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2021 Keynote Speakers

Contents

Cuijuan (Jada) Xia (夏翠娟)

Presentation: Building a Data Infrastructure to Enrich the Multiple Sources of Evidence for Humanities Studies: From the Perspective of Cultural Memory

Time: Plenary #1, Wednesday, March 17, 6:00 - 8:00 pm China Standard Time (UTC +8); this corresponds to 6:00 - 8:00 am U.S. Eastern Daylight Time, and 11:00 - 13:00 Central European Time

Biography: Cuijuan (Jada) Xia is Researcher of Shanghai Library, team leader of Shanghai Library's Digital Humanities (DH) projects, senior DH Platform architect, and Knowledge Organization System (KOS)) designer. She has taken a primary role in developing and designing DH projects for Shanghai Library. She hosts and participates in many national research projects, including work focused on Metadata, Ontology, Knowledge Organization, Linked Data, Digital Humanities, and Cultural Memory. She has published three books and more than 50 papers in the Journal of Library Science in China, Journal of Academic Libraries, Library Tribune, Library Journal, Library and Information Science and other academic journals. Visit her Shanghai Library biographical webpage for more.

From the speaker (click here for Chinese version)The theory of "Social Memory", which originated from "collective memory", systematically separates memory from history. It not only has a great influence on sociology, anthropology, culture and ethnology, but also provides a new perspective for historical research. On the basis of "double sources of evidence method", "triple sources of evidence method" and "quadruple evidence method", it develops "multiple  sources of evidence method" ”. In addition to all kinds of historical books and records, it attaches great importance to folk literature and field investigation, as well as cultural memory resources such as memorials, local chronicles, family genealogies, oral history, private archives, private notes, poems and odes, painting art, gold and stone porcelain, legends and ballads, operas and dramas, festival ceremonies, site relics, and even natural science research achievements such as climate disasters, tree rings and glaciers, All of them can be the sources of evidence and mutual confirmation. Most of these cultural memory resources are stored in libraries, archives, museums, art galleries (GLAM) and other cultural memory institutions.

In the era of Data & Intelligence (big data and machine intelligence), the separation of content and carrier has narrowed the gap between GLAM due to different resource carriers. Data, facts and knowledge have become the smallest unit of "cultural memory". Not only GLAM's cultural memory resources should become parts of the multiple sources of evidence reference system, but also the knowledge graph of people, places, times, events, things and other entities, as well as the quantitative analysis data and visual charts generated from large-scale, long-term, multi-dimensional and fine-grained data also constitute another important source of evidence that can not be ignored and is becoming more and more important in the multiple sources of evidence reference system.

Through the long-term preservation of cultural memory resources, carrier management, knowledge organization, inheritance and dissemination for culture, GLAMs have become the infrastructure of cultural memory and is participating in the work of "building future cultural heritage". In the era of Data & Intelligence, cultural memory institutions have undergone digital transformation, and the construction of "data infrastructure" with "data" as the basic unit has become an important work to support the humanities research and inherit cultural memory. The original definition of "data" is "information that can be transmitted and stored by computer". For humanities research, "data" can be understood as information units that can be processed by machine, such as literature or physical resources, objects, concepts, people, institutions, groups or their structured descriptive information (including variables, values, text symbols or facts, etc.). In addition to the openness, publicity and sustainability of the "infrastructure", the "data infrastructure" should also fully reflect the characteristics of large-scale data, long time coverage, wide geographical scope, fine-grained and muti-dimensional, so as to support the data request, fusion, automatic analysis, statistics and data visualization in a web scale. At the same time, it should be independent of specific application development and specific field research, follow the general data standards and open sharing specification, and become a "data center" between the "back-end" of information infrastructure and the "front-end" of specific field research.

Through theoretical construction and practical exploration, this presentation attempts to elaborate how cultural memory institutions enrich the multiple sources of evidence reference system for humanistic research in the Data & Intelligence age through the construction of data infrastructure.

Zvi Galil (צבי גליל)

Presentation: Georgia Tech’s online Master in Computer Science Program and the future of online learning

Time: Plenary #2, Monday, March 22, 12:00 - 1:30 pm Eastern Daylight Time (UTC -4); this corresponds to 17:00 - 18:30 Central European Time, and 12:00 - 1:30 am China Standard Time on Tuesday, March 23

Biography: Israeli-American computer scientist and mathematician Zvi Galil earned BS and MS degrees in Applied Mathematics from Tel Aviv University, both summa cum laude. He then obtained a PhD in Computer Science from Cornell University. Galil served as the President of Tel Aviv University from 2007 through 2009. From 2010 to 2019, he was the dean of the Georgia Institute of Technology College of Computing (iSchool)Galil's research areas have been the design and analysis of algorithms, complexity, cryptography and experimental design. In 1983-1987 he served as chairman of ACM SIGACT, the Special Interest Group of Algorithms and Computation Theory. He has written over 200 scientific papers, edited 5 books, and has given more than 150 lectures in 20 countries. Galil has served as editor in chief of two journals and as the chief computer science adviser in the United States to the Oxford University Press. He is a fellow of the ACM and the American Academy of Arts and Sciences and a member of the National Academy of Engineering. In 2009 the Columbia Society of Graduates awarded him the Great Teacher Award.

From the speaker: In March 2020, universities around the world were suddenly forced to move some or all of their teaching online. But Georgia Tech had begun the process six years earlier. In January 2014, Georgia Tech started the first MOOC-based Online Master in Computer Science program (OMSCS). OMSCS started with 380 students, but this spring the program enrolled 11,300 students — and it is still growing.

This talk will tell the story of OMSCS: how it started, what we have learned and are still learning from it and the role it and its successors have played before and during the pandemic. It will also share some thoughts on the role online programs can play in the future of higher education.

Catherine D’Ignazio and Lauren F. Klein.

Presentation: Data Feminism

Time: Plenary #3, Tuesday, March 23, 12:00 - 1:30 pm Eastern Daylight Time (UTC -4); this corresponds to 17:00 - 18:30 Central European Time, and 12:00 - 1:30 am China Standard Time on Wednesday, March 24

Biographies: 

Catherine D’Ignazio is an Assistant Professor of Urban Science and Planning in the Department of Urban Studies and Planning at MIT. She is also Director of the Data + Feminism Lab which uses data and computational methods to work towards gender and racial equity. D’Ignazio is a scholar, artist/designer and hacker mama who focuses on feminist technology, data literacy and civic engagement. She has run reproductive justice hackathons, designed global news recommendation systems, created talking and tweeting water quality sculptures, and led walking data visualizations to envision the future of sea level rise. With Rahul Bhargava, she built the platform Databasic.io, a suite of tools and activities to introduce newcomers to data science. Her 2020 book from MIT Press, Data Feminism, co-authored with Lauren Klein, charts a course for more ethical and empowering data science practices. Her research at the intersection of technology, design & social justice has been published in the Journal of Peer Production, the Journal of Community Informatics, and the proceedings of Human Factors in Computing Systems (ACM SIGCHI). Her art and design projects have won awards from the Tanne Foundation, Turbulence.org and the Knight Foundation and exhibited at the Venice Biennial and the ICA Boston.

Lauren F. Klein is an associate professor in the departments of English and Quantitative Theory & Methods at Emory University, where she also directs the Digital Humanities Lab. Before moving to Emory, she taught in the School of Literature, Media, and Communication at Georgia Tech. Klein works at the intersection of digital humanities, data science, and early American literature, with a research focus on issues of gender and race. She has designed platforms for exploring the contents of historical newspapersmodeled the invisible labor of women abolitionists, and recreated forgotten visualization schemes with fabric and addressable LEDs. In 2017, she was named one of the “rising stars in digital humanities” by Inside Higher Ed. She is the author of An Archive of Taste: Race and Eating in the Early United States (University of Minnesota Press, 2020) and, with Catherine D’Ignazio, Data Feminism (MIT Press, 2020). With Matthew K. Gold, she edits Debates in the Digital Humanities, a hybrid print-digital publication stream that explores debates in the field as they emerge. Her current project, Data by Design: An Interactive History of Data Visualization, 1786-1900, was recently funded by an NEH-Mellon Fellowship for Digital Publication.

From the speakers: As data are increasingly mobilized in the service of governments and corporations, their unequal conditions of production, their asymmetrical methods of application, and their unequal effects on both individuals and groups have become increasingly difficult for data scientistsand others who rely on data in their workto ignore. But it is precisely this power that makes it worth asking: "Data science by whom? Data science for whom? Data science with whose interests in mind? These are some of the questions that emerge from what we call data feminism, a way of thinking about data science and its communication that is informed by the past several decades of intersectional feminist activism and critical thought. Illustrating data feminism in action, this talk will show how challenges to the male/female binary can help to challenge other hierarchical (and empirically wrong) classification systems; it will explain how an understanding of emotion can expand our ideas about effective data visualization; how the concept of invisible labor can expose the significant human efforts required by our automated systems; and why the data never, ever “speak for themselves.” The goal of this talk, as with the project of data feminism, is to model how scholarship can be transformed into action: how feminist thinking can be operationalized in order to imagine more ethical and equitable data practices.

Margaret Hedstrom

Presentation: Collaboration Around Curation

Time: Plenary #4, Thursday, March 25, 9:00 - 10:30 am Eastern Daylight Time (UTC -4); this corresponds to 14:00 - 15:30 Central European Time, and 9:00 - 10:30 pm China Standard Time  

Margaret Hedstrom is the Robert M. Warner Collegiate Professor of Information at the University of Michigan where she teaches in the areas of archives, collective memory, and digital curation. She was the Interim Director of the UM Graduate Certificate Program in Museum Studies, 2018-2020. She was PI for two large NSF-sponsored projects: SEAD (Sustainable Environments – Actionable Data) and an IGERT traineeship called “Open Data” that investigated tools and policies for data sharing and data management across multiple disciplines. She was a member of the Board for Research Data and Information, National Academy of Sciences and chaired the National Research Council study committee on Data Curation Workforce and Education Issues. She has served on numerous national and international boards, including the National Digital Strategy Advisory Board to the Library of Congress, the Advisory Committee on Historical Diplomatic Documentation, U.S. Department of State, the ACLS Commission on Cyber-Infrastructure for the Humanities and Social Sciences and the International Scientific Advisory Board to the CATCH program, NWO, the Netherlands. Hedstrom is a fellow of the Society of American Archivists and recipient of a Distinguished Scholarly Achievement Award from the University of Michigan for her work with archives and cultural heritage preservation in South Africa.

From the speaker: The term curation has become overused and abused, so much so that its ubiquity has made the word “curation” meaningless. Curation has a long history of practice in archives, museums and libraries. In the last decade, curation has emerged as a challenge in many other areas under the iSchool umbrella, such as data science, web analytics, and moderation on social media platforms. This address will identify commonalities and differences in the conceptualizations and practices of curation across the fields of research and teaching in iSchools. It will identify areas where research that cuts across these field could be mutually beneficial. 



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Accepted papers will be published in Springer’s Lecture Notes in Computer Science


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