The Seed Fund Program at IDSS supports innovative, early-stage research projects which explore ideas at the interface of data sciences and statistics, information and decision systems, and social sciences to address societal problems.
Through these grants, IDSS seeks to encourage researchers from across MIT to collaborate and to open up new avenues for interdisciplinary research that bring together the full range of Institute capabilities.
Each proposal should have at least two faculty PIs from two different schools. Ideally each proposal will support an IDSS student, and provide a venue for generating additional external funding to support further collaborations. Proposals addressing a broad range of societal challenges are eligible.
Request for Proposals
The IDSS/SSRC Combatting Systemic Racism Seed Fund Program is accepting proposals for innovative, early-stage cross-disciplinary research projects with a focus on combatting systemic racism. Proposals addressing a broad range of systemic racism challenges are eligible, including but not limited to housing, healthcare, policing and education. The Request for Proposals is open until April 15.
Funded Seed Projects
Daron Acemoglu, Economics
Asu Ozdaglar, Electrical Engineering and Computer Science
Acemoglu and Ozdaglar studied how the interconnectedness of the financial system, especially during the years of the 2008 financial crisis, impacted lending between institutions. Their main focus was on how lending can "freeze" on a system-wide basis when lending relationships are varied and complex. These freezes prevent businesses, entrepreneurs, and households from getting access to funding.
Fotini Christia, Political Science
Constantinos Daskalakis, Electrical Engineering and Computer Science
This project combined call detail records on Syrian refugees with a variety of unique data gathered from government service provision and aid organizations to examine prospects for refugee integration in Turkey. Christia and Daskalakis studied how refugee mobility may be affected by factors such as public services, housing opportunities, service provision and policies, or areas with high religious endowments.
Saurabh Amin, Civil and Environmental Engineering
Nazli Choucri, Political Science
Amin and Choucri's research focused on the security and survivability of a critical global infrastructure: submarine fiber optic networks. Undersea networks are highly structured and widely distributed systems that play a critical role in the transmission of global communications. The goal of this project is to create robust foundations for an integrated technology-policy approach to examine the cyber-physical security and sustainability of critical global infrastructures.
Rich Nielsen, Political Science
Ali Jadbabaie, IDSS and Civil and Environmental Engineering
Nielsen and Jadbabaie analyzed data on jihadist extremist documents to build a model that would shed light on why some documents are more popular than others, and predict which new statements by jihadists are most likely to go viral. They used data from a large jihadist web library to build a model that predicts the number of page views based on features in each document.
Stan Finkelstein, IDSS
Roy Welsch, Sloan School of Management
Researchers studied the repurposing of FDA-approved drugs in order to identify new uses of medicines. The goal of this ongoing work is to use observational medical and health data, including harnessing large Electronic Health Record (EHR) datasets using modern data analytic methods and tools, to look for clinical signals that drugs currently prescribed for one condition could be beneficial to patients suffering from another condition. This research could speed up drug development and reduce costs and risks in the future.
Tamara Broderick, Electrical Engineering and Computer Science and Computer Science and Artificial Intelligence Laboratory (CSAIL)
In Song Kim, Political Science
The central goal of this project was to identify hidden economic and societal forces in international trade by developing scalable machine learning algorithms for the probabilistic inference of massive amounts of trade data. Probabilistic inference can establish the complex models required for these analyses, but in practice is often slow to run. Broderick and Kim used computational-statistical trade-offs to obtain the necessary run-time gains to make these analyses more practical.
Daron Acemoglu, Economics
Munther Dahleh, IDSS and Electrical Engineering and Computer Science
Acemoglu and Dahleh studied the relationship between financial network structures and bank failures. They were interested in better understanding how interbank linkages affect the transmission of adverse shocks in a banking system and how they shape the nature of economic fluctuations. They processed and collected data from a historical dataset of the United States banking system from the 19th century to produce an empirical model that tests whether there is a relationship between a bank's performance and the performance of banks to which it is connected.
Jinhua Zhao, Urban Studies and Planning
Nigel Wilson, Civil and Environmental Engineering
Zhao and Wilson examined the social aspects of the mobility sharing system. They developed matching algorithms to adhere to both transportation network optimization and the individuals’ preferences, or lack of preferences, for human interactions. The research ultimately provides insights into how policies should be crafted to acknowledge travelers’ preferences while setting boundaries against potential discriminatory behavior.
Noelle Selin, IDSS and Earth, Atmospheric, and Planetary Sciences
Valerie Karplus, Sloan School of Management
Selin and Karplus integrated economic and environmental modeling and empirical social science techniques to study the effects of energy and climate policies in China. The result of the study was that by meeting its greenhouse gas-reduction goals, China would improve its air quality. This would avoid a significant number of deaths due to air pollution. Fewer deaths from air pollution means a benefit for society that can be quantified as a $339 billion savings in 2030.
Mardavij Roozbehani, Laboratory for Information & Decision Systems
Christopher Knittel, Economics
Roozbehani and Knittel studied the design of electricity markets and incentive mechanisms for the integration of renewable resources. Renewable resources pose challenges to electricity markets, include uncertainty, variability, highly correlated contingencies across large geographical areas, and near zero marginal cost. Roozbehani and Knittel addressed these challenges by developing data driven models and drawing from tools and methodologies in economics, game theory, control theory, and energy networks.