ADSA Annual Meeting

Charlotte Cabasse-Mazel in a small group with other female data scientists

Submit a proposal for your talk or session

We are now accepting proposals for the 2020 ADSA Annual Meeting. For more information and to access the submission form, click the Submit a Proposal button.

Submit a Proposal

Save the Date! The 2020 Academic Data Science Alliance Annual Meeting will be held October 14 - 16, 2020 as a FREE virtual meeting. The 2020 ADSA Annual Meeting will bring together data science methodologists and domain researchers from all disciplines and career stages to share breakthroughs and new approaches in data science research and education, with a strong emphasis on responsible data science. We are encouraging new, untested ideas to promote brainstorming for innovation and promote collaborative feedback and engaging discussions. One focus of this meeting is building new collaborations, which we will facilitate through semi-structured and themed networking sessions.

Our Program Committee is encouraging submissions on timely topics such as:

  • Methods for addressing structural or institutional racism and other social justice issues
  • Covid-related research that leverages cross-discipline collaboration, e.g. risk-based analysis, interdependencies, human contexts
  • Explainable machine learning
  • Tooling for data collection, curation, annotation, and correction/revision
  • Physics-guided machine learning
  • Teaching and communicating about uncertainty and how to evaluate uncertainty in models like those that have emerged because of the pandemic
  • And whatever excites you!

Meet our 2020 Program Committee

Michael Fire

Ben-Gurion University

Alexander Franks

University of California, Santa Barbara

Stephanie Hicks

Johns Hopkins University

Meredith M. Lee

University of California, Berkeley

Jing Liu

University of Michigan

Brian McFee

New York University

Chris Mentzel

Stanford University

Jenny Muilenburg

University of Washington

Renata Rawlings Goss

Georgia Institute of Technology

Sarah Stone

University of Washington

Micaela Parker

Academic Data Science Alliance

Steve Van Tuyl

Academic Data Science Alliance