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                                                                  您的位置:首頁 > 新聞動態 > CCF聚焦

                                                                  ADL99《空間大數據與人工智能》開始報名了

                                                                  閱讀量:277
                                                                  2019-05-22

                                                                  CCF學科前沿講習班

                                                                  The CCF Advanced Disciplines Lectures

                                                                  CCFADL99

                                                                  主題 空間大數據與人工智能

                                                                  2019615-17

                                                                  北京·中國科學院計算技術研究所


                                                                  隨著云計算、物聯網、移動計算、大數據、智慧城市等新興信息通訊技術的迅猛發展,空間大數據及其人工智能分析已經成為一個炙手可熱的重要研究方向,在諸如城市大腦、智能交通、重大科學裝置數據處理、數字化戰場、智能安防、工業大數據與工業互聯網、應急管理及國土監測等關鍵應用中發揮了重要作用。

                                                                  本期CCF學科前沿講習班《空間大數據與人工智能》,講授當前空間大數據及人工智能領域研究的最新進展和典型示范應用,旨在幫助學員快速了解和學習該領域的研究熱點和前沿技術,掌握學科發展動向和重要的應用方法,開闊科研視野,增進學術交流,增強實踐能力。

                                                                  本期講習班邀請到了本領域5位來自于海內外著名高校與科研機構的重量級專家學者做主題報告。他們將對空間大數據與人工智能的基礎算法、關鍵技術、核心應用及當前熱點問題進行深入淺出的講解,并對如何開展本領域前沿技術研究等進行指導,使參加者在了解學科熱點、提高理論水平的同時,掌握最新技術趨勢。

                                                                  特別提醒:2019年6月15日上午在本屆ADL同一地點舉辦ACM SIGSPATIAL’2020 Pre-Workshop國際學術研討會,歡迎本期ADL學員免費參加。

                                                                  學術主任:丁治明 中國科學院軟件研究所

                                                                  主辦單位:中國計算機學會

                                                                  活動日程

                                                                  2019615日下午(周六)

                                                                  13:30-13:45

                                                                  開班儀式

                                                                  13:45-14:00

                                                                  合 影

                                                                  14:00-17:00

                                                                  專題講座1:時空大數據的智能質量增強與行為預測

                                                                  李 勇,清華大學,副教授

                                                                  2019616日(周日)

                                                                  09:00-12:00

                                                                  專題講座2Data Driven Smarter Urban Transportation Systems

                                                                  黃 艷,University of North Texas教授

                                                                  12:00-13:30

                                                                  午 餐

                                                                  13:30-16:30

                                                                  專題講座3Privacy and Security Challenges of Spatiotemporal Data and AI

                                                                  熊 莉,Emory University教授

                                                                  2019617日(周一)

                                                                  09:00-12:00

                                                                  專題講座4個性化推薦系統

                                                                  謝 幸,微軟亞洲研究院,首席研究員

                                                                  12:00-13:30

                                                                  午 餐

                                                                  13:30-16:30

                                                                  專題講座5:面向城市安全管理的時空數據分析與智能決策

                                                                  王靜遠,北京航空航天大學,副教授

                                                                  16:30-17:00

                                                                  結業式

                                                                  l  專題講座均包含兩次15分鐘課間休息


                                                                  謝幸  微軟亞洲研究院 首席研究員、中國科學技術大學 博士生導師

                                                                  講者簡介:謝幸博士于2001年7月加入微軟亞洲研究院,現任首席研究員,中國科技大學兼職博士生導師,以及微軟-中科大聯合實驗室主任。他1996年畢業于中國科技大學少年班,并于2001年在中國科技大學獲得博士學位,師從陳國良院士。目前,他的團隊在數據挖掘、社會計算和普適計算等領域展開創新性的研究。他在國際會議和學術期刊上發表了250余篇學術論文,共被引用22000余次,H指數71,1999年獲首屆微軟學者獎,多次在KDD、ICDM等頂級會議上獲最佳論文獎,并被邀請在MDM 2019, HHME 2018, ASONAM 2017、Mobiquitous 2016、SocInfo 2015、W2GIS 2011等會議做大會主題報告。他是ACM、IEEE高級會員和CCF杰出會員,多次擔任頂級國際會議程序委員會委員和領域主席等職位。他是ACM Transactions on Social Computing, ACM Transactions on Intelligent Systems and Technology、Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)、Springer GeoInformatica、Elsevier Pervasive and Mobile Computing、CCF Transactions on Pervasive Computing and Interaction等雜志編委。他參與創立了ACM SIGSPATIAL中國分會,并曾擔任ACM UbiComp 2011、PCC 2012、IEEE UIC 2015、以及SMP 2017等大會程序委員會共同主席。

                                                                  報告題目:個性化推薦系統

                                                                  報告摘要:Information overload has become a huge challenge for online users, especially for mobile users, due to the small screen size and uncomfortable inputting methods. In order to alleviate this problem, recommendation systems play an increasingly important role in Internet services, and is a constant hot topic in industry and academia. At the same time, with the rapid development of positioning, mobile and sensing technologies, large quantities of human behavioral data are now available. They reflect various aspects of human activities in the physical word, greatly improving the performance of personalized recommendation systems. In this talk, I will introduce the history of personalized recommendation systems and the challenges that are currently encountered, including the heterogeneity, sparsity, and lack of interpretability of human behavioral data. I will present how we improve the recommendation performance by leveraging the recent progress in deep learning, natural language understanding, and knowledge graph. We believe that personalized recommendation systems will continue to develop in various directions, including effectiveness, diversity, computational efficiency, and interpretability, ultimately addressing the problem of information overload.

                                                                  黃 艷 University of North Texas, 教授

                                                                  講者簡介:Yan Huang received her B.S. degree in Computer Science from Peking University, Beijing, China, 1997 and Ph.D. degree in, Computer Science from University of Minnesota, USA, 2003. She is currently a professor at the Department of Computer Science and Engineering and the Associate Dean for Research and Graduate Studies at College of Engineering of the University of North Texas, Denton, TX, USA. Her research interests include machine learning and data mining especially from geo-referenced datasets such as spatial intelligence, smart city, social media, and transportation data. She has been a visiting scholar of Microsoft Research Asia May – August 2011. During Fall 2011, she visited Fudan University, China. Currently, she is on the Board of Directors of The SSTD Endowment (2014-2019). She is Program Committee Chair of ACM SIGSPATIAL 2020, was the general chair of SSTD 2017, the General Chair of ACM SIGSPATIAL 2014 and 2015, and on the Executive Committee of ACM SIGSPATIAL (2010-2014). She received Distinguished Service Award from ACM SIGSpatial in 2010. Her research has been/is supported by Office of Naval Research, National Geospatial Intelligence Agency, Texas Advanced Research Program (ARP), Oak Ridge National Lab, National Science Foundation, and Texas Department of Transportation.

                                                                  報告題目:Data Driven Smarter Urban Transportation Systems

                                                                  報告摘要:Urban traffic gridlock is a familiar scene. With the ubiquitous availability of location enabled mobile devices and wireless communication, the time is ripe for a big data driven, dynamic, and smarter urban transportation system. In such a system, vehicles, users, and infrastructures interact with each other and are informed in real-time. They collaborate to avoidMatthew Effect; form seamless traffic flow; allow alternative and convenient means of transportation; and enable realtime ridesharing. In this talk, we will discuss algorithm and trust issues in a large scale real-time ridesharing system and bike flow prediction methods using context and new ways of location presentation learned from large scale movement data.

                                                                  特邀講者:熊 莉 Emory University, 教授

                                                                  講者簡介:Li Xiong is a Professor of Computer Science and Biomedical Informatics at Emory University. She held a Winship Distinguished Research Professorship from 2015-2018. She has a Ph.D. from Georgia Institute of Technology, an MS from Johns Hopkins University, and a BS from the University of Science and Technology of China, all in Computer Science. She and her research lab, Assured Information Management and Sharing (AIMS), conduct research on algorithms and methods for big data management, data privacy and security, in the context of spatiotemporal and health data. She has published over 120 papers and received five best paper awards. She currently serves as associate editor for IEEE Transactions on Knowledge and Data Engineering (TKDE), program co-chair for ACM SIGSPATIAL 2018 and 2020, program vice-chair for IEEE International Conference on Data Engineering (ICDE) 2020, and on many program committees for data science and data security conferences. Her research is supported by National Science Foundation (NSF), AFOSR (Air Force Office of Scientific Research), National Institute of Health (NIH), and Patient-Centered Outcomes Research Institute (PCORI). She is also a recipient of Google Research Award, IBM Smarter Healthcare Faculty Innovation Award, Cisco Research Award, AT&T Research Gift, and Woodrow Wilson Career Enhancement Fellowship.

                                                                  報告題目:Privacy and Security Challenges of Spatiotemporal Data and AI

                                                                  報告摘要:From AI-driven medicine to self-driving cars and smart city, artificial intelligence powered by spatiotemporal data and machine learning is increasingly transforming our lives. Yet there are privacy and security pitfalls that could lead to the disclosure of sensitive data and wrong actions. These include massive collection and usage of personal data without proper privacy protection (e.g. location traces and medical records), model inversion attacks that can infer and recover sensitive training data from a trained model (e.g. reconstruct trajectories from mobility models and faces from face recognition models), to data poisoning attacks that manipulate training data at learning stage to sabotage the model (e.g. manipulate traffic reports in crowdsourcing systems to mislead traffic prediction), to adversarial example attacks that create manipulated data instances at prediction stage to deceive a model (e.g. create toxic signs to deceive self-driving cars). In this lecture, we will study these challenges and the state-of-the-art techniques towards building privacy-enhanced and robust AI in various spatiotemporal applications. We will first review the privacy challenges and introduce differential privacy as a formal privacy notion, and study its techniques and applications in various settings including centralized setting (e.g. the Census Bureau), local setting (e.g. Google and Apple), and federated setting (for multiple organizations). We will then review the adversarial attacks on machine learning algorithms including data poisoning attacks and adversarial example attacks, and study defense techniques such as adversarial example detection and adversarial training in order to build more robust AI systems.

                                                                  李 勇 清華大學電子系數據科學與智能實驗室 副教授

                                                                  講者簡介:李勇,清華大學電子工程系副教授,博士生導師,ACM/IEEE高級會員,長期從事數據科學與智能及網絡系統方面的科研工作。發表學術論文100余篇,文章引用6200余次,4次獲國際會議最佳論文/提名獎,10篇論文入選ESI高被引用論文。入選國家“萬人計劃”青年拔尖人才、中國科協青年人才“托舉工程”計劃,獲2016年IEEE ComSoc亞太區杰出青年學者獎,教育部科技進步一等獎、電子學會自然科學二等獎、吳文俊人工智能優秀青年獎等。

                                                                  報告題目: 時空大數據的智能質量增強與行為預測

                                                                  報告摘要:現代城市面臨交通擁堵、能耗增加、規劃落后等諸多挑戰,隨著智能移動終端的普及,移動終端用戶在電信網絡和LBSN(基于位置的社交網絡)上生成了海量的時空移動數據,使得我們可以分析和研究移動用戶的行為規律,為我們掌握城市基本情況、理解城市發展、提升城市運轉效率提供了契機。

                                                                  針對以上問題,本報告詳解介紹時空移動數據的智能質量增強與用戶行為建模及預測。基于大規模、多維度的時空移動數據(包括用戶標識、地理位置、業務類型等信息),從移動數據處理、行為建模預測及綜合平臺體系構建三方面討論包含數據接入層、數據質量增強層、數據挖掘與AI層及智慧城市應用層的時空大數據模式挖掘與行為預測關鍵技術及系統。

                                                                  王靜遠  北京航空航天大學 副教授

                                                                  講者簡介:北京航空航天大學計算機學院副教授,研究興趣時空數據挖掘與智慧城市。發表學術論文30 余篇,其中包括大數據人工智能領域頂級期刊會議TKDE、KDD、ICDM、AAAI等。申請中國專利10余項,美國專利2項。承擔和參與課題包括:國家自然科學基金重點項目/面上項目/青年項目、973 項目、863“智慧城市(一期/二期)”項目、國家重點研發計劃等國家級科研項目多項。中國計算機學會大數據專委會委員,中國城市科學研究會大數據專委會委員,自動化學會經濟管理專委會SIG委員等。

                                                                  報告題目:面向城市安全管理的時空數據分析與智能決策

                                                                  報告摘要:城市安全是現代城市管理的重要內容,近兩年我國的一些大型城市頻繁發生安全事故,給人民生命財產帶來巨大威脅。時空大數據與人工智能決策為城市安全管理提供了全新的途徑。本報告將從三個角度介紹城市安全管理相關的時空數據分析與智能決策技術,分別是:

                                                                  1、在城市感知方面,介紹基于多源異構時空數據融合的城市感知技術;

                                                                  2、在風險建模方面,介紹基于網絡推斷與傳播分析的城市風險分析技術;

                                                                  3、在管理決策方面,介紹基于可解釋深度學習時空預測與決策支持技術。

                                                                  報告還會以北京、天津、無錫等城市的城市安全管理業務為案例,介紹上述技術在真實城市安全管理工作中的應用。

                                                                  學術主任

                                                                  丁治明 中國科學院軟件研究所 研究員

                                                                  現任中國科學院軟件研究所數據科學與數據智能研究中心主任、研究員、博導。主要研究領域為數據庫與知識庫系統、時空感知大數據系統、物聯網與智慧城市等。曾工作于德國時空數據管理領域的國際知名專家Ralf Hartmut Güting教授團隊;長期在中國科學院系統(計算所、軟件所)學習與工作;2014年8月通過海內外院長招聘擔任北京工業大學計算機學院院長。2018年4月重新引進回中國科學院軟件研究所工作至今。2016年獲國務院政府特殊津貼、青海省“千人計劃”領軍人才,2018年獲北京市特聘教授。是中國計算機學會(CCF)數據庫專委會委員、CCF大數據專委會委員、中國健康大數據產業聯盟常務理事,擔任IEEE Intelligent Transportation System Society的社會交通專委會主席、ACM SIGSpatial China Chapter的副主席,擔任國際刊物IEEE Transactions on Intelligent Transportation Systems、IEEE Intelligent Transportation Systems Magazine的編委。在國內外學術刊物發表論文130余篇,出版3部學術專著,獲6項發明專利、8項軟件著作權,制定國家標準1項,曾獲得北京市科技進步獎、國家優秀科技信息成果獎等榮譽。

                                                                  時間:2019年6月15-17日

                                                                  地點:北京(中國科學院計算技術研究所,北京市海淀區科學院南路6號)


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