United States

Problem Context
Machine learning efforts often begin not with “big data,” but with small, imperfect data. Legacy systems store information in non-machine-readable formats, and institutional repositories suffer from poor maintenance and broken links. These challenges compound when integrating data sources with misaligned data definitions, increasing ML workflow complexity. This work demonstrates a practical framework for making small, messy datasets analytically usable through careful preprocessing, transformation, and model selection.

Technical Approach
This paper demonstrates techniques for integrating heterogeneous government datasets to address foundational issues: (1) geospatial crosswalk construction using representative-point projection rather than geometric centroids for correct tract-to-region assignment; (2) variance stabilization for bounded, right-skewed rate data using Box-Cox transformation; (3) multicollinearity reduction in compositional features using isometric and additive log-ratio transformations; (4) model evaluation via stratified 10-fold cross-validation to address sample-size constraints, comparing linear regression and classification approaches. Together, these transformations mitigate spatial misalignment, heteroscedasticity, and multicollinearity while retaining observations—avoiding the need to remove influential cases (e.g., via Cook’s distance) or drop collinear features.

Results
Linear regression models achieved poor explanatory power (R² = 0.47 ± 0.28) with high cross-validation variance, indicating that continuous rate prediction is unreliable under current data constraints. Reframing the problem as binary classification (high-risk vs. baseline regions using 75th-percentile thresholds) yielded 91 % ± 4 % accuracy with F1 = 0.74 ± 0.16, demonstrating stable performance despite class imbalance. SHAP analysis provided ranked feature importance, enabling interpretable model outputs suitable for government decision-making contexts.

Implications
A well-designed pipeline combining careful preprocessing with appropriate model selection can yield actionable insights despite challenging data conditions. Geospatial crosswalk construction, variance stabilization, and compositional feature handling enabled meaningful analysis where raw data integration would have failed. While regression could not produce reliable forecasts (R² = 0.47 ± 0.28), classification identified high-risk regions (91 % ± 4 % accuracy), supporting prioritization and resource-allocation decisions. These methods are relevant for domains facing similar data constraints. The dataset and crosswalk methodology are publicly available on Kaggle for reproducible validation and adaptation.

David Elgas
MS Data Science Candidate, Harvard University
BSAE, Aerospace Engineering, San Diego State University


NAML 2026

Making Imperfect Data Work: A Practical ML Framework for Small-Sample, Multi-Source Data Integration

variance stabilization

compositional data

geospatial analysis

small-sample machine learning

data integration

model interpretability

## Welcome!
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EMNLP 2025

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poster

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The 10th Annual Workshop on Naval Applications of Machine Learning (NAML 2026) was held in San Diego, California, March 2-5, 2026. NAML is a forum for interdisciplinary experts from government, industry, and academia to discuss results of ongoing research on theory and applications of machine learning to address some of the most challenging problems faced by our naval forces. 



This project leveraging AI for Mission Assurance and Quality Management Systems in the Navy proposes the development of an advanced artificial intelligence (AI) framework to enhance mission assurance and quality management systems (QMS) within the Navy. By leveraging cutting-edge technologies such as natural language processing (NLP), large language models (LLMs), and specialized AI agents, the framework will address critical challenges in quality assurance, operational readiness, and controlled unclassified information (CUI) management. The solution emphasizes the secure handling of proprietary information, specifications, and requirements, ensuring compliance with authority to operate (ATO) standards.
Fine-tuned LLMs will be central to the framework, enabling domain-specific capabilities such as summarization, topic modeling, and document comparison (e.g., specification-to-functionality alignment). These models will be trained on curated datasets containing CUI, proprietary data, and technical documentation, ensuring relevance and accuracy. Vector databases will facilitate efficient inference and retrieval of embeddings, supporting rapid analysis and decision-making. Specialized agent assistance will automate routine tasks, such as data curation and wrangling, while providing actionable insights to Navy personnel.
To meet the Navy's stringent security and operational requirements, the AI solution will be deployed as a containerized application optimized for small footprint and hardware constraints. On-premises deployment will ensure data sovereignty and minimize exposure to external threats. Prompt engineering techniques will enhance user interactions, enabling tailored responses and improving human-machine collaboration.
The framework addresses key challenges in mission assurance and QMS, including managing large volumes of sensitive data, ensuring alignment between specifications and functionality, and maintaining operational readiness. By automating quality assurance processes, enforcing standardization, and delivering real-time insights, the project will transform the Navy's ability to ensure reliability, efficiency, and security in its operations. This innovative approach positions the Navy to leverage AI for strategic advantage in modern warfare.

Artificial Intelligence for Mission Assurance

The fusion of Artificial Intelligence (AI) with signal processing has highlighted signal labeling as an essential part of the workflow. This poster/presentation aims to explore the methodologies in signal labeling and the integration of deep learning techniques to automate and optimize the process, while focusing on an example of automated labeling of flight maneuvers. While some flight test data sets are already labeled, a large set of historical data may be missing or have less accurate labels. Using AI/ML methods to iteratively label these data sets allows the recovery of this historic data for use in analysis.

Enhancing AI Applications with Automated Signal Labeling – A Flight Test Data Application

Integrated photonics is an innovative technology that merges optical components with traditional electronic circuits, leveraging mature, cost-effective semiconductor manufacturing processes to enable high-speed data transmission and lower power consumption than conventional electrical interconnects. Currently, the market is experiencing robust growth, primarily in data centers and high-performance computing, where photonics-based transceivers are essential for meeting the escalating bandwidth demands of cloud services and AI workloads. Looking ahead, envisioned applications extend beyond telecommunications to include highly integrated solutions like co-packaged optics (CPO) placed directly alongside silicon chips and analog computation systems for lightspeed, energy-efficient matrix multiplication. However, scaling such systems does not come without challenges, such as modulation, loss, noise, and footprint. To address such concerns, we have employed advanced numerical optimization techniques, commonly called inverse design, to the engineering of photonic devices and circuits. Inverse design methods can be used to optimize a user-defined photonic metric with respect to thousands of design parameters using computationally efficient algorithms such as the adjoint state method, leading to, for example, photonic devices and circuits that have near-unity energy efficiency and minimal chip footprints. Furthermore, recent revelations in statistical learning methods such as artificial intelligence and scientific machine learning can be used to enhance inverse design algorithms. We will present our work on integrating the latest automatic differentiation tools into photonic inverse design workflows, constructing physics-informed neural operators as data-free surrogate partial differential equation (PDE) models, and ultra sample efficient optimization methods leveraging low-accuracy surrogates.

Engineering State-of-the-Art Photonic Devices and Circuits with AI-Enhanced Inverse Design

Major theoretical and experimental advances in designing controllable quantum systems have brought us closer to Richard Feynman's vision of using quantum mechanics to simulate nature. Today, we can access up to hundreds of qubits across a range of device types, including superconducting qubits, neutral atoms, trapped ions, photonics, and more. But to unlock the full potential of quantum computing (QC)—meaningful speedups for real-world problems like simulating chemistry and materials science—we need to scale these quantum systems to orders of magnitude more qubits and do so cost-effectively. This milestone, known as quantum utility, marks the point where quantum computers become truly useful. To achieve quantum utility, it will be necessary to tightly integrate quantum processors (QPUs) with classical, high-performance computing (HPC) systems. This is because, rather than acting as general-purpose processors, QPUs function more like accelerators or coprocessors, designed to tackle highly specialized tasks that are beyond the reach of classical computers. This hybrid HPC-QC model will be essential not only in the near term—the noisy, intermediate-scale quantum (NISQ) era—but also for future fault-tolerant quantum computation (FTQC), as quantum error-correction schemes will rely heavily on classical co-processors. To achieve so-called utility-scale quantum computing, it will be necessary to integrate with existing heterogeneous HPC infrastructures and develop a hybrid quantum-classical full computing stack. In other words, we will need to build a quantum supercomputer. 

We present a full quantum-classical stack with tight integration of HPC and QC in multiple layers. At the highest layer, we extend familiar HPC programming environments to include QC capabilities, making it easier for developers to run quantum tasks alongside classical, high-performance ones. This includes a quantum accelerator API, which allows seamless execution of quantum algorithms, and a smart scheduler that efficiently distributes quantum workloads across available resources. At a lower level, we provide an HPC solution for circuit compilation and real-time quantum error correction that uses known hardware profiles to synthesize optimized fault-tolerant circuits. Integration at this layer will require fast communication between the quantum and classical components, and can be enabled by ultra-low latency interconnects. Novel algorithms and heterogeneous coprocessing—including CPU/GPUs, QPUs, and probabilistic processing units (PPUs)—allow the system to partition a particular problem into subproblems and send them out to the right coprocessors, enabling even more efficient execution. To aid in designing and evaluating the hybrid stack, we provide resource estimates for utility-scale (FTQC) problems in materials science as well as demonstrate HPC-QC integration workflows for several near-term (NISQ) applications.

How to Build a Quantum Supercomputer: Enabling Utility-Scale Quantum Computing with HPC-QC Integration

Predictive maintenance is a high value application of machine learning, enabling early detection of degradation and reducing unscheduled failures. Across domains such as rotating machinery, HVAC systems, batteries, and unmanned platforms, ML driven programs report 18–25% reductions in sustainment cost and up to 40% reductions in downtime in mature systems. Naval and industrial assets, however, often operate in distributed, communication-limited environments where centralized training or cloud-based inference is impractical.

Distributed Predictive Maintenance (DPM) addresses this constraint by enabling edge nodes to analyze local sensor data, detect anomalies, and forecast failures using onboard ML models. Rather than sending raw data to a central server, nodes exchange compact model updates that allow a global model to emerge collaboratively. Our approach employs a fully decentralized learning algorithm, removing the parameter-server dependencies common in federated learning and enabling low latency inference even when networks are degraded, partitioned, or intermittently connected.

Communication efficiency is a central challenge: scalable model exchange requires many nodes to share updates without overwhelming RF resources. Traditional networks assume orthogonal channels per device, which becomes infeasible as sensor density grows to hundreds or thousands of shipboard or expeditionary IIoT nodes. To address this, Trabus Technologies developed the Spectrally Efficient Peer-to-Peer (SEPP) waveform, which allows many nodes to communicate simultaneously within the same band and reduces the overhead associated with distributed optimization.

Building on this capability, SEPP-DPM integrates SEPP with decentralized learning algorithms leveraging current research from Chris Brinton at Purdue University in topology-aware and consensus-based optimization. This enables autonomous condition-based maintenance driven by real-time performance trends rather than fixed schedules. The architecture remains resilient to communication loss, node churn, and non-IID data distributions typical of naval environments.

A key innovation is SEPP-DPM’s dual-model architecture:
• the Distributed System Health Model (DSHM), an online trained representation of equipment behavior; and
• the Distributed Communication Channel Model (DCCM), which captures time varying RF conditions that influence model-exchange strategies.

These models co-evolve, enabling joint optimization over sensing, communication, and learning. Nodes adapt update frequency, peer selection, and aggregation schedules based on channel estimates, yielding an ML system that is both data efficient and communication aware.

By unifying decentralized learning with efficient communications, SEPP-DPM provides a scalable framework for distributed fault detection, anomaly prediction, and resilience in naval environments, supporting higher availability and ML driven decision superiority at the tactical edge.


Spectrally Efficient Peer-Peer Networking for Enhanced Distributed Predictive Maintenance

Cyber-physical systems (CPS) increasingly require real-time intelligence and autonomous operation in environments where centralized analytics cannot meet latency, bandwidth, and reliability constraints. Traditional architectures that rely on cloud, or server-based processing may introduce delays and vulnerabilities that compromise system responsiveness during critical events. We propose an on-edge intelligent architecture utilizing expert device-layer agents, each powered by a specialized Small Language Model (SLM) fine-tuned with physics and behavior informed data. Each agent will be deployed directly to a compact embedded platform, e.g. Raspberry Pi unit, interfaced with individual field devices. 

Our approach integrates two adapter types: one trained on device-specific operational physics and another trained on historical behavioral data. This dual-adapter design enables increased contextual awareness for anomaly detection, data diagnostics and natural-language explanations. 

The experimental framework compares the deployed agents with a centralized baseline solution running on a remote server, creating controlled comparisons between conventional systems and distributed devices. This architecture allows for direct measurement of latency reduction from local inference, and an increase in accuracy performance from physics and behavior aware expert agents. 

Validation is performed using hardware-in-the-loop, power-grid simulators with physical SEL hardware to generate representative data streams. Two sets of three distinct SEL hardware devices are deployed. One set will operate with the proposed decentralized method, and the other set will operate with the typical centralized solution. This work introduces a pathway towards highly interpretable, low-latency, and resilient on-edge intelligence for mission-critical CPS applications.

Expert Device-Layer Agents for Enhanced On-Edge Intelligence in Cyber Physical Systems

A transfer learning approach was adopted, using a VGG16 neural network model to classify earthquakes from non-earthquake events in North-East China close to the North Korean test site. Magnitudes below 4 are considered for classification of tectonic events (earthquakes) from explosions. Because of the scarcity of observed explosion data, synthetic seismograms were generated using SW4 to augment the observed data so that overfitting doesn’t happen during validation. Our preliminary experiment shows promising results which may help us identify any potential future small event.

Machine-Learning Event Classification in North-East China using augmented data

The Small Weapons Automated Scoring (SWAS) system is a cost-effective, computer vision-based solution designed to modernize and enhance marksmanship training. SWAS automates the traditionally manual, time-consuming, and subjective process of scoring bullet impacts on targets. By leveraging a standard IP camera and a sophisticated machine learning model, the system provides shooters with immediate, objective, and data-rich feedback on their performance. The application processes this video feed in real-time, feeding each frame into a purpose-built computer vision model trained to detect and locate new bullet impacts. Key outcomes include improved training efficiency, detailed performance analytics for individual and programmatic review, and a significant reduction in the personnel and resources required for range operations.

The system's primary objective is to deploy a low-cost, lane-based automated scoring system that provides instantaneous feedback and robust data logging. SWAS empowers instructors and shooters with precise, actionable data, accelerating skill development and creating a comprehensive digital record of performance over time.

Small Weapons Automated Scoring

In support of developing a credible digital twin for prognostic health management of Navy-relevant equipment, one must first assess the quality of captured data before processing for either digital twin development or health assessment. This paper focuses on assessment of data quality in time-series vibration sensor data. Accelerometers are common in many machinery and equipment health management systems. Sources of poor quality, or corrupted vibration sensor data include internal sensor faults, wiring and connector problems, and incorrect or loose sensor mounting. Other common types of sensors used for health management, such as current or pressure sensors may have similar failure modes. The signal path from measurand to health management processing algorithms are themselves systems that require monitoring and assessment.

Numerous computational techniques exist to varying levels of maturity for monitoring the health of a system from which one can acquire data, including variants of statistical process control and scientific Machine Learning. Those techniques are ubiquitous for assessing equipment health. Whatever the chosen technique to monitor a process, one must curate the data appropriately. In collaboration with the Naval Surface Warfare Center Carderock Division, our team has developed a classifier using machine learning to distinguish between “good” and “bad” data from piezoelectric accelerometers. Algorithms extract salient features from vibration data and predict the class of data quality.

This paper describes the preprocessing, feature extraction and training of the classifier for data quality. We demonstrate our methodology on data sets acquired from machinery on a representative ship platform, from a shaker in a laboratory, and a combination thereof. A secondary goal of the research was to explore the use of simulated sensor data (in this case from a shaker) to supplement the relatively small amounts of actual faulty sensor data. The methodology also includes the use of real modeled sensor data to generate representative laboratory data for model development. This is a common problem in the development of health monitoring systems, for sensor as well as machinery faults. The results show both the ability to detect sensor quality issues and the successful use of supplemental data in model training. 


Feature Engineering and Classifier Training Method for Vibration Data Quality Assessment

My team at the University of Arizona’s SpaceTREx Laboratory has been working extensively with planetary scientists on advancing spacecraft technologies for high-priority reconnaissance missions. Among them are plans for fast flyby of small bodies, moons, and interstellar objects. For these application areas, we have been proposing spacecraft swarms that fly by a body or inspect it, providing rapid 3D mapping and imagery. The organization and coordination of these swarms are achieved through the use of Machine Learning algorithms that evolve the craft design, swarm organization, and trajectories of multiple crafts. Building on these flyby mission concepts, we are beginning to explore alternative techniques, including rendezvous, impactor/ramming, and landing/docking. Many of these architectural and operational techniques may be applicable for robotic UUVs. Using a swarm or fractionated modular craft, we may split groups depending on needs to take on specialized tasks, including maintaining communications links, inspecting a craft, or performing analysis from a complex debris field. Given a target, maybe an uncooperative craft, one or a few may get close, but others in the swarm would maintain a safe distance. If the craft is uncooperative and is on a collision course with vital assets, one consideration may be to use a ramming spacecraft that impacts and conjoins with the uncooperative craft and moves it off onto a safe trajectory. In other examples, we have used a spacecraft swarm to perform multipoint observation of a complex 3D event without any craft being a single point of failure for the evolving mission. These projects are early-stage and currently supported by NASA and Brinson Foundation funding. We have made promising progress in simulation and are planning towards an early-stage hardware demonstration in the laboratory through external support in preparation for field demonstrations.

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