cscw与社会计算暑期学校-尊龙凯时官方app下载

cscw与社会计算暑期学校

cscw & social computing summer school

2019年8月2-4日上海·复旦大学邯郸校区光华楼（hgx104）

主办：中国计算机学会

承办：中国计算机学会协同计算专业委员会、复旦大学

计算机支持的协同工作（computer supported cooperative work, cscw）迄今已经历了三十余年的发展，其旨在探索如何在网络环境下利用计算机有效地支持社会群体的通信、合作和协商，协同完成社会任务。这种社会群体、网络、计算设备、信息技术交织在一起的工作模式引发了对于社会与技术、社会科学与计算科学之间相互关系的关注和探索，这也就是“社会计算”（social computing）的雏形。社会计算的概念于1994年被首次明确提出，强调计算科学与社会科学的融合，探索如何结合活动理论、扎根理论、人种学、常人方法学、统计推断等社会科学的理论与方法，以及社会大数据挖掘、统计与机器学习、深度学习、自然语言处理等计算科学的方法与技术分析、理解和解决信息社会中所呈现的社会问题，是计算机科学、管理科学、社会学、心理学、传播学等交叉融合的前沿研究领域。

当前，大量的社会计算系统和平台持续涌现，包括facebook、twitter、微信等新型社会媒体，wikipedia、github、stack overflow等ugc（user generated content）社区，众包平台和推荐系统等，并在教育、医疗、金融、交通等领域得到广泛应用。围绕这些新兴社会计算场景中的社会与计算交互问题，cscw与社会计算领域的研究者结合定性与定量方法从不同侧面开展了一系列研究工作，涉及分布式协同模型、算法与系统，社会媒体与社交网络大数据建模、分析与预测，社会计算环境下的个性化算法如推荐算法和社会匹配系统，面向典型社会场景数据的知识抽取与自然语言理解，人工智能在社会计算空间中的角色和效用，区块链技术的社会效用，大规模开放协作与众包，社交游戏、科学系统中的机制设计，社会计算数据和系统的安全和隐私机制，社会化应用的架构设计与实现等。

本次暑期学校旨在为正在或即将从事cscw与社会计算研究的研究生和本科生提供向该领域优秀学者系统学习相关研究方法和了解前沿研究动态的机会，内容涉及支持大规模在线协作的ai技术、人与ai协同交互系统的构建、社交媒体数据挖掘、个性化推荐技术和社会计算中的知识图谱构建与知识管理，以及研究案例交流。

日程安排

规模与费用

学员规模：计划100人，根据报名情况择优录取，ccf会员优先；

费用：本次暑期学校免注册费，学员交通、食宿自理。

活动报名

报名链接：，或扫描下方二维码：

报名截止时间：2019年7月26日；

联系人：张鹏（复旦大学），, 18816511963.

更多信息请访问：

;

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

讲者介绍

haiyi zhu

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.

沈华伟

博士，中国科学院计算技术研究所研究员，中国中文信息学会社会媒体处理专委会副主任。主要研究方向：社交网络分析、网络数据挖掘。先后获得过ccf优博、中科院优博、首届ucas-springer优博、中科院院长特别奖、入选首届中科院青年创新促进会、中科院计算所“学术百星”。2013年在美国东北大学进行学术访问。2015年被评为中国科学院优秀青年促进会会员（中科院优青）。获得国家科技进步二等奖、北京市科学技术二等奖、中国电子学会科学技术一等奖、中国中文信息学会钱伟长中文信息处理科学技术一等奖。出版个人专/译著3部，在网络社区发现、信息传播预测、群体行为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等会议上发表论文100余篇。担任pnas、ieee tkde、acm tkdd等10余个学术期刊审稿人和kdd、www、sigir、aaai、ijcai、cikm、wsdm等20余个学术会议的程序委员会委员。

题目：社交媒体数据挖掘与信息传播预测

近年来，以微博、微信等为代表的社交媒体逐渐成为人们发布、传播和获取信息的主要媒介。社交媒体汇聚了大量的用户关系数据和信息传播数据，为分析和研究人类社会活动提供了弥足珍贵的数据资源。社交媒体中数据多源异构、个体间关系繁杂、信息传播突发等特点给社交媒体分析提出了科学技术挑战。分析社交网络的结构规律、挖掘用户行为的固有模式、探索网络信息传播的内在机理、研究高效的社交网络分析与网络信息传播预测方法，有利于提升对在线社交媒体的科学认知水平和有效利用能力。报告将从网络结构分析、网络表示学习、影响力度量、网络信息传播预测等几个方面介绍报告人近几年在社交媒体数据挖掘与信息传播预测方面的研究成果及其具体应用。

dakuo wang

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 diplômed'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

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 important 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.

李东胜

博士，ibm中国研究院高级研究员。主要研究方向为推荐算法的相关理论与技术，如推荐算法的准确性、泛化能力、可扩展性等。近年来，在信息推荐领域的知名国际会议和期刊，如icml、nips、sigir、www、aaai、ijcai、sdm、ieee tsne等，发表论文30余篇，申请国际专利10余项。2016-2018年连续3年获得ibm杰出技术成就奖(ibm outstanding achievement award)。主持开发的认知推荐引擎为公司带来过亿美元的年销售额，同时获得了2018年ibm corporate award（ibm最高奖）。

题目：推荐算法的基础理论与前沿技术

推荐系统技术已经发展了二十余年，目前广泛应用于各类与人们日常生活息息相关的信息系统中，如电子商务、社交网络、内容服务、生活服务等。推荐系统通过个性化的服务帮助用户便捷的发现感兴趣的信息，为信息系统带来销售额、参与度、满意度等多个方面的提升，例如亚马逊网站中推荐系统能够带来约30%的销售额提升。本次讲座首先基于推荐技术的发展历史介绍推荐算法的基础理论和方法，然后针对当前推荐领域的研究热点分析当前推荐算法的前沿理论与技术，最后将介绍如何从系统设计层面去尝试解决真实推荐系统所面临的关键研究挑战。