Understanding how animals move and decide.

I’m Jake, a research scientist studying animal behavior through computation, statistics, and theory.

My research focuses on modeling and theory, using computational models, Bayesian statistics, and machine learning to understand animal behavior.

I collaborate with neuroscientists, biologists, physicists, and engineers, connecting theory with data from experiments and observations in the lab and in the wild.

The goal is to build AI systems that go beyond prediction: testing hypotheses, revealing mechanisms, and turning computational insights into scientific understanding.

Explore my work Google Scholar View my CV

Jake Graving outdoors — **Jake Graving**Research Scientist
Max Planck Institute of Animal Behavior CASCB, University of Konstanz Department of Biology, University of Konstanz
Konstanz, Germany

Selected research

Experiments, models, and methods for understanding
behavior, across scales and species.

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Collective behavior 2025

How a swarm moves

Testing how individual decisions give rise to collective motion in locusts.

Explore project

This study revealed that locust swarming behavior emerges from internal decision-making processes rather than alignment with nearby individuals. I collaborated with colleagues to analyze behavioral data from controlled lab and immersive virtual reality experiments, developing a Bayesian model to test competing mechanistic hypotheses of collective motion. Our results challenged classical alignment-based theories, showing that group coordination emerges from individual decision-making informed by neural representations of neighboring locusts.

Links

Publication · Perspective · Feature Article · Model Code

Citation

Sayin, S., Couzin-Fuchs, E., Petelski, I., Günzel, Y., Salahshour, M., Lee, C.-Y., Graving, J.M., Li, L., Deussen, O., Couzin, I.D., et al. (2025). The behavioral mechanisms governing collective motion in swarming locusts. Science, 387(6737), 995–1000. https://doi.org/10.1126/science.adq7832

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Computer vision · Field research 2023

A new view of life in the wild

Using drones and computer vision to reconstruct animal movement and environmental context.

Explore project

This work introduced a drone- and computer vision–based system for high-resolution tracking of animal groups in the wild. I helped build and refine a scalable pipeline that combines drone video with deep learning and computer vision algorithms to detect, track, and reconstruct 3D movement and environmental context for wild animals. Working with an interdisciplinary team, we enabled large-scale, non-invasive behavioral analysis in natural settings — advancing tools for behavioral ecology and conservation.

Links

Code on GitHub · Publication · Press Release · Feature Article · HerdHover Project

Citation

Koger, B., Deshpande, A., Kerby, J.T., Graving, J.M., Costelloe, B.R., & Couzin, I.D. (2023). Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision. Journal of Animal Ecology, 92(7), 1498–1515. https://doi.org/10.1111/1365-2656.13904

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Other projects

Research / 2016–2025

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Collective behavior 2020

The mechanics of moving together

How schooling fish synchronize their movements to benefit from the flow around them.

Explore project

This paper identified a schooling strategy where fish synchronize their tailbeats to exploit hydrodynamic advantages, matching behavior previously seen in bio-inspired robots. I contributed to the analysis pipeline, applying deep learning–based pose estimation to track fish movement and test predictions from robotic models. The findings showed that real fish use similar vortex-phase matching strategies to gain energetic benefits during coordinated swimming.

Links

Publication · Press Release

Citation

Li, L., Nagy, M., Graving, J.M., Bak-Coleman, J., Xie, G., & Couzin, I.D. (2020). Vortex phase matching as a strategy for schooling in robots and in fish. Nature Communications, 11, Article 5408. https://doi.org/10.1038/s41467-020-19086-0

Animal pose estimation 2019

Measuring animal posture

Tracking body landmarks across species with deep learning, using DeepPoseKit.

Explore project

DeepPoseKit is an open-source software toolkit for fast, accurate animal pose estimation using a deep learning–based, multi-scale model. I led the development of DeepPoseKit to make deep learning–based posture tracking more accessible and efficient for behavioral researchers. The toolkit supports fast, accurate pose estimation across species — even with small training datasets — and has been adopted by labs working in both lab and field contexts.

Links

Code on GitHub · Publication · Feature Article

Featured in

Quanta Magazine · Nature Methods · Nature News & Views

Citation

Graving, J.M., Chae, D., Naik, H., Li, L., Koger, B., Costelloe, B.R., Couzin, I.D. (2019). DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. eLife, 8, e47994. https://doi.org/10.7554/eLife.47994

Behavioral tracking 2018

Following individuals, understanding groups

Computer vision and lightweight barcodes for tracking the lives and social networks of birds.

Explore project

This system uses barcode-equipped backpacks to enable automated, individual-level tracking of birds for behavioral research. As part of a collaborative project, I helped design and implement the computer vision pipeline used to detect and track barcoded zebra finches. The system provides continuous identity, position, and orientation data, supporting long-term studies of behavior and social networks at high resolution.

Links

Publication

Citation

Alarcón-Nieto, G., Graving, J.M., Klarevas-Irby, J.A., Maldonado-Chaparro, A.A., Mueller, I., & Farine, D.R. (2018). An automated barcode tracking system for behavioural studies in birds. Methods in Ecology and Evolution, 9(6), 1536–1547. https://doi.org/10.1111/2041-210X.13005

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Sensory ecology 2016–2017

Finding a way in the dark

Exploring how whip spiders navigate using touch and smell.

Explore project

Our studies revealed that whip spiders use tactile and olfactory cues — not vision — to navigate complex environments with impressive spatial precision. I led experimental design and data collection across both lab and field settings, collaborating with colleagues to combine radio telemetry, behavioral tracking, and sensory manipulations. Together, we showed that antenniform legs provide essential multisensory input for goal-directed navigation in the absence of visual cues.

Links

Journal of Experimental Biology (2017) · Journal of Comparative Physiology A (2017) · Frontiers in Behavioral Neuroscience (2016)

Citations

Graving, J.M., Bingman, V.P., Hebets, E.A., & Wiegmann, D.D. (2017). Development of site fidelity in the nocturnal amblypygid, Phrynus marginemaculatus. Journal of Comparative Physiology A, 203, 313–328. https://doi.org/10.1007/s00359-017-1169-5

Bingman, V.P., Graving, J.M., Hebets, E.A., & Wiegmann, D.D. (2017). Importance of the antenniform legs, but not vision, for homing by the neotropical whip spider Paraphrynus laevifrons. Journal of Experimental Biology, 220, 885–890. https://doi.org/10.1242/jeb.149823

Wiegmann, D.D., Hebets, E.A., Gronenberg, W., Graving, J.M., & Bingman, V.P. (2016). Amblypygids: Model organisms for the study of arthropod navigation mechanisms in complex environments? Frontiers in Behavioral Neuroscience, 10:47. https://doi.org/10.3389/fnbeh.2016.00047