Since its launch in 2017, the HDSI has worked across Harvard to unite leading computer scientists, statisticians, and domain experts from law, business, public policy, education, medicine, public health, and myriad academic disciplines to derive meaningful and actionable insights that shape the new science of data. Its research drives data-driven policy and analyzes implications of big data for human society. We welcome your interest and hope you will explore this site to learn more.
COVID-19 Resources Collected by the HDSI
Researchers across Harvard are using data science to tackle the novel coronavirus in an attempt to understand its transmission and slow its spread. In this unprecedented period, we will post their stories on this website and across our social media accounts. Featured work may represent initial results of early stage research conducted at Harvard University and may not yet have undergone peer review. This research is evolving quickly.
If you know of important work or opportunities that you would like us to highlight, please email email@example.com.
Featured Job Listings
Harvard Data Science Twitter
This week's newsletter is out now featuring #datascience-related #Pride resources, a special issue of the @TheHDSR on Differential Privacy and the 2020 Census, recommended reads, and full-time job opportunities: t.co/1IojnVA9iY t.co/ajYbKtroZH
- Just out! Our special issue on #DifferentialPrivacy & the #2020Census edited by the awesome @EricaGroshen, Ruobin Gong & Salil Vadhan with contributions from a diverse group of academics & practitioners. Read the issue intro: t.co/yWphZH8l8O
- Do you have innovative data sharing and reuse stories? Does a prize purse of $500,000 sound good? Registration for the ODSS sponsored DataWorks! Prize closes June 28! #NIHData #DataScience t.co/p1Viodf5Qu
- The Provisional Program for the SDN 2022 Annual Meeting is now posted! Register today and join us in Cambridge, MA July 27-30. t.co/7avyV6TWEs t.co/fNOghfLV6N
- 🥳🥳 I am very happy that our collaboration with @gsalhagalvan, @JLutzeyer, Romain Hennequin, and @mvazirg led to the publication in @ElsevierConnect's Neural Networks Journal of a novel graph autoencoder model, able to jointly predict links and cluster nodes! t.co/ibTShAZwbg