Current Research

Intro text

Program Manager

For information contact: dni-iarpa-info@iarpa.gov

Program Information

IARPA-BAA-16-10

final logo versions 72dpi-02The MOSAIC program seeks innovative approaches to unobtrusive, passive, and persistent measurement to predict an individual’s job performance.

The goal of the MOSAIC program is to improve the Intelligence Community’s capabilities to evaluate its workforce throughout their careers. The program aims to advance multimodal sensing to measure personnel and their environment unobtrusively, passively, and persistently both at work and outside of work, reduce the time and manpower required to process and integrate such data, and construct personalized and adaptive assessments of an individual that are accurate throughout the individual’s career.

Performers (Prime Contractors)

Lockheed Martin; The University of Memphis; The University of Notre Dame: The University of Southern California

Program Manager

David Markowitz

Program Information

IARPA-BAA-18-03

MIST logo finalThe Molecular Information Storage program seeks to use sequence-controlled polymers as the basis for deployable storage technologies that can eventually scale into the exabyte regime and beyond with reduced physical footprint, power and cost requirements relative to conventional storage technologies.

Program Manager

David Markowitz

Program Information

MICrONS Explorer
IARPA-BAA-14-06

MICrONS seeks to revolutionize machine learning by reverse-engineering the algorithms of the brain. The program is expressly designed as a dialogue between data science and neuroscience. Participants in the program will have the unique opportunity to pose biological questions with the greatest potential to advance theories of neural computation and obtain answers through carefully planned experimentation and data analysis. Over the course of the program, participants will use their improving understanding of the representations, transformations, and learning rules employed by the brain to create ever more capable neurally derived machine learning algorithms. Ultimate computational goals for MICrONS include the ability to perform complex information processing tasks such as one-shot learning, unsupervised clustering, and scene parsing, aiming towards human-like proficiency.

Performers (Prime Contractors)

Allen Institute; Baylor College of Medicine; Harvard University; Princeton University; Broad Institute, Inc.; Georgia Tech Applied Research Corporation

Research Area(s)

  • Computational neuroscience
  • Neurophysiology
  • Connectomics
  • Data science
  • Machine learning

Related Publications

To access MICrONS program-related publications, please visit Google Scholar.

Related Article(s)

 

 

Program Manager

For information contact: dni-iarpa-info@iarpa.gov

Program Information

IARPA-BAA-15-08

Mercury Final logo 72Past research has shown that publicly available data can accurately forecast societal events such as civil unrest and disease outbreaks. For example, DOD’s Integrated Crisis Early Warning System (ICEWS) and IARPA’s Open Source Indicators (OSI) have developed methods to forecast societal events using structural data, news feeds, blogs, web search queries and other publicly available data. However, in many cases, relevant data have significant lag times, lack accuracy or are classified. There has been little research to examine whether classified data from foreign Signals Intelligence (SIGINT) can be used to forecast events with high accuracy and lead-time. The Mercury program aims to fill this gap by developing methods for continuous, automated analysis of foreign SIGINT data to anticipate and/or detect significant events, including military and terrorist activities, political crises and disease outbreaks in Arabic-speaking countries in the Middle East and North Africa.

The Mercury program seeks to develop methods for continuous, automated analysis of SIGINT in order to anticipate and/or detect political crises, disease outbreaks, terrorist activity, and military actions. Anticipated innovations include: development of empirically driven sociological models for population-level behavior change in anticipation of, and response to, these events; processing and analysis of streaming data that represent those population behavior changes; development of data extraction techniques that focus on volume, rather than depth, by identifying shallow features of streaming SIGINT data that correlate with events; and development of models to generate probabilistic forecasts of future events. Successful proposers will combine cutting-edge research with the ability to develop robust forecasting capabilities from SIGINT data.

Research Area(s)

  • SIGINT analytics
  • Event forecasting
  • Machine learning
  • Streaming data
  • Data fusion
  • Disease outbreak

Related Publications

To access Mercury program-related publications, please visit Google Scholar.

Related Article(s)

Program Manager

Carl Rubino

Program Information

IARPA-BAA-16-11

MATERIAL Logo Final 150The MATERIAL Program seeks to develop methods for finding speech and text content in low-resource languages that is relevant to domain-contextualized English queries. Such methods must use minimal training data and be rapidly deployable to new languages and domains. Content that is responsive to the queries will be returned from multiple genres along with succinct summaries in English. The overall end-to-end capability will enable monolingual triage of multilingual datasets.

Performers (Prime Contractors)

Johns Hopkins University; Raytheon BBN Technologies; Columbia University and University of Southern California Information Sciences Institute

Related Program(s)

Babel

Research Area(s)
  • Natural language processing
  • Machine translation
  • Cross-lingual information retrieval
  • Domain recognition and adaptation
  • Multilingual ontologies
  • Multilingual speech recognition
  • Cross-lingual summarization
  • Keyword search algorithms
  • Low resource languages
  • Automatic language identification
  • Machine learning
  • Rapid adaptation to new languages
  • Domains and genres

Related Publications

To access MATERIAL program-related publications, please visit Google Scholar

Related Article(s)

Press Releases and Statements

IARPA Launches “MATERIAL” Program

Program Manager

 For information contact: dni-iarpa-info@iarpa.gov

Program Information

IARPA-BAA-16-01

IARPA-BAA-18-04

MAEGLIN final logo 72dpiThe MAEGLIN program intends to develop an ultra low power chemical analysis system for remote site detection and identification of explosives, chemical weapons, industrial toxins and pollutants, narcotics, and nuclear materials in the presence of significant background and interferents. Program goals include definitive chemical identification of species with an atomic mass < 500 atomic mass units (amu); a system footprint of less than or equal to 1.5 liters and weight of less than or equal to 7 kg, including sufficient power and, if necessary, consumables for two year operation with daily sample analysis; autonomous operation, including calibration; and a modular front end for gas, liquid and particulate aerosol, and bulk liquid and solid analysis.

In Phase 1 (covered by this solicitation) the program will be structured as three separate Thrust Areas:

  • Collection – Low-power, reversible gas phase collection/storage/release technology. Modular front end sampling adaptor to add additional capability for liquid or particulate aerosol and/or bulk liquid and solid phase collection and volatilization.
  • Separation – Low-power, non-destructive separation of chemical mixtures with a broad concentration range, potentially including the ability to “bleed off” all or part of the collected sample if desired. System will use minimal (preferably no) consumables.
  • Identification – Low-power, high-accuracy identification of chemicals as pure compounds or low-count mixtures with a large library. System will use minimal (preferably no) consumables. Phase 2 (which will be covered by a separate solicitation) will focus on system integration and will culminate in a prototype demonstration.

Performers (Prime Contractors)

BAE Systems; Hamilton-Sundstrand; Leidos; MassTech; Signature Science; SRI International; and University of Michigan

Press Releases and Statements

IARPA Announces Launch of MAEGLIN Phase 2

IARPA Launches MAEGLIN Program To Develop Low-Power, Autonomous Chemical Identification Systems

Related Program(s)

SILMARILS

COVID-19 Related Research

 

Research Area

  • Liquid and gas chromatography
  • Flow cytometry
  • Ion mobility spectrometry
  • Preconcentrators and sorbants
  • Ionization techniques
  • Mass spectrometry
  • MEMS technology
  • Microfluidics
  • Computational fluid dynamics
  • Signature library construction and use
  • Clutter detection
  • Multicomponent fits
  • Basis set transformations
  • Micro vacuum pump technology
  • Low power electronics
  • Device SWAP optimization
  • Regenerative energy sources and energy harvesting techniques

Related Publications

To access MAEGLIN program-related publications, please visit Google Scholar.

Related Article(s)

 

Program Manager

Michael Di Rosa

Program Information

IARPA-BAA-15-10

LogiQ FINAL 01The LogiQ Program seeks to overcome the limitations of current multi-qubit systems by building a logical qubit from a number of imperfect physical qubits. LogiQ envisions that program success will require a multi-disciplinary approach that increases the fidelity of quantum gates, state preparation, and qubit readout; improves classical control; implements active quantum feedback; has the ability to reset and reuse qubits; and performs further system improvements.

Performers (Prime Contractors)

Delft University of Technology; Duke University; IBM - T.J. Watson Research Center; University of Innsbruck

Related Program(s)

 

The Logical Qubits (LogiQ) Program seeks to overcome the limitations of current multi-qubit systems, described in the previous paragraph, by building a logical qubit from a number of imperfect physical qubits.  LogiQ envisions that program success will require a multi-disciplinary approach that increases the fidelity of quantum gates, state preparation, and qubit readout; improves classical control; implements active quantum feedback; has the ability to reset and reuse qubits; and performs further system improvements.