Jannatul Ferdous, G. Matthew Fricke, Judy Cannon, and Melanie Moses
Scientific Reports 15.1 (2025)
We ask how mammal size across species affects the speed of the adaptive immune response. We find that immune response time is scale free (a human seems to respond just as fast as a mouse despite having a much slower metabolism) and suggest biological mechanisms for why that is. One mechanism is that first-contact time between immune cells decreases superlinearly with the number of cells.
Christopher P. Kempes, Michael Lachmann, M. Redwan Chowdhury, Sara I. Walker, Andrew Iannaccone, G. Matthew Fricke
, and Leroy Cronin
Nature Complexity 2 (1), 27 (2025) (PDF hosted here embargoed until May 2026)
This paper places Assembly Theory, a method of measuring the complexity of molecular objects and therefore their likelihood as a biosignature, in context with existing complexity measures. We show that Assembly Theory is disctinct from previous measures and include a proof that it is NP-Complete.
J. Jake Nichol, Michael Weylandt, G. Matthew Fricke, Melanie E. Moses, Diana Bull, Laura P. Swiler.
JGR Machine Learning and Computation (2025)
We introduce a novel method for discovering causal relationships in space–time observational data, a problem that is especially difficult in Earth system science. Our approach, Causal Space-Time Stencil Learning (CaStLe), uses local patterns in space and time to make causal discovery tractable in high-dimensional, strongly correlated datasets. We show that CaStLe can recover known atmospheric causal structures, enabling more interpretable cause-and-effect analysis in complex Earth systems.
Tracy Marie Wenzl, Ruben D. Lopez-Parra, Vanessa Svihla, Tito Busani, G. Matthew Fricke
ASEE Annual Conference (2025)
This paper uses social network analysis to examine how students in engineering and computer science teams perceive psychological safety and interpersonal closeness. By comparing stable versus shifting team structures, the work identifies patterns in networks that relate to team experience and suggests directions for instructional practice that support effective collaboration.
Janatul Ferdous, G. Matthew Fricke, and Melanie Moses.
Journal of Computational Biology (2024)
We study how the number of searchers affects the time it takes for the first one to find a target. Using simple theory and simulations, we show that first contact time decreases strongly as population size increases, largely independent of the details of how individuals search. The result provides a general explanation for why larger populations, from immune cells to foraging animals, tend to find targets faster.
Tobias Fischer et al.
Bulletin of Volcanology (2024)
We measure CO₂ emissions during the 2023 Litli Hrtur eruption in Iceland using carbon-13 isotopes to track magma degassing. We show how the isotopic signal evolves over the course of the eruption and use it to quantify CO₂ release. This provides a clearer picture of how volcanic degassing unfolds in real time and improves estimates of volcanic carbon emissions.
John Ericksen et al.
Atmospheric Measurement Techniques (2024)
We infer eruption depth from CO₂ isotope ratios using drone-based sample return missions in volcanic plumes during active eruptions. The results show that drone-based measurements provide a safe and effective way to quantify volcanic carbon emissions. This work represents the first drone-based sample return mission within a CO₂ plume.
Roya Nasimi, Fernando Moreu, and G. Matthew Fricke.
Sensors (2023)
We describe how small sensor-equipped unmanned aerial systems (UAS) can be used to inspect bridges without physical contact. We show how the aircraft and onboard sensors were deployed in real field tests to collect structural data safely and efficiently. The work demonstrates that drones can provide detailed condition information while avoiding the safety risks and costs of traditional inspection methods.
John Ericksen et al.
Frontiers in Control Engineering (2022)
we develop and test a fleet of cooperating UAVs that fly in coordinated patterns to map CO₂ emissions at the Valles Calldera Supervolcano. We show how simple flocking rules and real-time planning let the drones cover large, complex plumes and terrain efficiently. The results demonstrate that multi-robot aerial surveys can collect high-resolution volcanic gas data in environments that are otherwise inaccessible.
Hillary H. Smith et al.
Life 11, 498 (2021)
we explore why the transition from nonliving chemistry to living systems is not a clear binary but a gradual, “gray” continuum. By examining a range of proposed origin-of-life pathways and comparing them through the lens of informational and structural complexity, we argue that life’s emergence involves overlapping stages rather than a single defining moment. The work helps clarify how we think about life’s beginnings and suggests practical ways to assess candidate prebiotic systems in the lab and in planetary environments.
Nichol, J. Jake; Matthew G. Peterson; Kara J. Peterson; G. Matthew Fricke; Melanie E. Moses.
Journal of Computational and Applied Mathematics (2021)
we apply machine learning feature analysis to compare outputs from E3SM climate models with observed climate change. We show which model features drive key differences from observations and identify systematic biases in how the models represent climate processes. The results help clarify where state-of-the-art climate models succeed and where they diverge from reality, offering guidance for improving future model development.
Emily J. Liu et al.
Science Advances 6(44), eabb9103 (2020)
we deploy drones and aerial platforms to collect volcanic gas measurements at volcanoes that are too dangerous or inaccessible for people to enter. We show that coordinated airborne surveys can resolve high-resolution gas concentration and flux patterns that ground methods miss. The work demonstrates that aerial strategies substantially expand our ability to quantify volcanic degassing, improving both hazard assessment and our understanding of volcano–atmosphere interactions.
Qi Lu, G. Matthew Fricke, John C. Ericksen, and Melanie E. Moses.
Current Robotics Reports (2020)
we review research on swarm foraging, comparing theoretical results with implementations in robots and other systems. We highlight where idealized algorithms work well in theory but face challenges in real-world environments, and we discuss strategies for making swarm search more robust in practice. The review helps bridge the gap between mathematical models of collective search and fieldable robotic systems.
Rotem Botvinik-Nezer et al.
Nature (2020)
In this paper dozens of research teams independently analyzed the same functional MRI dataset to see how much their results would vary. We show that different analysis choices lead to substantial differences in statistical outcomes, even when teams use broadly accepted methods. The work highlights how flexible analytical decisions can affect scientific conclusions and underscores the need for transparency and robustness in data analysis.
Cannon, Judy; Melanie E. Moses; Janie R. Byrum; Paulus Mrass; G. Matthew Fricke; Humayra Tasnim.
Biophysical Journal 116 (2019): 322a
We model how T cells move through tissues during an immune response to understand what factors influence their search for antigen. Using simulations grounded in measured movement statistics, we show how tissue structure and cell behavior shape effective search paths. The results give insight into how immune cells balance random exploration with directed motion to find targets efficiently in complex biological environments.
Tasnim, Humayra et al.
Frontiers in Immunology 9 (2018)
we quantify how naïve T cells physically associate with dendritic cells, fibroblastic reticular cells (FRCs), and blood vessels inside lymph nodes. Using detailed microscopy and image analysis, we map how these interactions vary across lymph node regions. The results help clarify the spatial context in which immune cells encounter antigens and support models of how search and signaling unfold in real tissue.
Paulus Mrass et al.
Nature Communications (2017)
We investigate how the enzyme ROCK influences the way T cells move through inflamed lung tissue. We show that ROCK activity controls an intermittent, stop-and-go migration mode that helps T cells navigate complex tissue environments. The results reveal a key molecular regulator of immune cell movement during inflammation and help explain how T cells balance exploration and persistence in real tissues.
Fricke, G. Matthew et al.
PLoS Computational Biology 12(3): e1004818 (2016)
We use computational models to explore how T cells balance broad exploration with detailed searching during an immune response. We show that T cells can adapt their movement patterns to persist in promising regions while still covering new ground efficiently. The results provide a framework for understanding how immune cells optimize search strategies in complex tissues.
G. Matthew Fricke, Joshua Hecker, Judy Cannon, and Melanie Moses.
Robotica 34(8) (2016): 1791–1810
We draw on immune cell behavior to design search strategies for robot swarms operating in complex, fractal-like environments. We show that Lévy-style movement patterns interact with environmental structure to improve search efficiency, especially when targets are sparse and unevenly distributed. The results explain when heavy-tailed search steps help or hurt and connect immune-inspired search to the geometry of real-world spaces.
T. P. Flanagan et al.
PLoS ONE (2012)
We study how the size of an ant colony and the spatial distribution of food affect how quickly ants find and collect resources. By combining field observations with quantitative models, we show that larger colonies and clustered food patches lead to faster and more efficient foraging. The results help explain how social insects adjust collective search to different environmental conditions.
Bin Hu; G. Matthew Fricke; James R. Faeder; Richard G. Posner; William S. Hlavacek.
Bioinformatics (2009)
We introduce GetBonNie, a software tool for constructing, analyzing, and sharing rule-based biochemical models without needing to write code by hand. The tool makes it easier to visualize complex reaction networks and to collaborate on model development. The result helps researchers build and explore mechanistic models of molecular systems more efficiently.
G. Matthew Fricke; James L. Thomas.
Biophysical Chemistry 119(2) (2006): 205–211
we use Monte Carlo simulations to explore how interactions between cell membranes can drive receptors to cluster together. We show that simple physical forces between membranes can produce aggregation patterns similar to those seen in real cells. The results help explain how membrane organization influences signaling without requiring specific biochemical drivers.
John Ericksen et al.
IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) (2024)
we develop a boundary-tracing algorithm for unmanned aircraft and demonstrate it on real flight hardware operating in volcanic plumes. The work explicitly links a theoretically motivated sensing strategy to the constraints of physical vehicles, noisy sensors, and turbulent environments. The result shows how abstract plume-tracking algorithms can be made robust enough to work in the real world, not just in simulation.
Qi Lu, G. Matthew Fricke, Takaya Tsuno, and Melanie E. Moses.
IEEE/RSJ International Conference on Intelligent Robots and Systems (2020)
we compare a deterministic search pattern with a bio inspired stochastic foraging strategy based on ant behavior, using both simulation and physical robot experiments. We show how the stochastic strategy is more robust to environmental noise and uncertainty than the deterministic approach, especially when conditions differ from ideal assumptions. The results highlight the importance of testing theoretical swarm algorithms on real hardware to understand how they perform in the physical world.
Ericksen John et al.
IEEE Robotics and Computing Conference (IRC) (2020)
we introduce LoCUS, a fault-tolerant tree data structure that lets teams of aerial robots coordinate volcanic surveys despite communication failures and the loss of multiple drones. The structure maps naturally onto the physical layout of the survey region, so failures degrade coverage gracefully rather than catastrophically. The work also frames the swarm itself as a unit of intelligence and action, not just a collection of individual vehicles.
Abhinav Aggarwal et al.
IEEE/RSJ International Conference on Intelligent Robots and Systems (2019)
we define the price of ignorance, a theoretical metric that quantifies how much performance is lost when a central-place foraging algorithm have unreliable target information. We then prove upper bounds on complete collection time for several algorithm families and show how factors like site fidelity and search overlap influence efficiency. The work provides formal insight into how ignorance affects distributed foraging performance and explains trends seen in prior swarm search implementatons.
Lu, Qi et al.
IEEE/RSJ International Conference on Robotics and Automation (2019)
We compare two swarm foraging algorithms, an ant-inspired stochastic foraging strategy and a deterministic spiral search, in both simulation and on real robots operating in outdoor arenas. We show that the deterministic spiral performs best in idealized simulation, but the ant-inspired strategy is more robust to physical noise, obstacles, and real-world errors. The results highlight how physical reality can change algorithm rankings and underscore the importance of testing swarm strategies on real hardware as well as in simulation.
Aggarwal et al.
PODC (2019)
We introduce the price of ignorance, a theoretical measure of how much performance is lost when foraging algorithms lack knowledge of target locations. We show analytically how site fidelity and reduced search overlap improve collection time, clarifying the algorithmic factors that govern efficiency in distributed foraging systems.
Fricke et al.
IROS (2016)
we introduce a distributed spiral search algorithm that guarantees complete coverage of a search area by a swarm without central coordination. The algorithm lets each agent follow a deterministic spiral pattern while sharing minimal information, so the team systematically explores space without gaps. The work provides a link between provable coverage guarantees and practical swarm implementations, making it easier to reason about performance in both simulation and real settings.
Fricke et al.
GECCO (2015)
we examine how adaptive search strategies differ from purely random search in both robot swarms and immune T cells. We focus on identifying behavioral signatures that distinguish genuinely adaptive search from randomness, rather than assuming that complex motion automatically implies adaptation. The work helps clarify what it really means for a biological or robotic system to be “adaptive” when searching in uncertain environments.
Fricke et al.
ECAL (2013)
we translate patterns of T cell movement observed in biological tissues into search algorithms for robotic swarms. We show how movement statistics like pause–run behavior can be mapped onto simple control rules for microcontroller-based robots. The results demonstrate that insights from immune cell motion can inform practical designs for distributed search in engineered systems.
Flanagan et al.
IEEE SSCI – ALIFE (2011)
we examine how ant colonies convert information into food through collective foraging. We show that efficiency does not come from individual ants making better decisions, but from mass storage and amplifying information in the environment itself, primarily through pheromone trails. The work reframes foraging as an information-processing problem and highlights how collective memory and feedback drive robust group performance.
Claiborne et al.
UNM-NASA PURSUE Conference (2000)
we describe a robot control architecture in which high-level behavior emerges from interactions among simple components rather than being explicitly programmed. We show how representations of space and task-relevant features arise naturally as the system operates, rather than being built in by hand. The results illustrate how embodied control architectures can produce adaptive behavior through emergence rather than top-down design.
S. Gipson Rankin et al.
we respond to a proposed HUD rule on how the Fair Housing Act’s disparate impact standard should be implemented. We explain practical concerns about how the rule would affect enforcement and fairness, drawing on legal and policy analysis to highlight potential unintended consequences. The document offers constructive feedback aimed at improving clarity and equity in how housing discrimination claims are evaluated.
V. Meadows et al.
we summarize the outcomes of the Biosignatures Standards of Evidence Workshop, which brought together scientists to define what constitutes credible evidence for life detection. We outline the community’s consensus on standards, evaluation criteria, and gaps that need to be addressed to make life-detection claims robust and comparable. The document helps guide future research and mission planning by clarifying how evidence for biosignatures should be assessed across disciplines.
Suderman, Ryan; G. Matthew Fricke; William S. Hlavacek.
In Modeling Biomolecular Site Dynamics, Methods in Molecular Biology vol. 1945
In this chapter we demonstrate how RuleBuilder can be used to graphically define and visualize BioNetGen-language patterns and reaction rules. By guiding users through interactive examples, we show how RuleBuilder simplifies the construction and interpretation of complex rule-based biochemical models. The work makes mechanistic modeling more accessible and helps researchers build, debug, and share models of molecular systems more effectively.
Moses, Flanagan, Letendre, and Fricke.
In Handbook of Human Computation, Springer (2014)
we explore how ant colonies solve distributed problems using simple local interactions and environmental feedback, and we use them as a model for human and computational systems. We show how collective behaviors like pheromone deposition and recruitment can be viewed as forms of distributed information processing. The work highlights principles from social insects that can inform the design of human and computer systems that compute without central control.
I am a Research Associate Professor of Computer Science at the
University of New Mexico, where I study computation in complex decentralised systems like supercomputing clusters, robot swarms,
social insects, and immune systems. I also have an interest in the ethics of artificial systems and complexity measures as biosignatures.
The robotics and computational biology research is funded by the Moses Biological Computation Lab.
Through the Center for Advanced Research Computing I collaborate with scientists from a variety of disciplines
to scale up their computations to supercomputers. I enjoy teaching whenever I can and mentoring students.
