2023 Data Science Leadership Summit
AFUA BRUCE is a leading public interest technologist who has worked at the intersection of technology, policy, and society.Afua is the Principal of the ANB Advisory Group LLC, a consulting firm that supports organizations developing, implementing, or funding responsible data and technology. With her background in software engineering, data science, and artificial intelligence, combined with experience developing and deploying technology in and with communities, Afua incorporates an equity-based framework into her engagements.
Afua is an adjunct professor at Carnegie Mellon University and an affiliate at Harvard Kennedy School’s Berkman Klein Center. Afua’s newest book, "The Tech That Comes Next: How Changemakers, Technologists, and Philanthropists can Build an Equitable World," describes how technology can advance equity.
Monday May 8, 9am
A bit of Unconference
On Monday during the breaks, attendees can sign up to give a 3-5min lightning talk on the topic of their choosing. Slides are optional but we will have AV to plug into. If we end up with a lot of talks, we may break into 2 parallel tracks. We have about 50 min before lunch on Tuesday and Wednesday set aside for the talks. The talks should be on topics that engage the other attendees, for example: highlight or pitch an opportunity for collaboration across schools, a thought exercise on a hot topic in data science, or other discussion-provoking content. The talks should not be a description of your data science programs, or what your school is doing in data science.
MONDAY, MAY 8
Data Science Organizational Structures
(Institutes vs Departments and Schools)
Chairs: Azer Bestavros (Boston University) and Hridesh Rajan (Iowa State University of Science and Technology)
The unrelenting demand for educational and research capacities in data science required universities to adopt various strategies that enable them to create and offer new degree programs, recruit faculty members to support these programs, and provide the infrastructure necessary to sustain data-intensive research. Over the last decade, these strategies led to the emergence of a number of organizational models and structures for embedding data science into the fabric of the academy. In this session, we will characterize the resulting landscape of organizational structures, which range from research and training initiatives to fully-fledged schools and colleges; we will examine their tradeoffs, the influence of existing organizational structures on their development, and their ability to support the interdisciplinary nature of data science as a field. Additionally, we will explore the implications of these structures on managing growth and sustainability. To achieve this, we will hear from a panel of experienced leaders who will provide insights and perspectives on these topics. This will be followed by a breakout session, where attendees can dig deeper and exchange ideas about specific structures of interest. Through the panel discussion and breakout session, we hope to provide attendees with a comprehensive understanding of the landscape of organizational structures for data science and the various factors that institutions need to consider when choosing one that best suits their needs.
University of Wisconsin - Milwaukee
Professor of Marketing and Co-Director of the Northwestern Mutual Data Science Institute
Director of the Data Intensive Studies Center and Stern Family Professor, Data Intensive Study Center
University of Washington
Executive Director of the eScience Institute
Iowa State University
Professor of Computer Science and Director of the Computational Media Lab
University of Virginia
Assistant Professor and Director of Undergraduate Programs, School of Data Science
Challenges in Faculty Hiring
Chair: Jeff Hamrick (University of San Francisco) and Kristin Eschenfelder (University of Wisconsin-Madison)
Hiring Data Science faculty into academic positions faces a number of challenges including: competition with industry, decision whether and how to hire adjunct faculty, coordinating across departments for expertise, and the explosion of online learning (formal and informal). All of these issues can be further compounded when one is hiring into a new department, when the hiring institution hasn't thought carefully about classification and career paths for data scientists, or when tenure and promotion requirements aren't aligned with the types of work data scientists conduct. In this session, we will touch on these challenges, and discuss the types of solutions and workarounds the panelists and others have implemented.
Executive Director of the Institute of Experiential Artificial Intelligence
University of Chicago
Liew Family Chairman of Computer Science; Senior Advisor to the Provost for Computing and Data Science; Faculty Co-Director of the Data Science Institute
University of Maryland, Baltimore County
Professor and Chair of the Information Systems Department,
Director NSF HDR Institute-iHARP
University of Toronto
Professor, Department of Computer Science; Senior Scientist at The Hospital for Sick Children
TUESDAY, MAY 9
Developing an Effective Data Science Work Force
Chairs: Bill Southerland (Howard University) and Amy Yeboah Quarkume (Howard University)
Data science is inherently a broad umbrella discipline that has captured the interest of both corporate and non-profit sectors. It should also be noted that the expectations of specific employers for their data science workforce are not only varied but constantly evolving. To meet these dynamic expectations, academic institutions have the challenge of producing trainees with the requisite complement of experiences and didactics that promote both flexibility and adaptability in the data science workspace. This session recognizes that effective data science training will maximally occur when there is collaboration between the academic community and those communities that employ a data science workforce. We will hear from representatives from the academic, corporate, and non-profit sectors in a panel format that encourages direct dialog between these communities, whether on the panel or among attendees. Each panelist will provide a summary overview of his/her data science workforce expectations followed by direct dialog/questions among panelists and with summit attendees. We expect that this type of direct dialog among panel experts and between the panel and attendees will serve as a catalyst to stimulate collaboration among sectors to produce a data science workforce that is perpetually effective in meeting workforce needs.
Principal (Vice President)
Accenture Federal Services
Director of the Applied Intelligence Discovery Lab
Executive Director of the South Big Data Hub
US Census Bureau
Research Mathematical Statistician
Amy Yeboah Quarkume
Associate Professor of African Studies; Director of Master’s Program in Applied Data Science & Analytics
Ramps & Pathways to Data Science:
K12, Community Colleges, and Minority-serving Institutions
Chairs: Rachel Saidi (Montgomery College) and Debzani Deb (Winston-Salem State University)
As the global demand for data scientists and analysts is projected to continue to grow, increasing the number and diversity of students enrolled in data science and data analytics courses and programs will help to increase the communities who can approach complex, multifaceted problems and decision-making. In this session, we will cover the variety of pathways to data science, including upskilling from a different job, small colleges and MSIs, undecided majors, community colleges, and certificate programs. We will consider the roadblocks to data science, including awareness of the field and opportunities for careers, the stigma of alternative pathways, and the challenges of creating articulation agreements with four year institutions. We will hear from representatives from various organizations and institutions who are working to build these nontraditional pathways to broaden our audience’s understanding of the challenges and opportunities these pathways present and learn how data science can be promoted and supported. At the end of this session, we hope to develop a report that may be used by schools and other organizations who are considering creating data science programs.
Associate Professor, Statistical & Data Sciences
Florida A&M University
Associate Professor in the School of Business & Industry; Director, Interdisciplinary Center for Creativity and Innovation (ICCI); Campus Director, Blackstone LaunchPad
Instructor, Department of Mathematics
Director, Curriculum Research Group
Data Science 4 Everyone
Director of Data Science
WEDNESDAY, MAY 10
Innovative Partnerships with Industry
Chair: Liz Langdon Gray (Harvard University)
In this session, we will explore the various ways in which different types of educational institutions are building relationships with industry and to what ends, including research, regional economic development, social impact, and workforce training. Participants will hear from representatives from public and private universities who will share their own experiences establishing and growing industry engagement programs within data science hubs. The session will explore the broad goals of those programs, the “nuts and bolts” of how programs are structured, how we can measure success, and future opportunities and challenges. Through panelist discussion and engagement with the audience we will work towards defining best practices and identifying ways in which the ADSA community can collaborate to support success in industry engagement. There will be ample opportunity for audience discussion, and attendees are encouraged to share their own experiences and learn from their colleagues.
Harvard Data Science Initiative
Director of External Engagement
University of North Carolina at Charlotte
Executive Director of the School of Data Science
Co-founder of the Initiative for Analytics and Data Science Standards; Sr. Advisor at the Institute for Experiential AI
The Future of ADSA
Chairs: Micaela Parker and Steve Van Tuyl (ADSA)
In this final session, we will briefly review ADSA activities from the past year, including the pilot Salary and Hiring Survey, solicit feedback on the Summit, and discuss future directions for ADSA. What should we tackle together next?