Looking back at two days of data science discovery and learning, brought to you by the Harvard Data Science Initiative
Dear 2022 conference attendees and speakers,
This year, we were delighted to host the first HDSI Annual Conference since 2019. We are grateful that you were able to join us for these two days dedicated to data science research and education, and we hope that you enjoyed this opportunity to learn from and connect with our community. In the coming years, the HDSI will continue to showcase multidisciplinary topics of critical importance to foster collaborations, encourage data-driven impact, and enable innovative change. We look forward to building upon your support and feedback from this year’s conference to ensure future conferences are meaningful to the Harvard and greater data science community.
All the best,
This year's Harvard Data Science Initiative (HDSI) Annual Conference took place on November 15 and November 16 in Boston, MA at the Harvard John A. Paulson School of Engineering's Science and Engineering Complex (SEC) and Harvard Business School's Klarman Hall. During the two-day event, speakers from across Harvard, academia, and industry showcased data science in research and education through multidisciplinary panel discussions, keynote addresses, a workshop on artificial intelligence, and a tutorial on causal inference. We were delighted to welcome a diverse, multidisciplinary audience from across the University and the public data science community for this annual opportunity to connect with expert methodologists, data science professionals, and educators.
Day 1 of the HDSI Annual Conference began with our Tutorial: Causal Inference led by Professor José R. Zubizarreta (Associate Professor, Harvard Medical School; Associate Professor, Harvard T.H. Chan School of Public Health). In this tutorial, Professor Zubizarreta provided an introduction to causal inference by describing the ideal study design principles to establish cause and effect relationships. Professor Zubizarreta explained the potential outcomes framework for causal inference and the central role of randomization for identification and inference in randomized experiments, in addition to focusing on observational studies distinguishing between study design strategies that aim to control for observed and for unobserved confounding.
View the recording of the causal inference tutorial here and the gallery of photos from this session below. | Photos by Hannah Rose Photography.
Day 1 continued with Workshop: Fairness + Explainability in AI led by Professor Marinka Zitnik and Professor Hima Lakkaraju. Speakers for this session included Arjun (Raj) Manrai (Harvard Medical School), Tina Eliassi-Rad (Northeastern University), Flavio Calmon (Harvard John A. Paulson School of Engineering and Applied Sciences), Sharad Goel (Harvard Kennedy School), Adam Tauman Kalai (Microsoft Research New England), and Irene Chen (Microsoft Research New England). View the photos from the workshop below. | Photos by Hannah Rose Photography.
The HDSI was delighted to co-host Day 2 of the conference with the new Digital, Data, and Design Institute (D^3) at Harvard. Watch the livestream recording of Day 2 here. View the Day 2 photo gallery below along with the schedule and list of speakers. | Photos by Hannah Rose Photography.
Day 2 Schedule:
Panel 1: Communicating Data Science – Trust with complexity
Hanspeter Pfister (Harvard John A. Paulson School of Engineering and Applied Sciences) and panelists Xiao-Li Meng (Editor-in-Chief, Harvard Data Science Review), Carolina Nobre (Assistant Professor, University of Toronto), Lace Padilla (University of California, Merced), and Natalie Dean (Emory Rollins School of Public Health).
Maria De-Arteaga (Assistant Professor, McCombs School of Business, University of Texas at Austin)
Panel 2: Impact Computing – Building an emerging field
Danil Mikhailov (data.org), Milind Tambe (Harvard John A. Paulson School of Engineering and Applied Sciences), Caroline Buckee (Harvard T.H. Chan School of Public Health), and Nelson González (Amazon Web Services).
Martin Tingley (Head of the Experimentation Platform Analysis Team, Netflix)
Panel 3: Agent-Based Modeling – Complex ecosystems in silico
David C. Parkes (Harvard Data Science Initiative), Michael Norton (Harvard Business School), Stephan Zheng (Salesforce Research), Yang Zhang (Bank of Canada)
Postdoctoral Fellows Lightning Talks
Damián Blasi, Esther Rolf, George Dasoulas, Ivana Malenica, Joseph Dexter
Panel 4: Data Science and Climate – Connecting planetary and human health
Francesca Dominici (Harvard Data Science Initiative), Amruta Nori-Sarma (Boston University School of Public Health), Peter Huybers (Harvard University Faculty of Arts & Sciences). Noelle Selin (MIT Institute for Data, Systems, and Society), Sara Beery (MIT)