Winners of NASA HRP’s Artemis II Data Methodology Challenge

Winners of NASA HRP’s Artemis II Data Methodology Challenge

NASA’s Human Research Program has selected 10 winning projects for the Artemis II Human Research Data Methodology Challenge, a public competition that sought better ways of drawing rigorous conclusions from the small, information-dense datasets that define human spaceflight research.

Each crew member on missions like Artemis II are measured across multiple physiological variables over time, and with only four people on board each mission, data is limited. The challenge asked entrants to design methodologies that extract trustworthy, decision-ready insight from exactly this kind of small-sample, high-dimensional data.

Below are the winners, listed by their final placing. Placings reflect not only the technical rigor of each methodology, but how directly each submission responded to the challenge and how well each submission’s approach matched the real needs of NASA’s Human Research Program. Where entries were closely matched, the edge went to entrants who engaged most fully with the problem and whose methods translate most readily to the Human Research Program’s work. Project approach summaries were pulled from submission materials.

Project Theme: Transfer-Calibrated Normative Modeling

Affiliation: Purdue University

Project Approach: “This project treats the Artemis II challenge – four astronauts measured across hundreds of features over time – as a high-resolution case series rather than a failed population study, applying a stack of proven small-sample, high-dimensional methods (variance-borrowing empirical-Bayes tests, exact permutation inference, shrinkage-based multivariate scoring, and Gaussian-process recovery modeling) on a common calibrated scale. We demonstrate it on real four-person spaceflight data from SpaceX’s Inspiration4 mission crew, recovering a coherent acute inflammatory signature and per-crew recovery trajectories – with automated quality control that catches assay drift rather than misreading it as biology.”

Project Theme: STELLAR (Systematic Trajectory Estimation via Layered Longitudinal Analysis and Regression)

Affiliation: Independent Researcher

Project Approach: “STELLAR is a three-layer Bayesian pipeline designed to extract meaningful signal from the kind of data NASA’s Human Research Program collects on missions like Artemis II: small crews, high-dimensional measurements, multiple biological systems tracked together over time. It combines multi-omics factor analysis v2, Bayesian hierarchical modeling, and Gaussian Process regression, with two purpose-built diagnostic metrics: the Physiological Data Diversity Index (PDDI) and Physiological Complexity Index (PCI), to characterize dataset diversity and physiological complexity. The methodology is built specifically for the small-sample reality of human spaceflight research, where traditional statistical approaches often fall short.”

Team member: Naseem Khan

Project Theme: Unified Hierarchical Bayesian Spine

Affiliation: Waste Parrot Technologies

Project Approach: “Waste Parrot Technologies developed a unified hierarchical Bayesian framework for extracting reliable insight from small-sample, high-dimensional spaceflight health data. The approach condenses large numbers of correlated biomarkers into a small set of interpretable signals and models each astronaut individually rather than averaging across the crew, recovering patterns that conventional methods miss with only four subjects. We demonstrated it end-to-end on public proxy data for two Artemis II studies, Immune Biomarkers and ARCHeR, producing calibrated early-warning flags for unusual physiological readings, designed for real mission constraints such as far-side communication blackout.”

Project Theme: Bayesian N-of-1 with Dynamic Borrowing

Affiliation: Independent Researcher

Project Approach: “This project applies Bayesian hierarchical N-of-1 modeling with dynamic borrowing to the Artemis II problem of extracting rigorous inference from only four astronauts across many measurements. Applied unchanged to SpaceX’s Inspiration4 mission (the only existing real n=4 spaceflight cohort), the model independently recovers textbook spaceflight physiology from serum and urinary biomarkers — demonstrating that a single small-sample framework can honestly quantify what four astronauts’ worth of high-dimensional data can, and cannot, support.”

Project Theme: Dynamic Empirical-Bayes Counterfactual

Affiliation: University of Virginia

Project Approach: “I developed a Dynamic Empirical-Bayes Bayesian Counterfactual Simulation Framework to analyze small-sample, high-dimensional astronaut health data from NASA’s Spaceflight Standard Measures program. The methodology leverages proxy cohorts, empirical priors, Bayesian borrowing, and robust longitudinal analysis to identify meaningful physiological changes while overcoming small sample sizes. This framework provides a scalable and interpretable approach for supporting evidence-based decisions in NASA’s Human Research Program.”

Project Theme: Bayesian State-Space Decision Layer

Affiliation: Independent Researcher

Project Approach: “My project is a hierarchical Bayesian framework for tracking the health of the Artemis II crew, built for a hard case: only four astronauts, each measured across dozens of physiological systems, the exact setting where standard statistics break down. It combines 43 established methods, organized in four layers and drawing on twelve public datasets, into a single system a flight surgeon can act on. I tested the whole thing on real spaceflight data from SpaceX’s Inspiration4 mission, and it runs the same way whether the mission lasts ten days or the several months it takes to reach Mars.”

Project Theme: Mechanism-Constrained AI for Vision Risk (SANS)

Affiliation: Massachusetts Institute of Technology

Project Approach: “I designed a multi-task deep learning model that simultaneously predicts SANS from longitudinal bed rest MRI data and regresses six quantitative biomarkers characterizing glymphatic dysfunction. This kind of approach could let researchers take tools traditionally suited to large-sample data and use them to extract meaningful, biologically grounded insight into the pathology of conditions that affect only a small minority of the population, such as SANS.”

Team members: Sushanta Khadka, Arpan Bom, Pranisha Karki

Project Theme: Integrated Health Monitoring

Affiliation: Vision One

Project Approach: “Vision One developed an Integrated Bayesian Multi-Modal Longitudinal Physiological Analysis Framework to help NASA extract meaningful insights from the Artemis II human research dataset despite having only four astronauts. By combining physiological, cognitive, immune, sleep, activity, imaging, and environmental data into a unified analytical framework, our approach enables personalized health monitoring, early detection of fatigue and stress, and supports future Artemis, Gateway, lunar surface, and Mars exploration missions.”

Project Theme: Integrated Multi-Study Health Profiling

Affiliation: Neuroviu

Project Approach: “This project uses a hierarchical Bayesian multi-block factor analysis to model each Artemis II astronaut individually while leveraging reference data from the International Space Station, SpaceX’s Inspiration4 mission, and terrestrial analog missions to improve statistical reliability. The approach is designed for extremely small, high-dimensional datasets, enabling personalized mission-stress profiling, uncertainty estimation, and detection of integrated physiological and behavioral changes without relying on traditional group-based statistical tests. Validation on the PhysioNet MMASH dataset demonstrated a reproducible end-to-end pipeline suitable for deep-space human research.”

Project Theme: Akasi (Interactive Data Interface)

Affiliation: University of California, Irvine

Project Approach: “Akasi is intended to be a visual aid to modeling & analyzing, with interactive graphs via the Streamlit web app service. With preprocessing tools & a solid framework, further physiological data can be incorporated smoothly into Akasi, with the intention to model & analyze trends between different datasets.”

Videos detailing the winning methodologies can be found here.

Winners are also invited to attend the Human Research Program’s 2027 Investigators’ Workshop (2027 IWS), which will take place in late January 2027 in Galveston, Texas.

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NASA’s Human Research Program

NASA’s Human Research Program pursues methods and technologies to support safe, productive human space travel. Through science conducted in laboratories, ground-based analogs, commercial missions, the International Space Station and Artemis missions, the program scrutinizes how spaceflight affects human bodies and behaviors. Such research drives the program’s quest to innovate ways that keep astronauts healthy and mission ready as human space exploration expands to the Moon, Mars, and beyond.

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