Columbia-IBM Center for Blockchain and Data Transparency COVID-19 Research Grant Recipients
The Columbia-IBM Center for Blockchain and Data Transparency brings together cross-disciplinary teams to advance innovation in blockchain, data transparency, and closely associated topics for the good of society and industries that will drive new science and technology, develop thought leadership, and influence policy. The Center was established by the Data Science Institute and School of Engineering and Applied Science in close partnership with IBM.
A special call focused on COVID-19-related research and aimed at delivering tangible results in six months. Areas of interest included secure sharing of health data, privacy preserving contact tracing, security and privacy of virtual meeting spaces, and other topics associated with the Center's mission.
The following Columbia researchers were selected for funding. The Center will provide a one-time award of up to $50,000 and recipients may submit an extension proposal for an additional $50,000 after six months depending upon progress.
Transparent food supply chain systems: Towards increasing efficiency and sustainability under uncertainty
Agostino Capponi, Associate Professor, Columbia Engineering
Capponi's team will develop metrics to assess the resiliency and sustainability of a multi-tier supply chain food system. The framework will incorporate (1) state-dependent price schedule and demand functions, accounting for the freshness of the products, and (2) uncertainty in demand schedules and delivery time, two key features of the COVID-19 pandemic. They will analyze the efficiency of a blockchain platform in reducing waste costs from spoilage, when firms may cut their production due to the reduced workforce and sell outdated or close to expired products to be profitable.
Cryptographic tools for secure sharing and learning in the wake of COVID-19
Tal Malkin, Professor, Columbia Engineering
Sharing of health data is critical towards containing the ongoing pandemic, from contact tracing to prevent spread, through testing and learning of symptoms, risks, and treatment outcomes, to collaboration among different groups working on vaccine development and treatments. Malkin's team will develop cryptographic tools for secure data sharing and collaboration while focusing on two technical domains: (1) collaborative secure computation in a setting without a trusted party, and (2) privacy-preserving machine learning.
Increasing usage of exposure notification applications
Eric Johnson, Norman Eig Professor of Business and Director, Center for the Decision Sciences, Columbia Business School
Widespread adoption and use of contact tracing apps can help reduce the spread of COVID-19 as the world's economies reopen. Johnson's team will study how insights from choice architecture can promote the uptake and understanding of these apps, ensuring that as many infected or exposed people are informed as possible.