# Project EMR-01 — MATH AND COMPUTER SCIENCES

**Program element:** 0601122E — Emerging Opportunities  
**Project:** EMR-01  
**Component:** Defense-Wide  
**Appropriation:** 0400 — RDT&E, Defense-Wide  
**Budget Activity:** 1 — Basic Research  
**Vintage:** President's Budget PB2027  
**Canonical URL:** https://hitchintel.com/programs/0601122E/EMR-01  
**Parent:** https://hitchintel.com/programs/0601122E

## Summary

Project EMR-01 — MATH AND COMPUTER SCIENCES requests $195.5M in FY2027, 50% of the $387.6M requested for program element 0601122E, up 8.8% on FY2026. 15 R-2A activities decompose the request.

## Funding profile

| Fiscal Year | Estimate Type | Amount ($M) |
|---|---|---|
| FY2025 | Actual | 0.0 |
| FY2026 | Enacted | 179.6 |
| FY2027 | Request | 195.5 |
| FY2028 | Outyear | 197.4 |
| FY2029 | Outyear | 201.2 |
| FY2030 | Outyear | 206.5 |
| FY2031 | Outyear | 212.6 |

> Estimate types are not summed. This project is one leaf of PE 0601122E; the PE total is the sum of its projects, never added to them.

## What project EMR-01 buys

The Math and Computer Sciences project supports scientific study and experimentation on new mathematical and computational algorithms, models, and mechanisms in support of long-term national security objectives. Modern information technologies, both classical and quantum, and analytic technologies, enable important new military capabilities and drive the productivity gains essential to U.S. economic competitiveness. Conversely, new classes of threats require the creation of fundamentally new mathematical and computational approaches to enhance the software-intensive systems upon which our society depends. The basic research conducted under the Math and Computer Sciences project will produce breakthroughs that enable new capabilities for national and homeland security. Prior to FY 2026, this project was funded in PE 0601101E, Project CCS-02.

## Activities (R-2A) — 15

| Activity | FY2025 | FY2026 | FY2027 | Move | Page |
|---|---|---|---|---|---|
| Math and Algorithms Studies and Concepts | — | 22.3 | 54.4 | +144% | [a11](https://hitchintel.com/programs/0601122E/EMR-01/a11) |
| Artificial Intelligence Studies and Concepts | — | 34.3 | 38.3 | +11% | [a10](https://hitchintel.com/programs/0601122E/EMR-01/a10) |
| Logic Understanding for Complex Information Dissemination (LUCID) | — | 9.2 | 23.7 | +157% | [a7](https://hitchintel.com/programs/0601122E/EMR-01/a7) |
| Foundational Artificial Intelligence (AI) Science | — | 16.4 | 16.1 | −2% | — |
| Young Faculty Award (YFA) | — | 16.2 | 16.0 | −1% | [a1](https://hitchintel.com/programs/0601122E/EMR-01/a1) |
| Exponentiating Mathematics (expMath) | — | 13.9 | 14.1 | +2% | [a2](https://hitchintel.com/programs/0601122E/EMR-01/a2) |
| Translating All C To Rust (TRACTOR) | — | 10.8 | 9.8 | −9% | — |
| Intrinsic Cognitive Security (ICS) | — | 10.3 | 7.5 | −26% | — |
| Fostering Research and Growth in Emerging Artificial Intelligence (AI FORGE) | — | 4.8 | 6.6 | +37% | — |
| Scientific Feasibility (SciFy) | — | 8.8 | 4.5 | −50% | — |
| Mapping Machine Learning to Physics (ML2P) | — | 4.4 | 2.8 | −37% | — |
| Advanced Tools for Modeling and Simulation | — | 6.8 | 1.8 | −74% | — |
| In The Moment (ITM) | — | 14.1 | — | −100% | — |
| Enhanced SBOM for Optimized Software Sustainment (E-BOSS) | — | 5.1 | — | −100% | — |
| Advanced Research Concepts (ARC) | — | 2.2 | — | −100% | — |

> Activities carry the prior, current and budget year only — no five-year plan. In the request year they partition this project exactly; in earlier years they can under-cover it.

### Math and Algorithms Studies and Concepts

- Investigate approaches for enabling cross-platform entanglement across disparate architectures for distributed quantum sensing and computing applications. - Design training for AI experts to profess a given science domain and advance its research and knowledge frontiers. - Enable scientific AI to create useful, communicable…

Full year-by-year narrative: https://hitchintel.com/programs/0601122E/EMR-01/a11

### Artificial Intelligence Studies and Concepts

- Enable the automated generation of quantum algorithms with computational quantum advantage over classical computing paradigms. - Develop a domain-agnostic approach to enable accelerating training through logical, Socratic style, specialist AI tutors. - Develop an approach for constructing common shared knowledge bases about science…

Full year-by-year narrative: https://hitchintel.com/programs/0601122E/EMR-01/a10

### Logic Understanding for Complex Information Dissemination (LUCID)

- Demonstrate that proof explicitly encodes key semantic information that can be mapped to the operational context to enhance the operator's insight. - Develop AI architectures that enable users to enhance system understanding. - Develop test scenarios and proof artifacts and perform test and evaluation within a realistic gaming…

Full year-by-year narrative: https://hitchintel.com/programs/0601122E/EMR-01/a7

### Foundational Artificial Intelligence (AI) Science

**FY2027 planned work.** - Instantiate and extend methods for high assurance in AI systems of systems to military relevant domains. - Develop a software composition library in support of developing methods for high assurance in AI systems.

**FY2026 to FY2027 change.** The FY 2027 decrease reflects minor program repricing.

**FY2026 plans — current year.** - Develop methods to incorporate complex logical knowledge and reasoning into AI systems, enabling assurance in decision support. - Begin creation of scalable, highly reusable, methods for high assurance in AI systems of systems.

### Young Faculty Award (YFA)

- Award FY 2027 cooperative agreements for new two-year research efforts across YFA topic areas, establishing a new set of scientific approaches to solve current DoW challenges. - Continue FY 2026 research on new concepts for microsystem, biological, strategic, and tactical technologies; information innovation; and defense sciences by…

Full year-by-year narrative: https://hitchintel.com/programs/0601122E/EMR-01/a1

### Exponentiating Mathematics (expMath)

- Integrate automated mathematical techniques in an AI system capable of performing complex mathematical derivations and proofs. - Perform initial demonstrations of mathematical knowledge discovery on mathematical problem domains/sub-domains of particular importance for national security. - Demonstrate math automation capabilities at a…

Full year-by-year narrative: https://hitchintel.com/programs/0601122E/EMR-01/a2

### Translating All C To Rust (TRACTOR)

**FY2027 planned work.** - Build tools to assist verification of program behavior equivalence in multi-threaded settings. - Scale up tools to translate C programs to safe and correct Rust while ensuring performance of the translation tools.

**FY2026 to FY2027 change.** The FY 2027 decrease reflects the shift from developing C-to-Rust automated translation technologies to assessing the performance of the translation tools.

**FY2026 plans — current year.** - Research the foundations of programming language translation in multi-threaded settings using formal analysis tools. - Evaluate tools in terms of percentages of C functions translated to safe and correct Rust programs, and in terms of the performance of the translation tools themselves.

### Intrinsic Cognitive Security (ICS)

**FY2027 planned work.** - Harden MR protection technologies and transition to Department of War (DoW) system development activities and acquisition programs. - Facilitate adoption of MR protection technology by commercial communities to enable broad benefits across US Government, DoW, and civilian sectors.

**FY2026 to FY2027 change.** The FY 2027 decrease reflects the shift from developing MR protection technologies to hardening and transition of the technologies.

**FY2026 plans — current year.** - Construct models that support establishing specific guarantees using engineering-relevant elements of the cognitive stack and validate that models are sufficiently high-fidelity. - Validate that the guarantees can be implemented in real, lab-bench quality MR systems and evaluate the effectiveness of cognitive protections.

### Fostering Research and Growth in Emerging Artificial Intelligence (AI FORGE)

**FY2027 planned work.** - Execute collaborative R&D efforts that address critical technical challenges associated with the use of frontier AI for national security. - Coordinate R&D efforts with government activities seeking to use frontier AI for national security applications, systems, and missions.

**FY2026 to FY2027 change.** The FY 2027 increase reflects the shift from planning activities to basic research addressing fundamental issues of assurance, predictability, reliability, resilience, and transparency.

**FY2026 plans — current year.** - Finalize an acquisition strategy and management plan for a joint government-university-industry project that features R&D efforts that solve critical challenges associated with the use of frontier AI for national security. - Execute formal agreements with government and industry partners to establish processes for initiation, approval, and review of R&D efforts focused on the use of frontier AI for national security.

### Scientific Feasibility (SciFy)

**FY2027 planned work.** - Collaborate with transition partners to demonstrate feasibility assessment tools in selected domains of interest.

**FY2026 to FY2027 change.** The FY 2027 decrease reflects the shift from development of feasibility assessment techniques to demonstration and transition.

**FY2026 plans — current year.** - Demonstrate techniques for automated feasibility assessment across multiple scientific domains, such as materials science, artificial intelligence, quantum computing, and other domains and sub-domains. - Collaborate with Department of War and Intelligence Community transition partners to evaluate utility of the feasibility assessments and to refine techniques.

### Mapping Machine Learning to Physics (ML2P)

**FY2027 planned work.** - Construct energy-aware ML and evaluate power usage and performance for a given task.

**FY2026 to FY2027 change.** The FY 2027 decrease reflects a shift from development to evaluation of energy-efficient ML models.

**FY2026 plans — current year.** - Map machine learning (ML) energy efficiency to physics using precise granular measurements in joules and metrics that are directly comparable across hardware architectures.

### Advanced Tools for Modeling and Simulation

**FY2027 planned work.** - Explore techniques to mitigate singularities in mathematical transformations. - Develop tools to detect and mitigate singularities of discovered transformations.

**FY2026 to FY2027 change.** The FY 2027 decrease reflects program completion.

**FY2026 plans — current year.** - Demonstrate artificial intelligence-based predictive modeling of test problems in turbulence guided by tabletop quantum simulators. - Validate predictive models of test problems with computational fluid dynamical techniques. - Examine the limits and modes of failure of useful mathematical transformations. - Assess if it is feasible to provide low fidelity models with high fidelity properties. - Demonstrate discovery of new algorithms for DoW relevant applications. - Develop mathematical methods to systematically find transformations that simplify DoW relevant modeling problems.

### In The Moment (ITM)

**FY2026 to FY2027 change.** The FY 2027 decrease reflects program completion.

**FY2026 plans — current year.** - Validate computational approaches for quantifying alignment and measuring the impact of alignment on trust of algorithmic decision-makers for complex use cases. - Evaluate an algorithmic decision-maker's ability to align with a reference group of human decision-makers. - Collaborate with military stakeholders to evaluate the utility of the human-algorithm alignment techniques in the context of medical triage in military environments.

### Enhanced SBOM for Optimized Software Sustainment (E-BOSS)

**FY2026 to FY2027 change.** The FY 2027 decrease reflects program completion.

**FY2026 plans — current year.** - Extend eSBOM with additional types of metadata and design use cases that are relevant to both open-source communities and Department of War software factories.

### Advanced Research Concepts (ARC)

**FY2026 to FY2027 change.** The FY 2027 decrease reflects program completion.

**FY2026 plans — current year.** - Complete efforts to expand data science techniques for non-ergodic systems.

## What is NOT on this page

Congressional marks, the R-2 mission description and acquisition strategy, the industry vs government split of the whole request, and related program elements are recorded at **program-element** grain — an NDAA mark lands on a PE, never on a project. They are at https://hitchintel.com/programs/0601122E.

## Source & machine access

- **Source:** FY2027 Office of the Secretary of Defense RDT&E Budget Justification, Exhibits R-2/R-2A/R-3, PE 0601122E project EMR-01 (PB PB2027).
- **MCP:** `mcp.hitchintel.com` — `budget_get_program_element(pe="0601122E")`.

*HitchAI is an independent intelligence service, not affiliated with the U.S. Department of Defense. Budget figures are requests/estimates, not obligations.*