報告時間：2024年11月9日（星期六）14:00-17:00
報告地點：合肥工業大學工程管理與智能制造研究中心第三學術報告廳
舉辦單位：管理學院
學術報告信息（一）
報告題目：醫學大數據挖掘及AI在疾病診斷及病人監測預警健康管理的創新應用/Innovative Applications of Medical Big Data Mining and AI in Disease Diagnosis, Patient Monitoring and Warning Health Management
報告時間：2024年11月9日（星期六）14:10-14:40
報 告 人：張彥春 教授
工作單位：維多利亞大學
報告簡介：
醫療健康是目前人工智能和大數據最為關注的領域。人工智能+醫療大數據將對醫療產業賦予新的能量與機會，是將機器學習和數據挖掘等技術用于醫療健康數據，提高醫療診治與健康管理水平，體現在智能輔助診斷、疾病風險預測、醫學圖像分析腫瘤監測、 藥物挖掘、健康管理等。本報告將從大數據分析/人工智能及應用的角度出發，探討生命各階段的健康分析，人體和疾病各因素之間的關系。通過實例介紹基于醫學數據的數據集成、數據挖掘、數據關聯分析及病人監測與分析預警。應用場景將包括睡眠健康/精神健康、心電分析、手術重癥分析、醫學圖像分析、腫瘤檢測等應用。
報告人簡介：
張彥春教授于1991年獲得澳大利亞昆士蘭大學計算機科學博士學位。多年來一直從事社會計算和電子健康，大數據與AI算法與應用研究工作，在信息技術及醫學領域發表國際期刊和學術會議文400多篇。已經出版5本專著，編輯書刊和專輯20多部，完成指導相關方向40多名博士生和博士后。研究成果被廣泛引用并已產生較大社會影響。比如張教授團隊的病人監測預警研究在多家中英媒體報道，包括The Australian, The Age，Brisbane Time，Sydney Morning Herold，China Daily, ChinaNews，XinhuaNet等。張教授目前擔任國際萬維網期刊 (World Wide Web)主編, 國際健康信息科學及系統期刊（Health Information Science and Systems）主編, 國際互聯網信息系統工程協會（WISE Society）主席，曾獲得多項國家/國際專家稱號，包括國家特聘準家、中國科協海智特聘專家、澳大利亞研究理事會專家委員會委員、新西蘭馬斯登基金評審專家、英國UKRI醫學研究理事會評審專家等稱號/職務。目前是英國皇家醫學會會士。
學術報告信息（二）
報告題目：從關聯到因果-可解釋數據分析/From Association To Causation - Explainable Data Analytics
報告時間：2024年11月9日（星期六）14:40-15:10
報 告 人：徐貫東 教授
工作單位：香港教育大學
報告簡介：
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.
報告人簡介：
徐貫東教授，香港教育大學人工智能講座教授，澳大利亞悉尼科技大學計算機學院教授。徐教授的創新研究榮獲澳大利亞研究理事會、政府機構和產業界超過一千萬澳元的科研和項目資助，其創新研究榮獲多項國際榮譽，在享譽國際的期刊和計算機頂級會議上發表逾數百篇論文，連續多年名列世界首2%科學家，研究成果受廣泛引用。他是《以人為中心的智慧系統期刊》（Springer）的創始主編，現亦擔任《萬維網期刊》（Springer）副主編。徐教授創辦了國際行為和社會計算學術會議，致力推動交叉學科的學術研究。徐教授分別于2021年和2022年當選為英國工程技術學會（IET）會士和澳大利亞電腦學會（ACS）會士。
學術報告信息（三）
報告題目：我的顧問、她的人工智能與我：基于人智協同與投資決策現場試驗的實證研究/My Advisor, Her AI and Me: Evidence from a Field Experiment on Human-AI Collaboration and Investment Decisions
報告時間：2024年11月9日（星期六）15:10-15:40
報 告 人：李喜彤 教授
工作單位：巴黎高等商學院
報告簡介：
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.
報告人簡介：
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.
學術報告信息（四）
報告題目：面向Web 3的智慧教育及落地/Smart Education in Web 3 and its Commercialisation
報告時間：2024年11月9日（星期六）16:00-16:30
報 告 人：沈俊 教授
工作單位：伍倫貢大學
報告簡介：
開放教育資源OER共享的研究已經多年，但其是否能可持續發展一直存在爭議。一些前期工作提出基于機器智能的微學習或知識提取等解決方案，但他們仍然沒法解決底層的開銷問題。本演講介紹一種基于Web3經濟模型的新型框架，旨在建立面向未來的OER生態系統。
報告人簡介：
沈俊教授博士畢業于東南大學，在多所澳大利亞大學工作之后現為伍倫貢大學全職教授，他的專長在于機器智能多領域應用，包括教育、交通、制造和生物信息學等。他曾擔任麻省理工學院和佐治亞理工學院訪問教授。目前擔任四種一區雜志編委，已發表論文400余篇。沈俊教授是ACM和IEEE雙料高級會員、IEEE杰出講員，是ACM/AIS課程MSIS2016評議組成員，曾擔任400多次雜志評審或國際會議程序委員。
學術報告信息（五）
報告題目：邁向主動式人工智能/Moving Toward Proactive Artificial Intelligence
報告時間：2024年11月9日（星期六）16:30-17:00
報 告 人：白佺 副教授
工作單位：塔斯馬尼亞大學
報告簡介：
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.
報告人簡介：
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.
