报告时间：2024年11月9日14:00-17:00（北京时间）
报告地点：合肥工业大学工程管理与智能制造研究中心第三学术报告厅
举办单位：管理学院
学术报告信息（一）
报告题目：医学大数据挖掘及AI在疾病诊断及病人监测预警健康管理的创新应用/Innovative Applications of Medical Big Data Mining and AI in Disease Diagnosis, Patient Monitoring and Warning Health Management
报告时间：2024年11月9日（星期六）14:10-14:40
报告人：张彦春，维多利亚大学，教授
个人简介：张彦春教授予1991年获得澳大利亚昆士兰大学计算机科学博士学位。多年来一直从事社会计算和电子健康，大数据与AI算法与应用研究工作，在信息技术及医学领域发表国际期刊和学术会议文400多篇。已经出版5本专著，编辑书刊和专辑20多部，完成指导相关方向40多名博士生和博士后。研究成果被广泛引用并已产生较大社会影响。比如张教授团队的病人监测预警研究在多家中英媒体报道，包括The Australian, The Age，Brisbane Time，Sydney Morning Herold，China Daily, ChinaNews，XinhuaNet等。张教授目前担任国际万维网期刊(World Wide Web)主编,国际健康信息科学及系统期刊（Health Information Science and Systems）主编,国际互联网信息系统工程协会（WISE Society）主席，曾获得多项国家/国际专家称号，包括国家特聘准家、中国科协海智特聘专家、澳大利亚研究理事会专家委员会委员、新西兰马斯登基金评审专家、英国UKRI医学研究理事会评审专家等称号/职务。目前是英国皇家医学会会士。
报告内容：医疗健康是目前人工智能和大数据最为关注的领域。人工智能+医疗大数据将对医疗产业赋予新的能量与机会，是将机器学习和数据挖掘等技术用于医疗健康数据，提高医疗诊治与健康管理水平，体现在智能辅助诊断、疾病风险预测、医学图像分析肿瘤监测、药物挖掘、健康管理等。本报告将从大数据分析/人工智能及应用的角度出发，探讨生命各阶段的健康分析，人体和疾病各因素之间的关系。通过实例介绍基于医学数据的数据集成、数据挖掘、数据关联分析及病人监测与分析预警。应用场景将包括睡眠健康/精神健康、心电分析、手术重症分析、医学图像分析、肿瘤检测等应用。
学术报告信息（二）
报告题目：从关联到因果-可解释数据分析/From Association To Causation - Explainable Data Analytics
报告时间：2024年11月9日（星期六）14:40-15:10
报告人：徐贯东，香港教育大学，教授
个人简介：徐贯东教授，香港教育大学人工智能讲座教授，澳大利亚悉尼科技大学计算机学院教授。徐教授的创新研究荣获澳大利亚研究理事会、政府机构和产业界超过一千万澳元的科研和项目资助，其创新研究荣获多项国际荣誉，在享誉国际的期刊和计算机顶级会议上发表逾数百篇论文，连续多年名列世界首2%科学家，研究成果受广泛引用。他是《以人为中心的智慧系统期刊》（Springer）的创始主编，现亦担任《万维网期刊》（Springer）副主编。徐教授创办了国际行为和社会计算学术会议，致力推动交叉学科的学术研究。徐教授分别于2021年和2022年当选为英国工程技术学会（IET）会士和澳大利亚电脑学会（ACS）会士。
报告内容：In recent years, AI has demonstrated super-human performance in image processing, speech analysis, natural language processing and many more. Unfortunately, existing state-of-the-art models lack transparency and interpretability, which impedes data analytics from being applied in many traditional fields such as the medical, finance and politics. Consequently, explainable data analytics has been widely considered by academics and industry and is expected to become an important direction. Although some studies have largely advanced the explainability research landscape, they still lack causal interpretations that are needed for humans to understand the truth.
This talk will start with a review of state-of-the-art explainable data analytics, which is devoted to exploiting the black-box nature of AI models to justify the model's reliability. Then, we report our recent research on causal recommendation and causal learning for explainable via causal graph, prior privilege information and transfer learning. The talk ends up with discussions on some open questions and promising directions towards high-quality Interpretable AI.
学术报告信息（三）
报告题目：我的顾问、她的人工智能与我：基于人智协同与投资决策现场试验的实证研究/My Advisor, Her AI and Me: Evidence from a Field Experiment on Human-AI Collaboration and Investment Decisions
报告时间：2024年11月9日（星期六）15:10-15:40
报告人:李喜彤，巴黎高等商学院，教授
个人简介：Dr. Xitong Li is a professor of information systems at HEC Paris and a research fellow of Hi! PARIS, the joint research center between HEC Paris and Polytechnic Institute of Paris. His primary research interests are in the economics of information and AI technologies, including social media, FinTech, digital marketing, online education, human-AI/algorithms collaboration. His primary research methods include applied econometric analysis, field and laboratory experiments. Xitong’s research appears in leading international journals, such as Management Science, Information Systems Research, Management Information Systems Quarterly, Production and Operations Management, Journal of Management Information Systems, and various ACM/IEEE Transactions. Xitong’s research has been granted by ANR AAPG France (solo PI), equivalent to National Science Foundation (NSF) in the U.S., for 2018-2023. His research has also been granted by Hi! PARIS Research Fellowship for 2021-2025. Xitong currently serves as an Associate Editor for Information Systems Research, a top journal in the information systems field. He also served as a guest senior editor for Production and Operations Management, a top journal in the operations management field.
Xitong received INFORMS Information Systems Society (ISS) Sandy Slaughter Early Career Award in 2022, and the HEC Foundation Researcher of the Year Award in 2023.
报告内容：Contributing to current policy and academic debates about bringing humans in the loop of Artificial intelligence (AI), we explore whether allowing humans to collaborate with AI in the AI-based service production, compared to a pure AI solution, benefits the service production and consumption side. We conduct a field experiment with a large savings bank and produce pure AI-based and human-AI collaborative investment advice to the bank's customers. On the production side, we find that implementing a human-AI collaboration by allowing bankers to have the final say with AI output does not compromise advice quality. More importantly, on the consumption side, we find that the customers are more likely to align their final investment decisions with advice from this human-AI collaboration, compared to pure AI, especially when making more risky investments. The higher reliance on human-AI collaborative advice also translates to higher monetary payoffs. Overall, the results from the field experiment suggest that bringing humans into the AI-based advisory service production is pivotal to allowing AI-enabled efficiency gains to transmit to downstream customers. In a complementary online experiment, we further uncover the mechanism underlying customers' higher reliance on bankers' participation in generating investment advice. We find that the persuasive efficacy of human-AI collaborative advice stems from social influence on the customers. Our findings not only offer new insights for companies contemplating the provision of pure AI-based services, but also enrich policy and regulatory discussions by demonstrating the value of humans in AI-based service production.
学术报告信息（四）
报告题目：面向Web 3的智慧教育及落地/Smart Education in Web 3 and its Commercialisation
报告时间：2024年11月9日（星期六）16:00-16:30
报告人：沈俊，伍伦贡大学，教授
个人简介：沈俊教授博士毕业于东南大学，在多所澳大利亚大学工作之后现为伍伦贡大学全职教授，他的专长在于机器智能多领域应用，包括教育、交通、制造和生物信息学等。他曾担任麻省理工学院和佐治亚理工学院访问教授。目前担任四种一区杂志编委，已发表论文400余篇。沈俊教授是ACM和IEEE双料高级会员、IEEE杰出讲员，是ACM/AIS课程MSIS2016评议组成员，曾担任400多次杂志评审或国际会议程序委员。
报告内容：开放教育资源OER共享的研究已经多年，但其是否能可持续发展一直存在争议。一些前期工作提出基于机器智能的微学习或知识提取等解决方案，但他们仍然没法解决底层的开销问题。本演讲介绍一种基于Web3经济模型的新型框架，旨在建立面向未来的OER生态系统。
学术报告信息（五）
报告题目：迈向主动式人工智能/Moving Toward Proactive Artificial Intelligence
报告时间：2024年11月9日（星期六）16:30-17:00
报告人：白佺，塔斯马尼亚大学，副教授
个人简介：Associate Professor Quan Bai received his PhD (2007) and MSc (2002) from the University of Wollongong, Australia. After he received his PhD, Quan worked as a Postdoctoral Research Follow for the University of Wollongong (2007-2009), and for the Commonwealth Scientific and Industrial Research Organisation (CSIRO) (2009-2011). In May 2011, Associate Professor Quan Bai joined the School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology as a lecturer, and then in 2012, promoted to a senior lecturer. Since March 2019, Quan Bai has been with the School of ICT, University of Tasmania, as an associate professor.
Quan Bai is a distinguished expert in agent-based modelling and multi-agent coordination, is at the forefront of cutting-edge research. His work focuses on the application of advanced AI methodologies to model intricate systems comprising numerous complex and interdependent components. Driven by the goal of orchestrating self-interested agents towards optimal outcomes, he has a remarkable record of over 170 high-quality publications in related fields and has secured research funding exceeding 2 million AUDs, including prestigious NHMRC grants. Bai currently leads a dynamic AI research group at UTAS.
报告内容：As AI continues to evolve, a significant shift is occurring from reactive systems to proactive AI models. Unlike traditional AI, which only responds to inputs and passively learns from human interactions, proactive AI can initiate actions based on contextual understanding and predictive analytics. It has the ability to influence external environments and even shape human behaviors. This shift promises to enhance efficiency, improve decision-making, and offer greater personalization across various fields, including healthcare, sustainability, and smart infrastructure. By harnessing advancements in machine learning, distributed AI, and generative AI, proactive AI can deliver more intuitive, autonomous systems.
