Official website of the Department of Homeland Security
News Release: S&T Announces 2020 Privacy Technology Demonstration
DHS S&T is calling on companies developing privacy-enhancing technologies to submit applications for the 2020 Privacy Technology Demonstration.
Video
TIES: Securing Mobile Network Infrastructure - DHS and Industry Solutions for the Future
TIES are a series of nation-wide collaborative events designed to engage, develop and foster relationships while educating innovator communities about DHS’ vast mission, homeland security challenges, and specific component operational needs.
TIES: Securing Mobile Network Infrastructure - CISA Mission Area
TIES are a series of nation-wide collaborative events designed to engage, develop and foster relationships while educating innovator communities about DHS’ vast mission, homeland security challenges, and specific component operational needs.
TIES: Securing Mobile Network Infrastructure - S&T Perspective
TIES are a series of nation-wide collaborative events designed to engage, develop and foster relationships while educating innovator communities about DHS’ vast mission, homeland security challenges, and specific component operational needs.
S&T Deconstructing SBIR Videos
A series of videos explaining the SBIR process.
S&T NGFR Future of First Response Videos
The Next Generation First Responder (NGFR) Apex Program is a five-year program that began in January 2015 but is part of a longer-term Department of Homeland Security (DHS) Science and Technology Directorate (S&T) commitment to envision and assist the responder of the future. NGFR continually collaborates with first responders across the nation on various projects -- from developing program requirements to testing prototypes of technology. These cutting-edge technologies will improve emergency response time and accelerate decision-making to save more lives.
S&T Magello Fact Sheet and Video
Marcello is a customizable, Web-based tool that integrates oceanic and atmospheric forecasting, plume modeling, and real-time information updates that can help decision-makers and emergency responders prepare for, respond to, and recover from events. The tool overlays multiple datasets, model outputs, and information sources in a single, user-friendly interface.
S&T Predicting Crowd Behavior Fact Sheet and Video
The Port Authority of New York and New Jersey (PANYNJ) is tapping a high-tech approach developed by the DHS S&T Center for Visualization and Data Analytics (CVADA) at Rutgers to enable safer crowd movement during emergencies and other events. The CVADA-Rutgers research team uses a combination of mathematical modeling and LiDAR (light and radar) to determine the best way to manage rush-hour crowds and bus schedules—including emergency evacuations— before, during, and after PANYNJ’s major reconstruction and rehabilitation construction projects.
S&T ADCIRC Model Fact Sheet
The ADvanced CIRCulation (ADCIRC) storm surge model combines rain, atmospheric pressure, and wind forecasts to predict when, where, and to what extent flooding will inundate a coastal community with greater precision than other available models. This enables decision-makers to identify which locations will become unsafe and plan for mitigation and response before severe storms occur.
S&T Disaster Recovery Tracking Tool Fact Sheet and Video
The Disaster Recovery Tracking Tool includes 79 metrics that measure how a community is recovering from a disaster. These metrics (e.g., total disaster related business closures, number of organizations involved in recovery, median home value) are organized in four themes and 10 focus areas that are based on FEMA’s recovery support functions and core capabilities in order to link the metrics to the National Disaster Recovery Framework (NDRF) guidance. Researchers are also working to test ways in which the metrics can be used to characterize recovery progress, detect problems with recovery, and improve future recovery and resilience. A practitioner checklist will help end users decide which metrics to use and how to begin collecting data.
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