Department of Homeland Security and Data!

We are very excited to have the DHS come to Data Innovation DC to offer a series of lightning talks including:

Stephen Dennis

Topic: DHS S&T HSARPA Data Analytics Engine

Many business processes can benefit from the leverage of corporate data sources to create efficiencies and improve missions and operations. The DHS Data Analytics Engine examines cross cutting technologies for automating computational analytics across S&T programs and the homeland security enterprise. This lightening talk will provide an overview of the S&T strategic goals, major program areas, and engine construct.

Chuck Lewis

Topic: Big Data Laboratory

HSARPA DA-E maintains and operates a state-of-the-art data analytics laboratory to support mission-relevant evaluations of emerging technologies, rapid experimentation, and strategic R&D efforts. The lab infrastructure complies with privacy authorities and maintains security accreditation, allowing DA-E to use operational data to help DHS identify enterprise data analytics architectures and solutions.

James Younger

Topic: Big Data Security

This talk will focus on the challenges surrounding securing Big Data systems. Hear how traditional security models are adapted to areas of Big Data security, where new approaches are being used and how Big Data itself is being utilized to provide more efficient and effective security operations.

Shane Cullen

Topic: Big Data Analytics for DHS Components

DHS S&T is assisting agencies with the development and fielding of advanced analytic systems. This lightning talk will focus on the system architectures and management methodologies that have resulted in successful analytic deployments.

Aaron Mannes

Topic: Big Data and Terrorism Analytics: Finding Haystacks

Big data is a powerful tool for counter-terror. The ability to combine and analyze a range of social, cyber, and physical data can be leveraged to find terrorists, predict attacks, and understand their networks. Big data can find broad trends – the haystacks. But it is not an all-purpose panacea. When big data tools are used to find needles they may find too many, raising a host of legal and privacy concerns.

Alexandria Phounsavath

Topic: Risk Assessment

How can DHS use data to respond to risk? The most familiar application of data analytics for risk assessment may be passenger screening at airports. TSA screens approximately 1.8 million passengers a day at 448 airports. Additionally, it prescreens 2 million passengers before they arrive at the airport. This discussion focuses on specific efforts at DHS’s Science and Technology Directorate (S&T) that will further enhance the use of data for risk-based screening in aviation.

Chris Featherston

Topic: Alaska/Arctic Situational Awareness Project (A/ASAP)

DHS has an interest in understanding national infrastructure assets and navigation issues in the Arctic region. The By examining, correlating, and developing analytic predictive tools, it is purposed that DHS can identify trends and datasets that can be tracked, measured and provide a timetable for infrastructure and asset management, movement, or protection. The overall objective for this topic area is developing a predictive analytic capability using observable and historical sensor modeling of climate change in Alaska Arctic areas.

Linda Vasta

Topic: Wearable Technologies’ Analytics

In support of one of S&T’s Visionary Goals: “Responder of the Future: Protected, Connected, and Fully Aware”, the Data Analytics Engine is exploring how data generated from sensors in wearable technologies can inform the first responder and emergency management community. A key step in this process is obtaining “customer input” from this community. The talk will briefly discuss the methodology and process that will be used to obtain this critical input.

Daniel Marasco

Topic: Smart Cities

Availability of new digital devices and enhanced connectivity has caused a shift in the way we develop urban environments. “Smart” cities leverage new technologies to solve new and old urban issues. In the past, cities were developed with static infrastructure, rigid in their operation and expansion. Smart cities can use data from vast sensor networks to make decisions, rapidly adapting and optimizing urban infrastructure and systems for ever-changing conditions. Smart city sensors can improve energy efficiency, optimize the pedestrian and vehicle traffic, evaluate principles of urban planning, and provide situational awareness and guidance in emergencies. However, information and communication infrastructure must be intelligent, resilient and adaptable to maximize impact. With proper development, smart cities will be more sustainable, secure, and livable than their more traditional counterparts.