The 11th Workshop on AI-Enabled Big Data and Management Science serves as a premier international academic platform that brings together researchers, practitioners, and scholars in the fields of artificial intelligence (AI), big data, and management science, dedicated to promoting interdisciplinary exchanges and cooperation. Building on the success of the previous ten editions, this workshop aims to showcase cutting-edge research findings, explore innovative application models, and address key challenges in the integration of AI, big data technologies with the theories and practices of management science. In the era of digital transformation, AI and big data have become core drivers reshaping management models across various industries, with application scenarios covering multiple fields such as intelligent management and decision-making. This workshop provides a platform for participants to exchange insights, promote collaborative research, and facilitate the development of data-driven management theories and intelligent decision-making technologies. We sincerely invite submissions of original research achievements from the global academic and industrial communities to enrich the workshop discussions and drive the innovative evolution of this interdisciplinary field. The Workshop Theme is AI-Driven Big Data Analytics: Empowering Intelligent Management and Decision-Making.
This workshop welcomes submissions of original research papers, case studies, and work-in-progress reports focusing on theoretical, methodological, or practical issues at the intersection of AI, big data, and management science. Topics of interest include but are not limited to the following areas.

1. Fundamental Theories of AI and Big Data in Management Science

• Data-driven management decision theory and optimization methods
• Machine learning and deep learning algorithms for management analytics
• Big data collection, fusion, and quality management in management scenarios
• Applications of computational intelligence (fuzzy logic, evolutionary algorithms, etc.) for management problems

2. Big Data Analytics for Management Applications

• Social media big data mining for digital marketing and consumer behavior analysis
• Multi-modal data fusion for risk early warning
• Big data analytics in financial risk management and investment decision-making

3. AI-Driven Intelligent Management Systems

• Intelligent decision support systems for health management
• Applications of generative AI in service innovation
• Human-machine collaborative decision-making in complex management environments

4. Emerging Topics and Practical Challenges

• Ethical issues and data privacy protection in AI-driven management
• Explainable AI for transparent management decision-making
• Sustainable development and green management based on big data analytics
• Case studies on the applications of AI and big data in management practice

Knowledge or hidden patterns discovered by data mining from large databases has great novelty, which is often unavailable from experts' experience. Its unique irreplaceability and complementarity have brought new opportunities for decision-making and it has become important means of expanding knowledge bases to derive business intelligence in the information era. The challenging problem, however, is whether the results of data mining can be really regarded as “knowledge”. To address this issue, the theory of knowledge management should be applied.

Intelligent Knowledge Management is the management of how rough knowledge and human knowledge can be combined and upgraded into intelligent knowledge. Intelligent Knowledge Management aims to bridge the gap between these two fields. In addition, Knowledge Graph can express knowledge through visualization method and explore and analyze the relationship between knowledge, reveal the dynamic development law of knowledge field, and then realize knowledge sharing and reusing, which can provide rich semantic knowledge network and auxiliary information source for knowledge management and knowledge recommendation. This study not only promotes more significant research beyond data mining, but also enhances the quantitative analysis of knowledge management on hidden patterns from data mining.

In order to promote intelligent knowledge management research, we want to organize a special workshop dedicated to the topic of “Intelligent Knowledge Management” under the ninth International Conference on Information Technology and Quantitative Management (ITQM 2024).

The main purpose of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the area of data mining, expert mining, knowledge graph, recommendation system and intelligent knowledge management, to generate new methods to determine directions for further research. Papers should present modeling approaches/perspectives to intelligent knowledge. The workshop is interested in topics related to all aspects of expert mining and intelligent knowledge. Topics of interest include, but are not limited to, the following:

• Intelligent Knowledge Management
• Wuli-Shili-Renli System Methodology (WSR)
• Knowledge Synthesis
• Expert Mining
• Knowledge Extraction
• Knowledge Graph
• Equipment Health Management (EHM)
• Problem-driven Knowledge Management
• Knowledge Graph Embedding (KGE)
• Recommendation Systems
• Link Prediction
• Large-scale Engineering Projects Management
• Data and Knowledge Management Related to Large-scale Research Infrastructures or Scientific Facilities
• Knowledge Presentation and Visualization
• Extension
• Knowledge Sharing
• Knowledge Spillover
• Knowledge Evaluation
• KDD Process and Human Interaction
• Intelligent Systems and Agents
• Knowledge Management Support Systems
• Deep Knowledge Tracing
• Deep learning
• Graph Neural Network

The prevention and governance of critical risks has been an important national strategic plan. To response the requirements of the national strategic plan, researchers are exploring new methods, models, and tools to address the issues arising from the various risk scenarios in different fields. Facing the increasing safety threat of critical infrastructures (CIs), how to prevent and govern multi-source compound risks to improve CI resilience? Facing the increasing credit demand of real economy, how to conduct credit decision and credit risk management? Facing the increasingly prosperous cryptocurrency market brought about by the rapid development of financial technology, how to look ahead and judge the risks of the volatile digital currency market?
In the prevention and governance process of critical risks, some general issues on risk management and decision-making have attracted much attention. For example, risk scenarios perception is conducted from the perspectives of individual behavior preference, interpersonal risk communication, risk knowledge structures, etc. For another example, compound risk characterization is represented in the form of multi-source, interdependency, and dynamicity, which are modeled by some quantification tools. Recently, some artificial intelligence (AI) technologies, such as knowledge graph, machine learning (ML), deep learning (DL), reinforcement learning (RL), and large language models (LLMs), are used to provide an efficient support for risk data collection and processing, visualize the judgment reference for risk identification and diagnosis, improve the accuracy of risk response decision, and solve the problems on risk monitoring and pre-warning. On the other hand, some application issues derived from the prevention and governance process of critical risks have been studied, such as data-intelligence-driven scenario modeling for risk and emergency decision making, and ML/DL/RL/LLMs-based modelling for CI risks, credit risks and Fintech risks. The main objective of this workshop is to facilitate the exchange of ideas and knowledge among industry practitioners, academic researchers, administrative personnel in government departments, and others interested in applications of decision-analysis methods in various risk scenarios.
Topics of interest include, but are not limited to, the following:

• Risk and emergency decision making
• Risk scenario perception
• Data-intelligence-driven risk modeling
• AI-based compound risk decision
• Risk data collection and processing
• Risk identification and diagnosis
• Risk monitoring and pre-warning
• ML/DL/RL/LLMs-based modelling in CI/finance risk management
• Credit risk decision
• Bayesian risk decision
• Cryptocurrency market risks
• Fintech risk management

With the rapid development of emerging information technology and digital economy, data has become an important element in the national governance system. It is of strategic significance to promote data security governance in a coordinated manner. At the same time, data elements are increasingly becoming an important driving force for high-quality development of the financial industry. Recently, data essentialization and trading, data security and risk assessment, data asset pricing, data governance policy evaluation and financial innovation are among the areas where management science and financial engineering researchers focus on. The main purpose of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the area of data essentialization and financial innovation. This workshop aims to promote in-depth research on theoretical and practical issues towards data governance and financial innovation, to generate new insights in this relatively under-researched area, and to propose meaningful future research directions. The workshop is interested in topics related to all aspects of data essentialization and financial innovation, which includes, but are not limited to, the following:

The workshop will focus on, but not limited to, the following topics:

• Data elements and digital finance
• Data elements and fintech innovation
• Marketization of data elements and digital transformation of finance
• Data elements and industrial development
• Data pricing and accommodation trading mechanism
• Data security and intelligent monitoring model
• Data circulation and risk management
• Intelligent evaluation of data governance policies
• System complexity of data governance
• Theories and methods of data element governance
• Tracking of cutting-edge technologies in data element market

With the development of big data and artificial intelligence technology, the digital transformation of enterprises has become an inevitable trend. Business intelligence plays an important role in discovering the knowledge behind the data and providing the management decision support for enterprises. Especially in the era of big data, how to use the new artificial intelligence technology to improve the quantitative management ability of enterprises has become the key point to the digital transformation of enterprises and the improvement of management decision-making ability.

The session is meant to include papers that contain recent results obtained by research teams from academia and industry concerning but not limited to the following topics:

• New Algorithm of Business intelligence
• Business intelligence based on big data
• Business intelligence for enterprise management
• Business intelligence for financial management
• Business intelligence in complexity system

New real world applications of artificial intelligence and machine learning have shown that popular methods may appear to be too simple and restrictive. Mining more complex, larger and generally speaking “more difficult” data sets pose new challenges for researchers and ask for novel and more complex approaches. We organize this workshop where we want to promote research and discussion on more complex and advanced methods for the particularly demanding operational research and machine learning problems. Although we welcome submissions concerning methods based on different principles, we also expected to see new research in it about the use of optimization techniques. The new challenges emerging in artificial intelligence are definitely more complex than traditional ones and they could result in more difficult non-convex optimization formulations. We would like to focus interest of artificial intelligence community on various challenging issues which come up while using complex methods to deal with the difficult machine learning problems.

Potential research topics include, but are not limited to the following:

• Optimization methods for machine learning
• Combined classifiers for complex learning problems
• Discussion on using artificial intelligence methods to solve problems in operational research
• Application of optimization in modeling and algorithm design of artificial intelligence
• New methods for constructing and evaluating on-line recommendation
• Mining spatial data and images
• Complex data representation learning
• Artificial intelligence driven combinational optimization

As is known to all, energy economics is a subfield of economics that focuses on energy relationships as the foundation of all other relationships. The field can arise from a number of disciplines, including economic theory, financial economics, computational economics, statistics, econometrics, operational research and strategic modeling. A wide interpretation of the subject includes, for example, issues related to forecasting, financing, pricing, investment, development, conservation, policy, regulation, security, risk management, insurance, portfolio theory, taxation, fiscal regimes, accounting and the environments. In these listed issues there are a large number of computational problems to be solved for the energy systems, particular for energy risk measurement and management. This will be the eleventh workshop for such a subject that provides a premier and open forum for the dissemination of innovative computational methods as well as original research results in energy economics and energy risk management.

In order to provide an academic exchange platform, the First International Workshop on Computational Methods in Energy Economics (CMEE 2007) was held in Beijing on May 27-30, 2007. Subsequently, the Second, Third, Fourth, Fifth, Sixth, Seventh, Eighth, Ninth, Tenth, Eleventh, Twelfth, Thirteenth, Fourteenth and Fifteenth International Workshop on Computational Methods in Energy Economics (CMEE 2008, CMEE 2009, CMEE 2010, CMEE 2011, CMEE 2012, CMEE 2013, CMEE 2015, CMEE 2016, CMEE 2017, CMEE2021, CMEE2022, CMEE2023, CMEE2024 and CMEE2025) were held in Nanjing, Sanya, Huangshan, Kunming, Harbin, Rio De Janeiro (Brazil), Asan (Korea), New Delhi (India), Chengdu, Beijing, Oxford(UK),Bucharest(Romania) and Piscataway (USA) on June 27-30, 2008, April 24-26, 2009, May 28-31, 2010, April 15-19, 2011, June 24-26, 2012, May 16-18, 2013, July 21-24, 2015, August 16-18, 2016, December 8-10, 2017, July 9-11, 2021, December 9-11, 2022, August 12-14, 2023, August 23-25, 2024, and August 15-17, 2025. To promote the idea-exchange and discussion of this field, the Sixteenth International Workshop on Computational Methods in Energy Economics (CMEE 2026) will be held in Rouen, France. The organizers solicit all interested academic researchers and industrial practitioners to submit their recent research results to this workshop within the scope of the following topics.

The workshop will provide an open forum for research papers concerned with the computational problems on energy economics and energy risk management, including economic and econometric modeling, computation, and analysis issues in energy systems.

The workshop will focus on, but not limited to, the following topics:

• Forecasting models for energy prices (oil, coal, gas, electricity, etc.)
• Pricing models in energy markets (mean reversion, jump diffusion)
• Investment analysis models in energy projects (portfolio theory)
• Econometric modeling for energy demands
• Energy and environment policy modeling
• Modeling strategic behavior for energy security
• Hybrid energy-economy models for energy policy simulation
• Statistical analysis of energy cost, energy consumption and economic growth
• Energy risk management (risk measurement, hedging strategy and instruments)
• Large Language Model in energy economics research
• Foundation models in energy economics research

As E-science has emerged as a persistent and increasingly large part of the research enterprise, scientists are exploring new roles, services, staffing, and resources to address the issues arising from this new mode of research. Scientists use computer modeling and simulation programs to test and produce new theories and experimental techniques, often generating and accumulating vast amounts of data. Ideally, that data could be shared with other scientists, for re-use and re-analysis, ultimately speeding up the process of scientific discovery. The collection and utilization of scientific data are the two primary features that characterize e-Science. The scientific data are generated by different aspects and departments in the management activities of research institutions and are decentralized-managed and separated-stored, which generates the difficult to share and manage the scientific data. Furthermore, the global sharing of data has promoted interdisciplinary teamwork on complex problems and has enabled other researchers to use data for different purposes. The main objective of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the area of data science and decision analysis for e-science, especial the ecological animal husbandry. The workshop aims to create a communication platform for researchers to share recent and significant developments in the general area.

Topics of interest include, but are not limited to, the following:

• Metadata standard for scientific data
• Scientific data quality analyzing
• Scientific data integration and sharing
• ETL process for scientific data
• Disambiguation of scientific data
• Scientific data visualization
• Decision analysis modeling from scientific data
• Network analysis from scientific data
• Bibliometrics analysis from scientific data
• Scientometrics analysis from scientific data

The fields of data mining and mathematical programming are increasingly intertwined. Optimization problems lie at the heart of most data mining approaches. For last several years, the researchers have extensively applied quadratic programming into classification, known as V. Vapnik's Support Vector Machine, as well as various applications. However, using optimization techniques to deal with data separation and data analysis goes back to more than many years ago. According to O. L. Mangasarian, his group has formulated linear programming as a large margin classifier in 1960's. In 1970's, A. Charnes and W.W. Cooper initiated Data Envelopment Analysis where a fractional programming is used to evaluate decision making units, which is economic representative data in a given training dataset. From 1980's to 1990's, F. Glover proposed a number of linear programming models to solve discriminant problems with a small sample size of data. Then, since 1998, the organizer and his colleagues extended such a research idea into classification via multiple criteria linear programming (MCLP) and multiple criteria quadratic programming (MQLP), which differs from statistics, decision tree induction, and neural networks. So far, there are more than 100 scholars around the world have been actively working on the field of using optimization techniques to handle data mining and web intelligence problems. This workshop intends to promote the research interests in the connection of optimization, data mining and web intelligence as well as real-life applications.

Shingo Aoki
Osaka Prefecture University, Japan aoki@ie.osakafu-u.ac.jp
Wanpracha Art Chaovalitwongse
Rutgers, the State University of New Jersey, USA wchaoval@rci.rutgers.edu
Masato Koda
University of Tsukuba, Japan Koda@sk.tsukuba.ac.jp
Gang Kou
University of Electronic Science and Technology of China, China kougang@uestc.edu.cn
Kin Keung Lai
City University of Hong Kong, Hong Kong, China mskklai@cityu.edu.hk
Heeseok Lee
Korea Advanced Institute Science and Technology, Korea
David Olson
University of Nebraska at Lincoln, USA Dolson3@unl.edu
Jiming Peng
University of Illinois at Urbana-Champaign, USA pengj@uiuc.edu
Yi Peng
University of Electronic Science and Technology of China, China pengyi@uestc.edu.cn
Lingfeng Niu
Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science niulfster@gmail.com
John Wang
Montclair State University, USA wangj@mail.montclair.edu
Ning Zhong
Maebashi Institute of Technology, Japan zhong@maebashi-it.ac.jp
Xiaofei Zhou
Institute of Computing Technology, Chinese Academy of Sciences, China exiaofei@gmail.com
Jianping Li
Institute of Policy & Management, Chinese Academy of Sciences, China ljp@casipm.ac.cn
Peng Zhang
Institute of Computing Technology, Chinese Academy of Sciences, China nczhang1999@163.com

Recently, artificial intelligence (AI) has emerged as the defining cultural technology of our time. Moving beyond mere computational tools, AI systems—through their algorithms, models, and applications—are actively reshaping the very fabric of human culture: how we create, interpret, preserve, and transmit knowledge, art, and social practices. This workshop posits AI as a “Cultural Engine”, a transformative force that both draws from and regenerates tradition, opening new frontiers for expression, inquiry, and societal development. We seek to explore this dynamic interplay, examining how cultural contexts shape AI and, conversely, how AI is redefining cultural boundaries, values, and possibilities for the future.

The main purpose of this workshop is to provide researchers and practitioners a dedicated forum to share the most recent advances at the intersection of AI and cultural transformation, to promote in-depth inquiry into frontier theoretical and practical issues, to generate innovative outcomes in this dynamically emerging area, and to chart directions for future interdisciplinary research. The workshop is interested in topics related to all aspects of AI as a cultural engine, which includes, but is not limited to, the following:

• AI and the Digital Transformation of Cultural Heritage
• Algorithmic Creativity in Arts, Music, and Design
• Data-Driven Insights into Cultural Trends and Social Behaviors
• Ethical AI and the Preservation of Cultural Diversity
• Generative Models and the Future of Narrative and Storytelling
• AI in Digital Humanities and Societal Impact Studies
• Cultural Dimensions of Human-Computer Interaction and UX

The growing complexity of financial activities has made it increasingly difficult to identify, measure, and govern financial risks. Among these, asymmetric financial risk is a distinct type of risk that financial institutions passively assume, characterized by negative net returns. It originates from complex fund transactions, such as illegal fundraising, money laundering, and telecom fraud, posing a severe threat to financial security. Traditional approaches to risk identification, measurement and governance are challenged by insufficient early warning capabilities and limited identification accuracy.

In response to these challenges, there is an urgent need to adopt more advanced methods, techniques and paradigms for financial risk measurement and regulation. State-of-the-art artificial intelligence technologies, including large language models and graph neural network, offer unprecedented potential for analyzing vast volumes of different types of data and uncovering hidden risk patterns, particularly those associated with asymmetric financial risks. Furthermore, breaking down data silos and establishing cross-departmental collaborative mechanisms are essential for enabling effective penetrative supervision.

This workshop will focus on the measurement and governance financial risk, especially the asymmetric financial risks. Key topics of interest include, but are not limited to:

• Identification and measurement of asymmetric financial risks using AI methods
• Methods and techniques for asymmetric financial risk governance
• Financial risk analysis with multi-source data/big data
• Financial risk measurement and governance with AI methods
• Financial risk identification based on transaction knowledge graph
• Large language models for financial risk analysis
• Coordinated financial regulation and governance
• Governance strategies simulation and optimization
• Data sharing method and strategies in financial regulation

The financial industry and the broader private-sector corporate landscape stand at the precipice of a revolution driven by Artificial Intelligence (AI). AI technologies, particularly machine learning (ML), deep learning (DL), reinforcement learning (RL), and large language models (LLMs), are profoundly reshaping the theory and practice of financial risk management, policy analysis, operational decision-making, and portfolio optimization. AI is revolutionizing financial institutions and enterprises alike by empowering the intelligent processing of high-dimensional market, corporate, and policy data, deciphering complex non-linear financial, enterprise risk, and policy relationships, as well as generating dynamic, adaptive investment, operational, and governance strategies. It thus provides pivotal tools to tackle the uncertainty, complexity, and real-time demands that challenge traditional models in both financial markets and corporate operations.

This workshop aims to provide a premier platform for global scholars, financial industry practitioners, technology experts, and regulatory researchers to exchange insights on the latest research, innovative applications, and critical challenges in applying AI to the frontiers of financial and enterprise risk management, policy analysis, and asset allocation. We will focus on pressing issues such as the interpretability of AI models, their robustness in extreme market scenarios, integration with financial and economic theory, their integration with corporate governance and policy frameworks, and the ethical and compliance considerations in deploying these models in real-world business settings. We sincerely invite original research contributions from both academia and industry to collectively advance the knowledge frontier and practical progress in this interdisciplinary field.

Topics of interest include, but are not limited to, the following:

• AI-enabled financial risk modeling and management
• Applications of RL in dynamic risk hedging and capital allocation
• Utilization of LLMs for risk reporting, sentiment analysis, and regulatory compliance
• AI risk modeling for emerging domains like cryptocurrencies and digital assets
• AI-driven portfolio optimization and algorithmic trading
• Data-driven smart asset allocation and multi-factor models
• Adaptive and robust investment strategies using RL
• AI for prediction and execution optimization in high-frequency and quantitative trading
• AI-powered portfolio construction integrating ESG (Environmental, Social, and Governance) factors
• Mining and application of alternative data (e.g., satellite imagery, social media text) in investment strategies
• Explainable AI methods and Regulatory Technology (RegTech) for financial models
• Integration and validation of AI models with traditional financial theory
• Graph Neural Networks (GNN) for analyzing financial networks and systemic risk contagion
• Governance frameworks to ensure fairness, compliance, and robustness of AI models
• AI-based analysis of corporate disclosure, financial reporting, and transparency policies
• AI for corporate investment, financing, and capital allocation policy design
• LLM-based corporate governance and board decision analytics
• AI-driven policy text mining and regulatory compliance analytics
• AI for corporate sustainability, ESG policy formulation, and reporting
• AI for policy impact evaluation and corporate behavioral prediction

Enterprise operations are confronting increasingly complex systemic risks amid geopolitical conflicts, economic sanctions, technological decoupling, and other external shocks. These risks not only threaten production and trade flows but may also propagate along operational networks, impacting the security and resilience of industrial systems. At the same time, advances in digital intelligence technologies—including big data analytics, artificial intelligence, knowledge graphs, and causal inference methods—offer new tools and approaches for risk identification, contagion modeling, and operational optimization. Through systematic modeling and data-driven analysis, digital intelligence can identify critical vulnerabilities, simulate risk propagation pathways, and support the design and optimization of recovery strategies across various operational domains.

This special session aims to provide a platform for scholars, industry practitioners, policymakers, and technology experts to exchange and share research and practical insights on the application of digital intelligence in enterprise operations management. The session will particularly focus on the use of digital intelligence to enhance information systems management, optimize production processes, safeguard industrial system security, strengthen operational resilience, and support risk management and recovery strategies in complex and dynamic environments. We look forward to contributions from both academia and industry to advance the theory and practice in this emerging field.
Topics of interest include, but are not limited to:

• Data-Intelligence-Driven Supply Chain Risk Identification and Early Warning
• Intelligence-Driven Industrial Chain Security Strategies and Resilience Optimization
• AI-Based Critical Node Identification and Risk Control in Operations Networks
• Data-Intelligence-Driven Operational Resilience Assessment and Real-Time Monitoring
• AI and Big Data Applications in Production Anomaly Detection and Event Response
• Data-Intelligence-Assisted Decision Support Systems for Operations Management
• AI-Driven Operational Disruption Prediction and Mitigation Strategy Design
• Intelligent Algorithm-Based Optimization of Production Planning and Resource Allocation
• Digital Intelligence in Enterprise Information Systems Management and Process Optimization
• Smart Manufacturing and AI-Enabled Production Process Control
• Knowledge Graph Applications in Operations Risk Modeling and Visualization
• Causal Inference Methods for Operations Management Strategy Evaluation