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Simon Garnier - New Jersey Institute of Technology. Newark, NJ, US

Simon Garnier Simon Garnier

Assistant Professor, College of Science and Liberal Arts | New Jersey Institute of Technology

Newark, NJ, UNITED STATES

Professor Garnier focuses on the study of collective behaviors and swarm intelligence in natural and artificial systems

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Biography

As principle Investigator of the Swarm Lab, Garnier performs interdisciplinary research that studies the mechanism underlying the coordination of large animal groups, such as any colonies or human crowds.

Garnier has a substantial record in academic research and scientific communication. Garnier has broad experience in all aspects of data analysis, behavioral studies and teaching. He is interested in academic and corporate work involving the analysis of behavioral data and the development of applications for the study of large scale behaviors in animal groups, collective robotics and/or human social networks.

Areas of Expertise (6)

Biological Sciences

Swarm Robotics

Interdisciplinary Education

Collective Behavior

Swarm Intelligence

Artificial Intelligence

Accomplishments (1)

DARPA Young Faculty Award

June 2019

Education (3)

Université Paul Sabatier (Toulouse III): Ph.D., Behavioral Science, Swarm Intelligence 2008

Université Paul Sabatier (Toulouse III): M.S., Neuroscience, Behavior and Cognition 2004

Université Bordeaux II: B.S., Biochemistry, Cellular Biology and Physiology 2002

Affiliations (3)

  • Swarm Lab
  • Entomological Society of America
  • The Swarm Intelligence - Editor

Languages (2)

  • English
  • French

Media Appearances (5)

Striking Down the Queen Won’t Save You From the Swarm

The New York Times  

2019-05-14

It’s a common trope in science fiction, but hives in nature are not dependent on any central node for their function. If you’ve watched any science fiction or fantasy movies in the last few decades, you’ve probably seen the following scenario play out enough times that the next paragraph shouldn’t count as a spoiler...

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How slime mold could shape the future of self-driving cars

The Verge  

2018-05-01

Here, assistant professor Simon Garnier studies decentralized systems like colonies of ants — or petri dishes filled with slime mold — to figure out how these organisms make decisions and solve problems together. That’s what’s called “swarm intelligence,” and in the future, this kind of information could help us design an algorithm to create a more efficient network of self-driving cars. If you want to learn more about slime mold, watch the video above. We joined Garnier in the lab to re-create a famous slime mold experiment, and we even tasted some right out of the petri dish. (Spoiler: it tasted like moss.) Not that we walked into his lab with the idea of eating some stinky, unicellular organism. But we had to give it a try after Garnier said: “It tastes like... have you ever licked the floor?”...

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Slime mold gives insight into intelligence of neuron-less organisms

ScienceDaily  

2016-06-08

"Working with Physarum constantly challenges our preconceived notions of the minimum biological hardware that is required for sophisticated behavior," says Simon Garnier, an assistant professor of biology at NJIT and the principal investigator of the study...

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Army ants' 'living' bridges span collective intelligence, 'swarm' robotics

ScienceDaily  

2015-11-24

The research also included Scott Powell, an army-ant expert and assistant professor of biology at George Washington University; Albert Kao, a postdoctoral fellow at Harvard who received his doctorate in ecology and evolutionary biology from Princeton in 2015; and Simon Garnier, an assistant professor of biological sciences at NJIT who studies swarm intelligence and was once a postdoctoral researcher in Couzin's lab at Princeton...

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Do robot ants dream of electric crumbs?

Los Angeles Times  

2013-03-28

Which is smarter: a swarm of brainless mini-robots with clockwork guts, or a colony of ravenous, half-blind Argentine ants? If you answered mindless robots, you’re right — but just barely. Researchers studying the problem-solving abilities of foraging ants enlisted the aid of 10 sugar-cube-sized robots to determine whether the real-life insects had to put any thought into deciding which direction they should go when they came to a fork in the road or an obstacle in their path. The answer to that question is important for the understanding of how large communities of organisms interact and coordinate their behavior.

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Event Appearances (7)

We the Swarm: lessons in problem-solving from tiny brains and neuron-less creatures

In Entomology Colloquium  University of Illinois at Urbana-Champaign

2019-02-11

Not so smart after all: How to solve problems with no neurons and too many brains

In Coffee @ Beyond  Arizona State University

2018-10-29

From Blob to Mob: my random walk through complex systems

In Collective Behavior Seminar Series  Max Planck Institute for Ornithology and Konstanz University, Germany

2018-07-19

Of robots and ants: a brief history of biological and bio-inspired self-assembly

In XXIII Simpósio de Mirmecologia: An International Ant Meeting  Curitiba, Paraná, Brazil

2017-10-25

The lab who stares at goats - Tools and toys to study the role of vocal communication in organizing collective movement

In Collective Motion 2016  Uppsala University, Uppsala, Sweden

2016-06-10

Living Architectures: Autonomous Self Assembly and Disassembly in Army Ants

In 9th EAI International Conference on Bio-inspired Information and Communications Technologies  Columbia University

2015-12-05

How Millions Become One - The Biological Principles of Swarm Intelligence

In Nonlinear Science & Mathematical Physics Seminar  Georgia Institute of Technology

2014-12-03

Research Grants (2)

No Brainer: Cognitive-like Behaviors in a Unicellular Slime Mold

National Science Foundation $339,998

2016-07-01

The proposed project will set up a comprehensive experimental and theoretical framework that can be used beyond the scope of this project to study decision-making in other neuron-less organisms and to establish comparisons with brained animals, thereby advancing our comprehension of the emergence of cognitive processes in biological systems...

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Encouraging Data Sharing and Reuse in the Field of Collective Behavior through Hackathon-Style Collaborative Workshops

National Science Foundation $24,997

2018-10-01

The investigators will bring together diverse researchers who work in the field of collective and emergent behavior. Collective and emergent behavior is the study of complex biological and social systems, ranging from bacterial colonies to human groups. The hackathon-style workshop draws researchers around identifying best practice mechanisms for sharing data, communicating methods of data analysis, and reusing publicly available data. The investigators propose a series of two workshops where teams of 2-5 participants work on a specific project during the duration of the 3-day event. An objective of the workshop is to foster novel collaborations between researchers in biology, data science, mathematics, computer science and physics, all of whom have an interest in collective behavior.

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Articles (6)

Stability and Responsiveness in a Self-Organized Living Architecture PLOS: Computational Biology

Simon Garnier, Tucker Murphy, Matthew Lutz, Edward Hurme, Simon Leblanc, Iain D. Couzin

2013-03-28

Robustness and adaptability are central to the functioning of biological systems, from gene networks to animal societies. Yet the mechanisms by which living organisms achieve both stability to perturbations and sensitivity to input are poorly understood. Here, we present an integrated study of a living architecture in which army ants interconnect their bodies to span gaps. We demonstrate that these self-assembled bridges are a highly effective means of maintaining traffic flow over unpredictable terrain. The individual-level rules responsible depend only on locally-estimated traffic intensity and the number of neighbours to which ants are attached within the structure. We employ a parameterized computational model to reveal that bridges are tuned to be maximally stable in the face of regular, periodic fluctuations in traffic. However analysis of the model also suggests that interactions among ants give rise to feedback processes that result in bridges being highly responsive to sudden interruptions in traffic. Subsequent field experiments confirm this prediction and thus the dual nature of stability and flexibility in living bridges. Our study demonstrates the importance of robust and adaptive modular architecture to efficient traffic organisation and reveals general principles regarding the regulation of form in biological self-assemblies

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Do Ants Need to Estimate the Geometrical Properties of Trail Bifurcations to Find an Efficient Route? A Swarm Robotics Test Bed PLOS: Computational Biology

Simon Garnier, Maud Combe, Christian Jost, Guy Theraulaz

2013 Interactions between individuals and the structure of their environment play a crucial role in shaping self-organized collective behaviors. Recent studies have shown that ants crossing asymmetrical bifurcations in a network of galleries tend to follow the branch that deviates the least from their incoming direction. At the collective level, the combination of this tendency and the pheromone-based recruitment results in a greater likelihood of selecting the shortest path between the colony's nest and a food source in a network containing asymmetrical bifurcations...

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Individual rules for trail pattern formation in Argentine ants (Linepithema humile) PLOS: Computational Biology

Andrea Perna, Boris Granovskiy, Simon Garnier, Stamatios Nicolis, Marjorie Labédan, Guy Theraulaz, Vincent Fourcassié, David Sumpter

2012 We studied the formation of trail patterns by Argentine ants exploring an empty arena. Using a novel imaging and analysis technique we estimated pheromone concentrations at all spatial positions in the experimental arena and at different times. Then we derived the response function of individual ants to pheromone concentrations by looking at correlations between concentrations and changes in speed or direction of the ants...

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Visual attention and the acquisition of information in human crowds Proceedings of the National Academy of Sciences

Andrew C Gallup, Joseph J Hale, David JT Sumpter, Simon Garnier, Alex Kacelnik, John R Krebs, Iain D Couzin

2012 Pedestrian crowds can form the substrate of important socially contagious behaviors, including propagation of visual attention, violence, opinions, and emotional state. However, relating individual to collective behavior is often difficult, and quantitative studies have largely used laboratory experimentation. We present two studies in which we tracked the motion and head direction of 3,325 pedestrians in natural crowds to quantify the extent, influence, and context dependence of socially transmitted visual attention. In our first study, we instructed stimulus groups of confederates within a crowd to gaze up to a single point atop of a building. Analysis of passersby shows that visual attention spreads unevenly in space and that the probability of pedestrians adopting this behavior increases as a function of stimulus group size before saturating for larger groups. We develop a model that predicts that this gaze response will lead to the transfer of visual attention between crowd members, but it is not sufficiently strong to produce a tipping point or critical mass of gaze-following that has previously been predicted for crowd dynamics. A second experiment, in which passersby were presented with two stimulus confederates performing suspicious/irregular activity, supports the predictions of our model. This experiment reveals that visual interactions between pedestrians occur primarily within a 2-m range and that gaze-copying, although relatively weak, can facilitate response to relevant stimuli. Although the above aspects of gaze-following response are reproduced robustly between experimental setups, the overall tendency to respond to a stimulus is dependent on spatial features, social context, and sex of the passerby.

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The walking behaviour of pedestrian social groups and its impact on crowd dynamics PLoS One

Mehdi Moussaïd, Niriaska Perozo, Simon Garnier, Dirk Helbing, Guy Theraulaz

2010 Human crowd motion is mainly driven by self-organized processes based on local interactions among pedestrians. While most studies of crowd behaviour consider only interactions among isolated individuals, it turns out that up to 70% of people in a crowd are actually moving in groups, such as friends, couples, or families walking together. These groups constitute medium-scale aggregated structures and their impact on crowd dynamics is still largely unknown. In this work, we analyze the motion of approximately 1500 pedestrian groups under natural condition, and show that social interactions among group members generate typical group walking patterns that influence crowd dynamics. At low density, group members tend to walk side by side, forming a line perpendicular to the walking direction. As the density increases, however, the linear walking formation is bent forward, turning it into a V-like pattern. These spatial patterns can be well described by a model based on social communication between group members. We show that the V-like walking pattern facilitates social interactions within the group, but reduces the flow because of its “non-aerodynamic” shape. Therefore, when crowd density increases, the group organization results from a trade-off between walking faster and facilitating social exchange. These insights demonstrate that crowd dynamics is not only determined by physical constraints induced by other pedestrians and the environment, but also significantly by communicative, social interactions among individuals.

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Experimental study of the behavioural mechanisms underlying self-organization in human crowds Proceedings of the Royal Society B: Biological Sciences

Mehdi Moussaïd, Dirk Helbing, Simon Garnier, Anders Johansson, Maud Combe, Guy Theraulaz

2009 In animal societies as well as in human crowds, many observed collective behaviours result from self-organized processes based on local interactions among individuals. However, models of crowd dynamics are still lacking a systematic individual-level experimental verification, and the local mechanisms underlying the formation of collective patterns are not yet known in detail. We have conducted a set of well-controlled experiments with pedestrians performing simple avoidance tasks in order to determine the laws ruling their behaviour during interactions. The analysis of the large trajectory dataset was used to compute a behavioural map that describes the average change of the direction and speed of a pedestrian for various interaction distances and angles. The experimental results reveal features of the decision process when pedestrians choose the side on which they evade, and show a side preference that is amplified by mutual interactions. The predictions of a binary interaction model based on the above findings were then compared with bidirectional flows of people recorded in a crowded street. Simulations generate two asymmetric lanes with opposite directions of motion, in quantitative agreement with our empirical observations. The knowledge of pedestrian behavioural laws is an important step ahead in the understanding of the underlying dynamics of crowd behaviour and allows for reliable predictions of collective pedestrian movements under natural conditions.

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