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                                                                  CSCW與社會計算暑期學校

                                                                  閱讀量:134
                                                                  2019-05-08

                                                                  CSCW與社會計算暑期學校

                                                                  CSCW & Social Computing Summer School

                                                                  201982-4 上海·復旦大學邯鄲校區光華樓(HGX104

                                                                  主辦:中國計算機學會(CCF

                                                                  承辦:CCF協同計算專業委員會、復旦大學

                                                                  計算機支持的協同工作(Computer Supported Cooperative Work, CSCW)迄今已經歷了三十余年的發展,其旨在探索如何在網絡環境下利用計算機有效地支持社會群體的通信、合作和協商,協同完成社會任務。這種社會群體、網絡、計算設備、信息技術交織在一起的工作模式引發了對于社會與技術、社會科學與計算科學之間相互關系的關注和探索,這也就是“社會計算”(Social Computing)的雛形。社會計算的概念于1994年被首次明確提出,強調計算科學與社會科學的融合,探索如何結合活動理論、扎根理論、人種學、常人方法學、統計推斷等社會科學的理論與方法,以及社會大數據挖掘、統計與機器學習、深度學習、自然語言處理等計算科學的方法與技術分析、理解和解決信息社會中所呈現的社會問題,是計算機科學、管理科學、社會學、心理學、傳播學等交叉融合的前沿研究領域。

                                                                  當前,大量的社會計算系統和平臺持續涌現,包括Facebook、Twitter、微信等新型社會媒體,Wikipedia、GitHub、Stack Overflow等UGC(User Generated Content)社區,眾包平臺和推薦系統等,并在教育、醫療、金融、交通等領域得到廣泛應用。圍繞這些新興社會計算場景中的社會與計算交互問題,CSCW與社會計算領域的研究者結合定性與定量方法從不同側面開展了一系列研究工作,涉及分布式協同模型、算法與系統,社會媒體與社交網絡大數據建模、分析與預測,社會計算環境下的個性化算法如推薦算法和社會匹配系統,面向典型社會場景數據的知識抽取與自然語言理解,人工智能在社會計算空間中的角色和效用,區塊鏈技術的社會效用,大規模開放協作與眾包,社交游戲、科學系統中的機制設計,社會計算數據和系統的安全和隱私機制,社會化應用的架構設計與實現等。

                                                                  本次暑期學校(活動編號:CCF-19-TC26-01T)旨在為正在或即將從事CSCW與社會計算研究的研究生和本科生提供向該領域優秀學者系統學習相關研究方法和了解前沿研究動態的機會,內容涉及支持大規模在線協作的AI技術、人與AI協同交互系統的構建、社交媒體數據挖掘、個性化推薦技術和社會計算中的知識圖譜構建與知識管理,以及研究案例交流。

                                                                  日程安排

                                                                  時間

                                                                  內容

                                                                  82

                                                                  8:00-8:30

                                                                  簽到

                                                                  8:30-8:45

                                                                  開課儀式

                                                                  8:45-9:00

                                                                  合影

                                                                  9:00-12:00

                                                                  Haiyi Zhu, University of Minnesota

                                                                  Title: From Discovery to Design: Creating AI Technologies to Support Massive-Scale Online Collaboration

                                                                  12:00-14:00

                                                                  午餐

                                                                  14:00-17:00

                                                                  沈華偉,中國科學院計算技術研究所

                                                                  題目:社交媒體數據挖掘與信息傳播預測

                                                                  83

                                                                  9:00-12:00

                                                                  Dakuo Wang, IBM Research AI

                                                                  Title: Introduction to Computer Supported Cooperated Work (CSCW) and Human-AI-Collaboration

                                                                  12:00-14:00

                                                                  午餐

                                                                  14:00-17:00

                                                                  Bin Shao, Microsoft Research Asia

                                                                  Title: Parallel Graph Processing and Knowledge Graph Serving

                                                                  84

                                                                  9:00-12:00

                                                                  李東勝,IBM中國研究院

                                                                  題目:推薦算法的基礎理論與前沿技術

                                                                  12:00-14:00

                                                                  午餐

                                                                  14:00-16:30

                                                                  研究案例交流

                                                                  16:30-17:00

                                                                  結課儀式

                                                                  規模與費用

                                                                  ◇ 學員規模:計劃100人,根據報名情況擇優錄取,CCF會員優先;

                                                                  費用:本次暑期學校免注冊費,學員交通、食宿自理。

                                                                   活動報名

                                                                  報名鏈接:https://www.wjx.cn/jq/37865567.aspx,或掃描下方二維碼:

                                                                  1

                                                                  ◇ 聯系人:張鵬(復旦大學),[email protected], 18816511963.

                                                                  更多信息請訪問:

                                                                  http://cscw.fudan.edu.cn/summer-school/;

                                                                  http://www.scholat.com/team/tccc.

                                                                  講者介紹

                                                                  Haiyi Zhu

                                                                  1

                                                                  ◇ Haiyi Zhu is an assistant professor in the Computer Science and Engineering Department at the University of Minnesota, Twin Cities. Her research focuses on (1) integrating different research methods to produce clear descriptions and causal understandings of large Internet-based platforms, and (2) designing AI tools and services to support management activities on large Internet-based platforms. She holds a B.S in Computer Science from Tsinghua University and an M.S. and a Ph.D. in Human-Computer Interaction from Carnegie Mellon University. She has received an NSF CRII award as well as several paper awards in venues such as CHI, CSCW, and Human Factors, and an Allen Newell Award for Research Excellence. She has also taken on major service roles in the community, serving as the general co-chair of HCIC, program committee members for CHI and CSCW, and the acting editor for an HCI Journal Special Issue on unifying AI and HCI.

                                                                  ◇ Talk Title: From Discovery to Design: Creating AI Technologies to Support Massive-Scale Online Collaboration

                                                                  The development of Internet technologies creates virtual spaces where people all over the world can interact around a shared purpose. Internet-based platforms, such as Wikipedia, Facebook, Airbnb, and Uber, have transformed the way people connect, communicate, collaborate, work, and live. These platforms also enable collaboration and coordination at unprecedented scales. The English Wikipedia alone, as of July 2017, has over 5 million encyclopedia articles, 2.9 million active editors, 38,628 new editors registering on the site in an average month, and about 160,000 new edits every day. In my research, I conduct two types of research activities: 1) “Discovery” – I integrate different research methods, including machine learning, log data analysis, and controlled experiments, to produce a clear description and causal understanding of how activities are managed in large online platforms. 2) “Design” – I combine an in-depth empirical understanding with design methods to create innovative AI technologies to support massive-scale collaboration on these platforms and evaluate their effectiveness and impacts in the real world. In this talk, I will illustrate my approaches to research, discuss my shift from discovery to design, and discuss my on-going work and future directions.

                                                                  ◆  沈華偉

                                                                  2

                                                                  ◇  博士,中國科學院計算技術研究所研究員,中國中文信息學會社會媒體處理專委會副主任。主要研究方向:社交網絡分析、網絡數據挖掘。先后獲得過CCF優博、中科院優博、首屆UCAS-Springer優博、中科院院長特別獎、入選首屆中科院青年創新促進會、中科院計算所“學術百星”。2013年在美國東北大學進行學術訪問。2015年被評為中國科學院優秀青年促進會會員(中科院優青)。獲得國家科技進步二等獎、北京市科學技術二等獎、中國電子學會科學技術一等獎、中國中文信息學會錢偉長中文信息處理科學技術一等獎。出版個人專/譯著3部,在網絡社區發現、信息傳播預測、群體行為分析等方面取得了系列研究成果,在Science、PNAS等期刊和WWW、SIGIR、AAAI、IJCAI、CIKM、WSDM等會議上發表論文100余篇。擔任PNAS、IEEE TKDE、ACM TKDD等10余個學術期刊審稿人和KDD、WWW、SIGIR、AAAI、IJCAI、CIKM、WSDM等20余個學術會議的程序委員會委員。

                                                                  ◇  題目:社交媒體數據挖掘與信息傳播預測

                                                                  近年來,以微博、微信等為代表的社交媒體逐漸成為人們發布、傳播和獲取信息的主要媒介。社交媒體匯聚了大量的用戶關系數據和信息傳播數據,為分析和研究人類社會活動提供了彌足珍貴的數據資源。社交媒體中數據多源異構、個體間關系繁雜、信息傳播突發等特點給社交媒體分析提出了科學技術挑戰。分析社交網絡的結構規律、挖掘用戶行為的固有模式、探索網絡信息傳播的內在機理、研究高效的社交網絡分析與網絡信息傳播預測方法,有利于提升對在線社交媒體的科學認知水平和有效利用能力。報告將從網絡結構分析、網絡表示學習、影響力度量、網絡信息傳播預測等幾個方面介紹報告人近幾年在社交媒體數據挖掘與信息傳播預測方面的研究成果及其具體應用。

                                                                  ◆ Dakuo Wang

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                                                                  ◇  Dakuo Wang is a Research Scientist in IBM Research AI. He studies problems at the intersection of technology and humans in collaboration. He got his Ph.D. from the Informatics Department at the University of California Irvine in 2016, where he worked with Judy and Gary Olson on the collaborative writing topic. He is currently serving as the Conference Co-Chair of Social Media for CHI 2019. He previously earned an MS in Electrical Engineering and Computer Science at the University of California Irvine, a Dipl?me d'Ingénieur (MS) in Information System at école Centrale d'électronique Paris, and BS in Computer Science at the Beijing University of Technology. He has worked in the fields of engineering (France Telecom) and of user experience research and design, in France, China, and the U.S.

                                                                  ◇  Talk Title: Introduction to Computer Supported Cooperated Work (CSCW) and Human-AI-Collaboration

                                                                  Collaboration is an essential activity in all kinds of work, and it is not easy. That is why Human Computer Interaction (HCI) researchers from both academia (e.g., CMU, Stanford, UMichigan, and UCI) and industry (e.g., PARC, MSR, and IBM Research) have spent decades of efforts in designing computer systems to support it. Many of these yesterday’s research systems (e.g., Email, video-conferencing, and word-processors) have come out of laboratories and become today’s commercial products in the real world. Thus, researchers have shifted their research efforts to focus more on exploring users’ experiences in the wild, and that exhibits new opportunities as well as challenges. In this session, I will give a brief overview of the 40 years CSCW history and key topics. Besides studying today’s computer-supported collaborations in the wild, I am also interested in studying near future’s collaborations. Given the recent advance of Artificial Intelligent (AI) techniques, my colleagues and I at IBM Research started to investigate how to design AI-empower computer systems to support future collaborations. We envision that there is a possible future that humans and agents will collaborate with each other to accomplish tasks, such as doctors make decisions with AI help. In the second part of this talk, I will present some ongoing research work that we have done at IBM Research in designing and developing systems that aims to work together with humans in future’s collaborations.

                                                                  ◆ Bin Shao

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                                                                  ◇  Bin Shao is a lead researcher at Microsoft Research Asia. He joined Microsoft after receiving his Ph.D. degree from Fudan University in July 2010. His research interests include machine learning, in-memory databases, distributed systems, and parallel graph processing.

                                                                  ◇  Talk Title: Parallel Graph Processing and Knowledge Graph Serving

                                                                  Knowledge proliferates and becomes increasingly linked. Connected knowledge is naturally represented and stored as knowledge graphs, which are of more and more importance for many frontier research areas such as machine intelligence. Big graph serving at scale, however, faces challenges at all levels. In this talk, we discuss the challenges of big graph serving, propose several general principles of designing graph serving systems, and use large-scale knowledge graph serving to demonstrate the presented design principles.

                                                                  ◆ 李東勝

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                                                                  ◇  博士,IBM中國研究院高級研究員。主要研究方向為推薦算法的相關理論與技術,如推薦算法的準確性、泛化能力、可擴展性等。近年來,在信息推薦領域的知名國際會議和期刊,如ICML、NIPS、SIGIR、WWW、AAAI、IJCAI、SDM、IEEE TSNE等,發表論文30余篇,申請國際專利10余項。2016-2018年連續3年獲得IBM杰出技術成就獎(IBM Outstanding Achievement Award)。主持開發的認知推薦引擎為公司帶來過億美元的年銷售額,同時獲得了2018年IBM Corporate Award(IBM最高獎)。

                                                                  ◇  題目:推薦算法的基礎理論與前沿技術

                                                                  推薦系統技術已經發展了二十余年,目前廣泛應用于各類與人們日常生活息息相關的信息系統中,如電子商務、社交網絡、內容服務、生活服務等。推薦系統通過個性化的服務幫助用戶便捷的發現感興趣的信息,為信息系統帶來銷售額、參與度、滿意度等多個方面的提升,例如亞馬遜網站中推薦系統能夠帶來約30%的銷售額提升。本次講座首先基于推薦技術的發展歷史介紹推薦算法的基礎理論和方法,然后針對當前推薦領域的研究熱點分析當前推薦算法的前沿理論與技術,最后將介紹如何從系統設計層面去嘗試解決真實推薦系統所面臨的關鍵研究挑戰。


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