Defense Advanced Research Projects AgencyAbout UsOfficesDefense Sciences Office

Defense Sciences Office (DSO)

DSO Banner

DARPA's Defense Sciences Office (DSO) identifies and pursues high-risk, high-payoff research initiatives across a broad spectrum of science and engineering disciplines and transforms them into important, new game-changing technologies for U.S. national security. Current DSO themes include frontiers in math, computation and design, limits of sensing and sensors, complex social systems, and anticipating surprise. DSO relies on the greater scientific research community to help identify and explore ideas that could potentially revolutionize the state-of-the-art.

Useful links to work with DSO

Highlights

Episode 32: The Opportunity Hunter

Episode 32: The Opportunity Hunter

In this episode of the Voices from DARPA podcast, Air Force Lt. Col. C. David Lewis, a program manager since 2018 in the agency’s Defense Sciences Office, takes listeners on a tour of the amazingly diverse portfolio of programs he oversees. The foci of these range from the deep math underlying optimization challenges, such as planning complex routes and managing supply chains, to using untapped signals in the atmosphere as indicators of both natural and human activities on the planet’s surface.
Episode 29: The Light and Matter Maestro

Episode 29: The Light and Matter Maestro

In this episode of the Voices from DARPA podcast, Dr. Michael Fiddy, a program manager since 2016 in the agency’s Defense Sciences Office (DSO), takes listeners on a whirlwind tour of his programs.
Episode 27: Detecting Threats with Time to Act

Episode 27: Detecting Threats with Time to Act

In this episode of the Voices from DARPA podcast, Mark Wrobel, a program manager since 2019 in the agency’s Defense Sciences Office (DSO), chronicles progress in the SIGMA+ program and its potential near-term relevance to monitoring the environment for SARS-CoV-2, the virus that causes COVID-19.

Tags

| Agency | Autonomy | Complexity | Fundamentals | Materials | Math | Sensors |

 

Opportunities

To view a selective listing of solicitations posted by this office please visit the DSO Opportunities page, where you can further sort by topic.

Programs

Collapse List     View 10 | View 50 | View All

µBRAIN

Dr. Jiangying Zhou
The past decade has seen explosive growth in development and training of artificial intelligence (AI) systems. However, as AI has taken on progressively more complex problems, the amount of computation required to train the largest AI systems has been increasing ten-fold annually. While AI advances are beginning to have a deep impact in digital computing processes, trade-offs between computational capability, resources and size, weight, and power consumption (SWaP) will become increasingly critical in the near future. More
| Artificial Intelligence | Autonomy | Bio-systems | ISR | Neuroscience | Processing | SWAP |

Accelerated Molecular Discovery (AMD)

Dr. Anne Fischer
Efficient discovery and production of new molecules is essential to realize capabilities across the DoD, from simulants and medicines essential to counter emerging threats, to coatings, dyes and specialty fuels needed for advanced performance. More
| Artificial Intelligence | Automation | Autonomy | Chemistry | COVID-19 | Data | Fundamentals | Integration |

Agile Teams (A-Teams)

Dr. John S. Paschkewitz
The Agile Teams (A-Teams) program aims to discover, test, and demonstrate generalizable mathematical abstractions for the design of agile human-machine teams and to provide predictive insight into team performance. More
| Autonomy | Complexity | Interface | Math | Systems |

All Together Now (ATN)

Dr. Tatjana Curcic
The goal of All Together Now (ATN) is to develop theoretical protocols and experimental techniques that enable new collective atom regimes, leading to sensitivities approaching the ultimate fundamental limits of performance. More
| Fundamentals | Photonics | PNT | Quantum |

Artificial Intelligence Research Associate (AIRA)

Dr. Jiangying Zhou
The Artificial Intelligence Research Associate (AIRA) program is part of a broad DARPA initiative to develop and apply “Third Wave” AI technologies that are robust to sparse data and adversarial spoofing, and that incorporate domain-relevant knowledge through generative contextual and explanatory models. More
| Algorithms | Analytics | Artificial Intelligence | Data |

Atmosphere as a Sensor (AtmoSense)

Lt. Col. C. David Lewis, USAF
The Atmosphere as a Sensor (AtmoSense) program is a fundamental science program that seeks to understand the propagation of mechanical and electromagnetic energy from the surface of the Earth through the Earth's ionosphere due to transient events such as meteorological sources, geophysical sources, prompt hazards, etc. For example, an event on the surface of the Earth, such as a volcanic eruption, will produce radially outward longitudinal mechanical perturbations on the atmosphere. More
| Space |

Competency-Aware Machine Learning (CAML)

Dr. Jiangying Zhou
In order to transform machine learning systems from tools into partners, users need to trust their machine counterpart. One component to building a trusted relationship is knowledge of a partner’s competence (an accurate insight into a partner’s skills, experience, and reliability in dynamic environments). While state-of-the-art machine learning systems can perform well when their behaviors are applied in contexts similar to their learning experiences, they are unable to communicate their task strategies, the completeness of their training relative to a given task, the factors that may influence their actions, or their likelihood to succeed under specific conditions. More
| Adaptability | Algorithms | Artificial Intelligence | Autonomy | ML | Trust |

Complex Adaptive System Composition And Design Environment (CASCADE)

Dr. John S. Paschkewitz
System-of-Systems (SoS) architectures are increasingly central in managing defense, national security and urban infrastructure applications. However, it is difficult to model and currently impossible to systematically design such complex systems using existing tools, which has led to inferior performance, unexpected problems and weak resilience. More
| Complexity | Math | Systems |

Computable Models (COMP Mods)

Dr. Jan Vandenbrande
Accurate multi-physics simulation codes are essential for understanding the behavior of complex DoD systems, but they are generally not available from the commercial sector and have to be custom built. Current approaches to building simulation codes scale poorly with the number of interacting physics involved and often introduce inaccuracies that are difficult to trace and quantify. More
| Algorithms | Complexity | Math | Programming |

Deep Purposeful Learning (Deep Purple)

Mr. Ted Senator
Deep Purple aims to advance the modeling of complex dynamic systems using new information-efficient approaches that make optimal use of data and known physics at multiple scales. The program is investigating next-generation deep learning approaches that use not only high throughput multimodal scientific data from observations and controlled experiments (including behaviors such as phase transitions and chaos), but also of the known science of such systems at whatever scales it exists. More
| Algorithms | Artificial Intelligence | Bio-complexity | Data |