入探讨如何运用先进人工智能技术——涵盖深度视觉理解与推理、新型大数据算法与模型等——应对复杂挑战。特别聚焦于从多源异构数据（包括视觉场景、脑电活动及动态网络交互）中提取深层特征，构建能够揭示隐藏模式、预测未来状态并最终加速科学突破的预测模型。本分论坛旨在探索人工智能驱动发现的前沿领域，推动计算感知力与预测力的融合，以破解复杂系统奥秘并拓展科学边界。
Topics of interest include but are not limited to:
1. Advanced Computer Vision with Reasoning & Interpretation高级视觉与推理解析
- Focus: Moving beyond simple recognition, this topic explores AI models that achieve deep scene understanding, contextual reasoning, causal inference, and explainable interpretation of visual data.
- 聚焦领域：突破传统识别局限，探索实现深度场景理解、情境推理、因果推断及可视化数据可解释性解析的AI模型。
2. AI for Scientific Discovery (AI4Science) 科学发现人工智能（AI4Science）
- Focus: Showcasing novel applications of AI in accelerating research across physical, biological, chemical, environmental, and other sciences. Includes challenges like data scarcity, multi-scale modeling, and discovery of novel patterns/laws.
- 聚焦领域： 展示人工智能在物理、生物、化学、环境等科学领域加速科研突破的创新应用，涵盖数据稀缺性、多尺度建模及新规律发现等核心挑战。
3. AI-Driven Modeling of Complex Network Dynamics 复杂网络动力学的人工智能建模
- Focus: Utilizing AI (including ML, DL, GNNs) and big data analytics to model, simulate, predict, and control the behavior of complex networked systems (e.g., social networks, biological networks, infrastructure networks, financial systems, etc).
- 聚焦领域： 运用人工智能技术（含机器学习、深度学习、图神经网络）及大数据分析方法，对社交网络、生物网络、基础设施网络、金融系统等复杂网络系统的行为进行建模、仿真、预测与控制。
4. Mining Insights from Neural Signals (EEG & Beyond) 神经信号深度挖掘（脑电及多模态技术）
- Focus: Applying advanced signal processing, machine learning, and big data techniques to analyze EEG and other neurophysiological data. Applications include brain-computer interfaces, cognitive state monitoring, neurological disorder diagnosis/prognosis, and understanding brain dynamics.
- 聚焦领域： 通过先进信号处理、机器学习与大数据技术解析脑电等神经生理数据，应用于脑机接口、认知状态监测、神经疾病诊断/预后及脑动态机制研究。
5. Foundational Big Data Models, Algorithms & Architectures 大数据基础模型、算法与架构
- Focus: Presenting novel computational frameworks, scalable algorithms, efficient data structures, and specialized hardware/software architectures designed to handle the volume, velocity, variety, and veracity challenges of massive datasets, underpinning the AI advancements in the other topics.
- 聚焦领域： 提出面向海量数据"4V挑战"（体量、速度、多样性、真实性）的新型计算框架、可扩展算法、高效数据结构及专用软硬件架构，为其他议题的人工智能突破提供基础支撑。
Chair: Prof. Huan Rong, Nanjing University of Information Science and Technology, China
Professor Huan Rong is now working at Nanjing University of Information Science & Technology (NUIST), recognized as Provincial Science & Technology Commissioner of Jiangsu Province. His research focuses on three interconnected domains: (1) content security for social network users, (2) evolutionary pattern analysis of public opinion dynamics, and (3) brain-inspired computing architectures. Up to now, he has authored over 30 SCI-indexed publications in journals including IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Affective Computing (TAFFC), IEEE Transactions on Artificial Intelligence (TAI), ACM Transactions on Knowledge Discovery from Data (TKDD), and ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), with two ESI Highly Cited Papers. He holds 15 authorized invention patents and actively contributes as a reviewer for leading journals such as IEEE Transactions on Cybernetics (TCYB), IEEE Transactions on Industrial Informatics (TII), IEEE Transactions on Computational Social Systems (TCSS), IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), ACM Transactions on Intelligent Systems and Technology (TIST), Pattern Recognition, and Information Fusion. His research have been supported by multiple competitive grants, including projects from the National Natural Science Foundation of China (NSFC), Jiangsu Provincial Natural Science Foundation, Open Research Fund of Ministry of Education Key Laboratories, and industry-sponsored applied research initiatives.
Paper/Abstract Submission Instructions
1. Word Template:
Formatting.doc (文章模板)
2. Paper submission link for BDAI2025 is at:
Electronic Submission System (投稿链接)
3. Full Paper (Presentation and Publication)
Accepted full paper will be invited to give the oral presentation at the conference and be published in the conference proceeding.