Nicolas Chaumont

Nicolas Chaumont
Stanford University | SU · Department of Biology

engineer, research associate

About

14
Publications
2,134
Reads
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597
Citations
Citations since 2017
0 Research Items
374 Citations
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20172018201920202021202220230204060
20172018201920202021202220230204060
Additional affiliations
July 2012 - present
Stanford University
Position
  • Software Engineer, Research Associate
August 2011 - present
Michigan State University
Position
  • Researcher
September 2004 - June 2012
Keck Graduate Institute of Applied Life Sciences
Position
  • Research Assistant
Education
August 2004 - May 2009
Claremont Graduate University
Field of study
  • Computational Mathematics and Numerical Analysis
August 2004 - February 2014
Keck Graduate Institute
Field of study
  • Computational and Systems Biology
January 2001 - August 2004
Université de Sherbrooke
Field of study
  • Computer Science (Computer Graphics & Optimization)

Publications

Publications (14)
Article
Full-text available
Significance Fresh produce has become the primary cause of foodborne illness in the United States. A widespread concern that wildlife vector foodborne pathogens onto fresh produce fields has led to strong pressure on farmers to clear noncrop vegetation surrounding their farm fields. We combined three large datasets to demonstrate that pathogen prev...
Article
Full-text available
Efforts to maximise crop yields are fuelling agricultural intensification, exacerbating the biodiversity crisis. Low-intensity agricultural practices, however, may not sacrifice yields if they support biodiversity-driven ecosystem services. We quantified the value native predators provide to farmers by consuming coffee's most damaging insect pest,...
Article
Full-text available
Artificially evolving foraging behavior in simulated legged animals has proved to be a notoriously difficult task. Here, we co-evolve the morphology and controller for virtual organisms in a three-dimensional physically realistic environment to produce goal-directed legged locomotion. We show that following and reaching multiple food sources can ev...
Data
Full-text available
Relationship between and . (PDF)
Data
The trajectory and brain states of an evolved animat at generation 14,000. At this point, the animat has acquired the capacity to maintain a direction of travel and move opposite to the direction indicated by the lateral contact sensor. Its movement with respect to the door opening is still random. The fitness at this time point is of maximal. (MP4...
Data
Full-text available
Genetic encoding of animat controllers. A: In this example, two HMGs encoded by two genes can read from and write to several of the 12 Markov variables, indexed 0–11. The top row shows the Markov variables at time that the HMGs can read from while the row below shows how the HMGs write into those variables to update their state at . B: The genome i...
Data
Full-text available
Genetic encoding of network structure and function. (PDF)
Data
Full-text available
Relationship between different forms of (PDF)
Data
This movie shows the trajectory of an evolved animat traveling through the maze after 2,000 generations of evolution in the top panel, and the inner workings of its Markov network brain in the lower panel. At this point in evolutionary history, the animat has learned to move forward whenever it stands in front of an opening, but otherwise performs...
Data
The trajectory and brain states of an evolved animat at generation 49,000. By this time, the animat has evolved the capacity to use the information provided by the door beacon by storing it in bit 9, and move purposefully in the indicated direction after emerging from the previous door. Because of its high fitness, the animat traverses the maze fiv...
Article
Full-text available
One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-def...
Article
Full-text available
We present a system that can evolve the morphology and the controller of virtual walking and block-throwing creatures (catapults) using a genetic algorithm. The system is based on Sims' work, implemented as a flexible platform with an off-the-shelf dynamics engine. Experiments aimed at evolving Sims-type walkers resulted in the emergence of various...
Article
Full-text available
We describe the re-implementation of a system to evolve the morphology and behavior of artificial creatures, originally due to Karl Sims. The new implementation is fast, flexible, and allows for the evolution of a range of novel behaviors not previously seen in Sims' or related work. In particular, we use off-the-shelf dynamics simulator engines th...
Article
Full-text available
This paper addresses the problem of generating a volumetric shape to approximate an arbitrary cloud of points. The volumetric shape is an aggregate of blobbies, computed using a genetic algorithm. The choice of this model is motivated by the simplicity and the popularity of this primitive. The algorithm is tested on clouds of points representing a...

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