Intelligence Fusion
Combine sensor data, human reporting, geospatial information, open sources, and prior models into a common evidence graph without erasing source identity or reliability.
Deimos-One Starlab // Data Science
Machine intelligence for contested reality.
Starlab develops research systems that fuse incomplete information, test competing explanations, forecast adversarial futures, and produce traceable decisions under uncertainty.
Information state Incomplete
Model ensemble 07 active
Hypothesis contest Running
Decision trace Preserved
Research mandate
The world does not arrive as a clean dataset.
Operational data is delayed, duplicated, biased, adversarial, incomplete, and frequently contradictory. A useful system cannot merely calculate faster. It must determine what is known, what is uncertain, which evidence is decaying, and which explanation still survives contact with new information.
Starlab treats data science as the engineering of belief, prediction, and decision. The objective is not a dashboard. The objective is a defensible operating picture that can change as the environment changes.
Build systems that expose uncertainty, preserve provenance, contest their own assumptions, and still produce an actionable decision.
Input state Fragmented, multimodal, asynchronous, and unreliable.
Research problem Reason correctly when the evidence is insufficient.
System output Forecasts, hypotheses, confidence bounds, and decision traces.
Success condition Useful action without hiding what the system does not know.
Active research domains
From raw signal to challenged decision.
Our data science work focuses on the layers between collection and action: fusing evidence, generating explanations, forecasting outcomes, quantifying uncertainty, arbitrating models, and structuring the final decision.
Predictive Modeling
Estimate evolving system states, detect regime shifts, and generate bounded predictions when historical conditions no longer resemble the present.
Strategic Forecasting
Build branching futures, counterfactuals, indicators, and warning thresholds to examine how complex systems may evolve under adversarial or discontinuous conditions.
Model Arbitration
Compare independent models, expose disagreement, score reasoning quality, challenge unsupported claims, and construct a final answer without pretending consensus exists.
Uncertainty Engineering
Represent confidence, evidence quality, temporal decay, hidden assumptions, and unknown unknowns as first-class system objects rather than burying them inside a single score.
Decision Systems
Convert intelligence and forecasts into structured courses of action with constraints, tradeoffs, trigger conditions, human authority, and an auditable decision path.
Cognitive architecture
Intelligence is a process, not a model output.
A single algorithm cannot carry the full burden of collection, interpretation, forecasting, challenge, and action. Starlab decomposes the problem into explicit research layers so each assumption, transformation, and decision can be inspected.
01 Source identity and timing remain attached to the evidence.
02 Competing hypotheses remain alive until the evidence closes them.
03 Forecasts carry confidence bounds and failure conditions.
04 Final decisions preserve the reasoning chain and human authority.
Model arbitration
When models disagree, the disagreement is intelligence.
Starlab research treats model conflict as a signal to investigate, not a nuisance to average away. Independent models are compared against evidence, constraints, temporal validity, causal coherence, and known failure modes before a final assessment is constructed.
Evidence test Which claims are directly supported, inferred, or merely plausible?
Conflict test Where do models disagree, and what assumption causes the divergence?
Adversarial test How does the answer change under deception, missing data, or false priors?
Decision test Which disagreement materially changes the recommended action?
Uncertainty engineering
Confidence is not certainty with a decimal point.
Reliable systems distinguish observation from inference, confidence from evidence coverage, and known risk from missing knowledge. They also understand that stale information and adaptive adversaries can invalidate yesterday’s strongest model.
Strategic forecasting
Forecast the range of futures, not one comforting line.
Strategic environments are shaped by adaptation, feedback, nonlinear thresholds, and actors who react to being observed. Starlab develops scenario systems that expose alternate futures, identify discriminating indicators, and specify what evidence would force the forecast to change.
01 Generate structurally different futures, not cosmetic variations.
02 Attach observable indicators and warning thresholds to each path.
03 Red-team assumptions against deception, adaptation, and regime shift.
04 Recalculate the decision when the operating picture changes.
Operating environments
Research for systems where failure is expensive.
The methods are designed for environments where data is scarce, timing matters, adversaries adapt, and a plausible answer is not enough.
Battlespace Intelligence
Fuse multi-domain reporting, detect changing threat states, and support time-sensitive courses of action under contested information conditions.
State // dynamicSpace and Near Space
Model sparse observations, orbital or atmospheric uncertainty, mission state, and evolving constraints across persistent autonomous systems.
State // partially observedStrategic Warning
Identify weak signals, competing explanations, escalation pathways, and indicators that distinguish one future from another.
State // anticipatoryCritical Systems
Analyze infrastructure, logistics, economic, and operational networks where cascading effects can outpace conventional monitoring.
State // coupledResearch program model
From hard question to validated decision system.
Starlab programs begin with an operational decision that existing data, models, or workflows cannot support reliably. Research is structured around the evidence, uncertainty, adversary, and transition path required to close that gap.
The output is a working artifact, validated model, technical finding, or defensible program decision—not a presentation about the problem.
Define the decision.
Identify the action, authority, timing, evidence, and failure consequences the system must support.
Build the evidence layer.
Establish source identity, provenance, temporal alignment, reliability, and data-quality controls.
Construct competing explanations.
Develop estimators, forecasts, hypotheses, and model ensembles appropriate to the uncertainty structure.
Attack the system.
Red-team assumptions, expose brittle behavior, introduce deception, and measure calibration under stress.
Prove what holds.
Test against historical, synthetic, adversarial, and live-like scenarios with explicit acceptance criteria.
Move research into use.
Deliver software, model artifacts, technical findings, interfaces, and decision logic that can enter the operating system.
Program inquiries // controlled intake
Build the system that decides when the data cannot.
Engage Starlab on advanced AI, intelligence fusion, predictive modeling, strategic forecasting, model arbitration, uncertainty engineering, and decision-system research.