The Directorate for Computer and Information Science and Engineering (CISE) of the National Science Foundation (NSF) and the Office of Financial Research (OFR) of the Department of Treasury share an interest in advancing basic and applied research centered on Computational and Information Processing Approaches to and Infrastructure in support of, Financial Research and Analysis and Management (CIFRAM). NSF and OFR have established a collaboration (hereafter referred to as CIFRAM) to identify and fund a small number of exploratory but potentially transformative CIFRAM research proposals. The collaboration enables OFR to support a broad range of financial research related to OFR’s mission, including research on potential threats to financial stability. It also assists OFR with the goal of promoting and encouraging collaboration between the government, the private sector, and academic institutions interested in furthering financial research and analysis. The collaboration enables the NSF to nurture fundamental CISE research on a variety of topics including algorithms, informatics, knowledge representation, and data analytics needed to advance the current state of the art in financial research and analysis. Proposals that involve collaborations between Computer Scientists, Mathematicians, Statisticians, and experts in Financial Risk Analysis and Management are especially welcome. Topics of interest in CIFRAM include, but are not limited to: Analysis of financial networks; algorithms and methods for measuring threats to financial stability; Representation and standardization of financial data and information; Formal methods for representation and analysis of financial contracts and regulations, e.g., logics, ontologies, and rule-based approaches; Complexity of financial systems and relationships; Technologies for modeling and monitoring financial systems and infrastructure; Visualizations of the financial system and its attendant risks; Financial risk management techniques for the quantification of uncertainty and risk, including stress testing, risk and volatility forecasting, and the modeling of statistical distributions, processes, and dependence structures; Representation and querying of uncertain financial data, such as marks to model for infrequently traded instruments; Storage and query tools and techniques applicable to financial data; Assembly, integration, and analysis of new datasets for financial research; Techniques for ensuring the security and confidentiality of sensitive financial data, including approaches for selective sharing; Technologies and methodologies to support investigations of failures and disruptions in financial markets, such as those that might arise from (or be exacerbated by) automated high frequency trading systems; Simulation of financial systems, for example using Monte Carlo and agent-based methods; and Tools to support financial policymaking and decision-making.