ADSA Virtual Meeting

Slide decks are attached to this session, for call-in attendees. Some slides have been removed because permission to share could not be obtained.
See also: Dharma Dailey's slides - https://doi.org/10.5281/zenodo.5669627

https://escience.washington.edu/data-for-good-workshop/

Creating a University-Based Data for Good Program: A Decision Points Workshop
We invite you and your colleagues to apply to attend a free online workshop on designing a university-based Data for Good program offered remotely over four, 2-hour sessions between January 14 and February 4, 2022. University-hosted Data for Good (D4G) programs provide research, service, and learning opportunities to students through team-based projects run outside of normal course offerings (typically during the summer). Popular with students, D4G programs provide the opportunity to work directly with data science mentors and subject matter experts from non-profits, government agencies, community organizations, and academia. Those interested in starting a program are invited to apply to attend the workshop. Discussions will draw on the collective experience of organizers running successful D4G programs in the US, Canada, and UK— experience shared recently in the white paper The Data for Good Growth Map. Applications close December 10th. Learn more here.

Abstract:
Making a positive social impact with data-intensive technologies requires partnerships across a range of stakeholders who can provide both technical expertise and nuanced understanding of the social issues and contexts in question. In recent years, a number of such partnerships have emerged through university-based initiatives. Data for Good summer programs are one approach that consist of fellowships for data science students to work on focused, collaborative projects with non-profit and government partners. Another approach has been to partner with research organizations working in (mostly) urban communities that have been collecting and evaluating societal data for many years.This session will have short 'roadmap' presentations on these approaches aimed at organizations interested in designing similar programs, followed by a panel session featuring leaders of various programs to draw out commonalities and challenges as well as potentially unique situations for each approach.

Chairs: Doug Hague (University of North Carolina, Charlotte) and Sarah Stone (University of Washington)

Speakers:
Dharma Dailey (University of Washington)
Amy Hawn Nelson (University of Pennsylvania)

Panelists:
Cass Dorius (Iowa State University)
Gizem Korkmaz (University of Virginia)
Rebecca Shearer (University of Miami)
Karthikeyan Umapathy (U of North Florida)
David Uminsky (University of Chicago)

Register, free, now:
https://us02web.zoom.us/meeting/register/tZApf-2rrDgjHd0ospyCLEL7r2KSG3oif4de

Join us! We will hear 5 min lightning talks from Montgomery College and Wake Technical Community College followed by moderated discussion and audience Q&A. The focus will be on approaches and challenges of implementing data science programs at 2 years colleges.

Montgomery College is a two-year college located outside of Washington, D.C., ranked Most Diverse TYC (in continental US) by Chronicle of Higher Ed, and has an enrollment of approximately 55,000 total students across three campuses. The first cohort to join the College’s Data Science Certificate program in 2017 included 30 students. To date, we have over 200 students who are either currently enrolled in or have taken data classes in the past.

Wake Tech is North Carolina’s largest community college, serving more than 70,000 adults annually, with six campuses, three training centers, multiple community sites, and a comprehensive array of online learning options. Wake Tech is accredited and offers more than 200 associate degrees, diplomas, and certificates that prepare students for university transfer or immediate employment. WTCC's Associate of Applied Science degree, in Data Science and Programming Support Services, prepares learners to design and develop desktop and web application with an emphasis on business logic and data‐driven applications.


The discussion will focus on the questions:

1. Who are the students we serve?
2. What are the academic and career goals of these students in terms of our data science courses?
3. How can we fill data science needs in our community?
4. What are the skills needed from data science faculty?

Register, free, here:
https://us02web.zoom.us/webinar/register/WN_Rrb9GIAZQpWLE3BsI8ZyVg

Abstract:
The development of AI models has often been siloed and associated with lack of representative data samples across different social classes and cultures. Conventional deep learning models often do not perform well when there is such undersampling of data. This can impact marginalized populations as the data curation is not representative or inclusive of all groups. In this panel, we discuss how curating new datasets and corpora representing the Hispanic population, can contribute to inclusive AI systems that mitigates biases towards underserved populations and move towards democratizing AI. The panel group will discuss this across different case studies in: long-term health tracking, disease classification in dementia and its subtypes, and population diversity in biological models, and cognitive behavioral changes from wearable sensors across diverse demographics.

Chair: Dhireesha Kudithipudi (UT San Antonio)

Panelists:
Mohamad Habes (UT Health, San Antonio)
Dhireesha Kudithipudi (UT San Antonio)
Paula Kay Shireman (UT Health, San Antonio)
Amina Qutub (UT San Antonio)

Register, free, here:
https://us02web.zoom.us/meeting/register/tZcrfu2vqDgtGNPqaWHxgNEL4MTOkX7o012Q

Join us! We will hear 5 min lightning talks from Florida A&M University/HBCU Consortium and Johnson C. Smith University, followed by moderated discussion and audience Q&A. The focus will be on approaches and challenges of implementing data science programs at Historically Black Colleges and Universities.

Register, free, here:
https://us02web.zoom.us/webinar/register/WN_i_sHlGdHTBuqlyHYlqvYrA

Abstract:
This panel will discuss the California Alliance for Data Science Education, a new collaborative effort that spans across 150 campuses of the California Community Colleges (CCCs), California State Universities (CSUs), and the University of California (UC) systems. It aims to democratize and streamline access to Data Science Education throughout California, building access to Data Science curriculum in traditionally underserved communities in STEM. The alliance does so by synchronizing data science efforts across campuses to build a shared open-source infrastructure, create community college transfer pipelines, and empower leaders at partner campuses.

In partnership with a non-profit computing organization 2i2c and CloudBank, the team has facilitated access to fully-maintained JupyterHubs for California Community Colleges that have launched Data Science Curriculum. The board of the alliance has been focused on standardizing procedures for community college Data Science class articulation to four-year colleges in California, working with key stakeholders including campus articulation officers, the UC Office of the President, and the CCC Chancellor’s Office. Lastly, the alliance has held multiple data science education workshops and regularly holds cross-campus discussions, elaborating on the specifics of building a successful undergraduate Data Science curriculum.

Chair:
Eric Van Dusen (UC Berkeley)

Panelists:
Aaron Fraenkel (UC San Diego)
Judith Canner (CSU Monterey Bay)
Solomon Russell (El Camino College)
Kseniya Usovich (UC Berkeley)

Register, free, here:
https://us02web.zoom.us/meeting/register/tZ0tfumurjgiE9f97iXYa3MJvYtIhIBZEzXi

Join us! We will hear 5 min lightning talks from Jackson State University and Fisk University, followed by moderated discussion and audience Q&A. The focus will be on approaches and challenges of implementing data science programs at Historically Black Colleges and Universities.

Register, free, here:
https://us02web.zoom.us/meeting/register/tZ0lcO-qqzwrHtLWrxAkv04mHtoHD4w-93ft

Join us! We will hear 5 min lightning talks from University of Michigan and University of Washington, followed by moderated discussion and audience Q&A. The focus will be on approaches and challenges of implementing data science programs.

Register, free, here:
https://us02web.zoom.us/meeting/register/tZcvde6gpzIuE9AX_HY59eXewkfA-jTqlxa-

Join us! We will hear 5 min lightning talks from New Jersey Institute of Technology and University of Arizona, followed by moderated discussion and audience Q&A. The focus will be on approaches and challenges of implementing data science programs.

Register, free, here:
https://us02web.zoom.us/webinar/register/WN_SABMML7rRiimunQeRQtAMA

Abstract: Data science is increasingly important in producing knowledge and technologies that impact us daily. How we analyze data, design analysis tools, deploy algorithmic systems, and publish data-driven conclusions matters to real lives. When data science ignores variations and disparities in the human experience, it can bias decision-making and outcomes that negatively affect people with disabilities.

In this hands-on workshop, we adopt an equity lens to ask how data scientists can confront ableism, which is systemic bias that privileges people with “typical” abilities. Our breakout teams will be guided through the following activities: (1) explore data science as a practice that includes planning, building, and deploying; (2) identify patterns of ability-based bias in data science; (3) analyze particular cases of ableism in data science; and (4) develop tools that can guide data science practitioners in avoiding the most common ableist pitfalls.

Chair: Anat Caspi (University of Washington)

Register, free, here:
https://us02web.zoom.us/meeting/register/tZAkde2gqzIiEtEkqKRR2bB4lqXlPCaMr5fZ

Join us! We will hear 5 min lightning talks from Boston University and University of Chicago, followed by moderated discussion and audience Q&A. The focus will be on approaches and challenges of implementing data science programs.

Register, free, here:
https://us02web.zoom.us/meeting/register/tZAocuGpqzgqGNBbXQxiJUy0GU5v6MYsXWOs

Join us! We will hear 5 min lightning talks from Bowie State University and Winston-Salem State University followed by moderated discussion and audience Q&A. The focus will be on approaches and challenges of implementing data science programs at Historically Black Colleges and Universities.

Read more about the data science programs at these two schools HERE and in the attached pdfs.
https://academicdatascience.org/leaders/dsinstitutionupdates

Register, free, here:
https://us02web.zoom.us/webinar/register/WN_y59dWOOYTW-moYfF9HheAw

Abstract:
Data collected from underrepresented communities is of utmost interest for a variety of purposes, from drug therapeutics to algorithm development. Yet, there are still no detailed regulations and guidelines around data governance and reciprocity, neither on a global scale nor in the United States. While many researchers advocate for the increased collectivization of information from public and open databases for the “greater good of research and innovation”, historically oppressed communities are concerned that such data extraction may continue existing colonial exploitative models that do not translate to direct benefit for those people from whom the data is derived.

We argue that it is imperative to create new models for data equities to encourage benefit-sharing. In recent years, new models of engagement have been developed that ensure research participants benefit from the development of new therapeutics (by receiving free or low-cost access to developed therapies); receive a share of any financial value created; and can take greater control in the data governance and analysis process through scientific capacity building. In this interactive session, we will hear perspectives on data justice from three scholars (one moderator and two speakers) from Indigenous and Black communities and two representatives (both speakers) in the industry and government sectors to learn how all areas of research and development can ethically promote data equity and empowerment.

Session Chair: Krystal Tsosie (Native BioData Consortium, Arizona State University)

Speakers:
Keolu Fox (University of California, San Diego) - Moderator
Latifa Jackson (Howard University)
Kaja Wasik (Variant Bio)
Ann McCartney (National Institutes of Health, National Human Genome Research Institute, Bioinformatics)
Krystal Tsosie (Native BioData Consortium, Arizona State University)

Register, free, here:
https://us02web.zoom.us/meeting/register/tZYlde2srDItGtZuCWrZEbHDis-SF-4nH_KV

Join us! We will hear 5 min lightning talks from University of Texas, San Antonio and University of Virginia, followed by moderated discussion and audience Q&A. The focus will be on approaches and challenges of implementing data science programs.

Register, free, here:
https://us02web.zoom.us/webinar/register/WN_8HHG6guhTrertotaRmr5cw

Abstract:
As of April 21, 2021, the American Statistical Association is now a full member of CSAB (https://csab.org/), joining the world’s two largest professional and technical societies for computing, the Association for Computing Machinery (ACM) and the IEEE Computer Society (IEEE-CS). As the lead ABET member society for computing, CSAB is responsible for developing accreditation criteria for a number of programs including Data Science. Programs in data science and data analytics, which have previously not fallen under ABET’s accreditation umbrella, will soon be eligible for ABET accreditation. There are two distinct ABET groups working on data science program accreditation criteria - the Applied and Natural Sciences Accreditation Commission (ANSAC) and Computing Accreditation Commission (CAC). This session will provide an update on the process and will discuss the drafting of the criteria. The session will be interactive by design to provide the participants an opportunity to share their insights and ask questions.

Chairs: Donna LaLonde (American Statistical Association), Dave Hunter (Penn State and ASA), and Kathy Ensor (Rice University and ASA)

Register, free, here:
https://us02web.zoom.us/meeting/register/tZYvc-iqrj0iHN1IVrDBiqvv46WUF2lTCaXA

Join us! We will hear 5 min lightning talks from University of Wisconsin-Madison and University of Oklahoma, followed by moderated discussion and audience Q&A. The focus will be on approaches and challenges of implementing data science programs.