Dr. Fran Berman
Director of Public Interest Technology, Research Professor, University of Massachusetts, Amherst
Dr. Berman’s work focuses on the social and environmental impacts of information technology, and in particular of the Internet of Things (IoT) — a deeply interconnected ecosystem of billions of devices and systems that are transforming commerce, science and society. Dr. Berman explores the overarching ecosystem needed to guide the development of information technologies that maximize benefits, minimize risks, and promote individual protections, the public interest, and planetary responsibility.
Dr. Rebecca Fiebrink
Reader, Creative Computing Institute, University of the Arts London
Dr. Fiebrink’s research focuses on human-computer interaction, machine learning, and signal processing all to allow people to apply machine learning to new areas such as designing new musical instruments or gestural interfaces for accessibility. She is also involved in digital humanities scholarship and machine learning education.
Head of Data Science, OpenLabs R&D
We are pleased to invite David Shor back to a larger stage following the overwhelming response to his MIDAS Seminar Series appearance preceding the 2020 election. David is the Head of Data Science at OpenLabs a non-profit research lab using data science to provide products for progressive organizations throughout the country. He previously spent seven years working at Civis as their director of Political Data Science. Prior to helping found Civis, he worked on the Obama campaign to develop their election forecasting engine the “Golden Report”.
Organizer and Instructor:
Fred Feng, Assistant Professor, Industrial and Manufacturing Systems Engineering
Organizers and Instructors:
Meghan Dailey, Machine Learning Specialist, Advanced Research Computing
Jule Krüger, Program Manager for Big Data and Data Science, Center for Political Studies, and Advanced Research Computing
Target audience: Anyone who is interested in the topic. A basic familiarity with Python or R is expected for the second half of the workshop.
In this workshop, we will analyze a text corpus to demonstrate the use of machine learning for natural language processing. In the first half of the workshop, we will provide a basic overview of machine learning, introduce the main concepts and logic of using text as data, and walk through a typical workflow for processing, managing and analyzing a text corpus. We will discuss how to choose between Python and R for text analysis and how to interpret the results from a topic model. In the second half of the workshop, instructors will demonstrate in two concurrent hands-on tutorials how the topic modelling example from the first half was accomplished in either Python or R. Participants who attend the first part of the workshop will walk away with a basic overview of the capabilities and methods for using text as data. Participants who attend the entire workshop will be equipped with basic programming tools to apply natural language processing in their own research. The workshop will also cover helpful resources for machine learning implementations, such as data sets, storage space, high performance computing, and consultation services at the University of Michigan.
Presenters and panelists:
Lia Corrales, Assistant Professor, Astronomy; Founder of Women of Color Coders
Tayo Fabusuyi, Assistant Research Scientist, U-M Transportation Research Institute; Leading the project “Towards a more representative Public Interest Technology (PIT) field”
Libby Hemphill, Associate Professor, School of Information; Leading “Data Feminism” faculty group
H.V. Jagadish, Director, MIDAS; Professor, Computer Science and Engineering; Leading the project “Framework for Integrative Data Equity Systems”
Target audience: people who are involved or are interested in promoting equity and diversity both from the technical perspective and from the community perspective.
Through this session, they will learn about similar activities on campus, share their ideas and activities, and get to know like-minded colleagues for collaboration. In the first part of the session, the panelists will present their work addressing different aspects of diversity and equity in data science. This will include increasing diversity and inclusion in the data science field, as exemplified by Women of Color Code and Public Interest Technology; it will also include technical solution efforts to make the data we deal with, and the decisions made with these data, more equitable and inclusive, as exemplified by Data Feminism and Data Equity Systems. The second half will consist of a community forum, where participants will share their work and their thoughts on these topics, with conversation moderated by the workshop panelists.
Jing Liu, Managing Director, MIDAS
Presenters and panelists:
George Alter, Research Professor Emeritus, Institute for Social Research
Johann Gagnon Bartsch, Assistant Professor, Statistics
Thomas Valley, Assistant Professor, Pulmonary and Critical Care Medicine
Target audience: Researchers who would like to learn about best practices in code review and sharing, and reproducible workflows. It is also for researchers who are interested in participating in the MIDAS Reproducibility Challenge.
The 2020 MIDAS Reproducibility Challenge highlighted important conceptual issues of reproducible data science in multiple dimensions and the creative practical approaches U-M researchers have used to address these challenges. Two winners of the Challenge will present their practical approaches in the first half of the workshop. Building on the 2020 Challenge, the 2021 Challenge focuses on actionable solutions that can be shared with other researchers to improve reproducibility. In the second half of the workshop, a panel of the 2021 Reproducibility Challenge planning committee members will discuss with the audience the conceptual issues and practical solutions for making data science research transparent, traceable, and trustworthy, and answer questions for researchers who are interested in participating in the 2021 Reproducibility Challenge.
More workshops will be added soon
Call for Research Presentation and Mini-Workshop Proposals
The Michigan Institute for Data Science (MIDAS) invites submission of 1) abstracts for presentations and 2) proposals for workshops, for the 2021 U-M Data Science and AI Symposium that will take place on Nov. 15-16.
As the focal point of data science and AI at U-M, MIDAS facilitates the work of the broad U-M data science and AI community, advances cross-cutting methodologies and applications, promotes the use of data science and AI to benefit society, and develops partnerships with industry, academia and community. The annual symposium showcases the breadth and depth of U-M data science and AI, shares research ideas that will lead to the next breakthroughs, and builds collaboration.
Presentations at the symposium should cover one or more of the following areas of data science and AI:
- Theoretical foundations
- Methodology and tools
- Real-world application in any research domain
- The ethics and societal impact of data science and AI
- Creative and artistic applications of data science and AI
- Emerging areas of data science and AI
We invite submissions for the following:
- Abstracts for Research Talks (20 minutes including Q&A). The talks should discuss exciting research ideas, provide vision and context for challenging data science questions, stimulate discussions, and lay out collaboration opportunities. These talks should not simply be technical reports of projects. Independent researchers are especially encouraged to submit abstracts for talks.
- Abstracts for Posters. The Posters can be used as technical reports of projects. Posters with students as first authors will be automatically entered in the poster competition.
- Proposals for mini-workshops. The symposium will include 3-6 mini-workshops (parallel sessions). Proposal submission closed.
Mini-workshop proposal submission: deadline passed; notification: Aug. 20, 2021
Talks and posters abstract submission: 11:59 pm, Sept. 24, 2021; notification: Oct. 15, 2021
- At least one author/presenter should have a U-M affiliation.
- Please do not include figures, tables or bibliography in the abstract.
- To submit abstracts for research talks and posters:
- Please include a title, list of authors/presenters and their affiliations.
- The main body of the submission should be no more than 300 words.
- For research talks, please include a brief summary of the research idea and its context, methods and impact, and how it can benefit from collaboration.
- For posters, please include a brief summary of the research, methods, main results, and impact.
For questions, please contact email@example.com.