Jake Graving — Curriculum vitae

Jake GravingResearch Scientist

Max Planck Institute of Animal Behavior CASCB, University of Konstanz Department of Biology, University of Konstanz

Konstanz, Germany

Overview

Scientist and engineer with expertise in mathematical modeling, Bayesian inference, machine learning, and computer vision. I develop theory, methods, and software to understand complex systems, working with interdisciplinary teams to connect models with data. My research focuses on animal behavior, with broader applications in data science and AI. Across this work, I aim to turn computational insights into scientific discoveries.

Experience

2020-present

Research Scientist

Advanced Research Technology Unit, Max Planck Institute of Animal Behavior

Responsibilities

Lead independent research at the interface of machine learning and behavioral science, developing general-purpose tools to measure and model animal behavior in both lab and field environments.
Design and maintain scalable ETL pipelines for multimodal behavioral datasets, ensuring data integrity and reproducibility across experimental systems.
Collaborate with interdisciplinary teams - including software engineers, neuroscientists, and field biologists - to build AI-driven tools for behavior analysis and experimental optimization.
Apply Bayesian inference, causal modeling, and uncertainty quantification to large-scale time series and experimental data to generate interpretable, data-driven insights.
Provide technical guidance and mentorship across collaborative projects, promoting reproducible science and statistical rigor.

2015-2020

Doctoral Researcher

Department of Collective Behaviour, Max Planck Institute of Animal Behavior & University of Konstanz

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Developed computer vision and deep learning methods for measuring and modeling animal behavior, including DeepPoseKit for animal pose estimation.
Studied how sensory information and internal state shape the collective behavior of animal groups.

2013-2015

Graduate Research Assistant

Department of Biological Sciences, Bowling Green State University

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Conducted research on homing and sensory navigation in nocturnal whip spiders.

2011-2013

Undergraduate Research Assistant

Department of Biological Sciences, Bowling Green State University

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Studied navigation and spatial learning in whip spiders with Daniel Wiegmann, and the sensory basis of rheotaxis in fish with Sheryl Coombs (2013).

Education

2021

Dr.rer.nat., Biology

Max Planck Institute of Animal Behavior & University of Konstanz - Germany

Summa cum laude · 0.0 — highest possible grade

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International Max Planck Research School (IMPRS) · Supervisor: Iain Couzin

Thesis: Computer Vision and Deep Learning Methods for Measuring and Modeling Animal Behavior

2015

M.Sc., Biology

Bowling Green State University - USA

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Supervisor: Daniel Wiegmann; co-supervisor: Verner Bingman

Focus: Animal Behavior, Neuroscience

2013

B.Sc., Biology

Bowling Green State University - USA

Publications

12 selected works

AcceptedJournal of Comparative Physiology A

High intensity alters spectral orientation in dancing honeybees

Authors, contributions & citation

Foster, J.J., Graving, J.M., Pfeiffer, K. (2026).

Citation

Foster, J.J., Graving, J.M., Pfeiffer, K. (2026). High intensity alters spectral orientation in dancing honeybees. Journal of Comparative Physiology A. Accepted.

@article{foster2026spectralOrientation,
  title={High intensity alters spectral orientation in dancing honeybees},
  author={Foster, James J. and Graving, Jacob M. and Pfeiffer, Keram},
  journal={Journal of Comparative Physiology A},
  year={2026},
  note={Accepted}
}

In PrepManuscript in preparation

Unwrapping circular statistics with Bayesian multilevel models

Authors, contributions & citation

Graving, J.M., Sayin, S., Costelloe, B.R., Goldshtein, A., and Foster, J.J. (in prep).

Introduces a novel Bayesian generalized linear model framework for circular data by incorporating the von Mises distribution.
Jointly models mean direction and variance to improve interpretability of circular outcomes.
Provides practical guidelines and implementations using Bayesian toolkits such as PyMC and Stan.

Citation

Graving, J.M., Sayin, S., Costelloe, B.R., Goldshtein, A., and Foster, J.J. (in prep). Unwrapping circular statistics with Bayesian multilevel models.

@unpublished{gravingCircularMultilevel,
  title={Unwrapping circular statistics with Bayesian multilevel models},
  author={Graving, Jacob M. and Sayin, Sercan and Costelloe, Blair R. and Goldshtein, Aya and Foster, James J.},
  note={Manuscript in preparation}
}

In ReviewCurrent Biology

Morphogenesis, differentiation, and robust multi-scale organization of the honeybee nest revealed by a novel 4D atlas

Authors, contributions & citation

Marting, P.R., P. Prathibha, Koger, B., Rogers, S.R., Graving, J.M., Smith, M.L. (in review).

Developed autoregressive modeling methodology, software, statistical analysis, and visualizations of honeybee nest organization.

Citation

Marting, P.R., P. Prathibha, Koger, B., Rogers, S.R., Graving, J.M., Smith, M.L. (in review). Morphogenesis, differentiation, and robust multi-scale organization of the honeybee nest revealed by a novel 4D atlas. Current Biology. In review.

@unpublished{martingHoneybeeNestAtlas,
  title={Morphogenesis, differentiation, and robust multi-scale organization of the honeybee nest revealed by a novel 4D atlas},
  author={Marting, Peter R. and {P. Prathibha} and Koger, Ben and Rogers, Stephanie R. and Graving, Jacob M. and Smith, Michael L.},
  note={In review at Current Biology}
}

In ReviewCurrent Biology

Collective detection and processing of distributed information by fish schools

Authors, contributions & citation

Bath, D.E., Graving, J.M., Walter, T., Sridhar, V.H., Vizcaíno, J.P., Couzin, I.D.

Code: github.com/jgraving/bayesian_beta_regression

Led development of a Bayesian model to analyze collective information processing in fish schools.
Modeled 3 billion behavioral observations across 400+ conditions using JAX and NumPyro.
Revealed how distributed sensing shapes group-level responses to environmental signals.

Citation

Bath, D.E., Graving, J.M., Walter, T., Sridhar, V.H., Vizcaíno, J.P., Couzin, I.D. Collective detection and processing of distributed information by fish schools. In revision for Current Biology.

@unpublished{bathCollectiveDetection,
  title={Collective detection and processing of distributed information by fish schools},
  author={Bath, D. E. and Graving, J. M. and Walter, T. and Sridhar, V. H. and Vizcaíno, J. P. and Couzin, I. D.},
  note={Manuscript in revision for Current Biology}
}

Code

2025Science

The behavioral mechanisms governing collective motion in swarming locusts

Authors, contributions & citation

Sayin, S., Couzin-Fuchs, E., Petelski, I., Günzel, Y., Salahshour, M., Lee, C.-Y., Graving, J.M., Li, L., Deussen, O., Sword, G.A., Couzin, I.D. (2025).

Press: Science Perspective

Led Bayesian modeling and statistical analysis of VR behavioral data from locusts.
Identified and solved a key methodological flaw in prior work - confounding of group coordination with group size.
Helped validate the core findings and strengthen the paper’s quantitative rigor.

Citation

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

@article{sayin2025behavioral,
  title={{The behavioral mechanisms governing collective motion in swarming locusts}},
  author={Sayin, Sercan and Couzin-Fuchs, Einat and Petelski, Inga and G{\"u}nzel, Yannick and Salahshour, Mohammad and Lee, Chi-Yu and Graving, Jacob M and Li, Liang and Deussen, Oliver and Sword, Gregory A and Couzin, Iain D},
  journal={Science},
  volume={387},
  number={6737},
  pages={995--1000},
  year={2025},
  publisher={American Association for the Advancement of Science (AAAS)},
  doi={10.1126/science.adq7832}
}

Paper

2023Journal of Animal Ecology

Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision

Authors, contributions & citation

Koger, B., Deshpande, A., Kerby, J.T., Graving, J.M., Costelloe, B. R., Couzin, I.D. (2023).

Press: Orange Hello Future

Code: github.com/benkoger/overhead-video-worked-examples

Co-developed a computer vision pipeline combining drone footage and deep learning to extract 3D behavioral and environmental data from animal groups in natural settings.
Integrated spatial context reconstruction with fine-scale movement tracking to support large-scale behavioral ecology studies.

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. doi:10.1111/1365-2656.13904

@article{koger2023quantifying,
  title={Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision},
  author={Koger, Benjamin and Deshpande, Adwait and Kerby, Jeffrey T and Graving, Jacob M and Costelloe, Blair R and Couzin, Iain D},
  journal={Journal of Animal Ecology},
  volume={92},
  number={7},
  pages={1357--1371},
  year={2023},
  publisher={Wiley Online Library}
}

Paper Code

2020Nature Communications

Vortex phase matching as a strategy for schooling in robots and in fish

Authors, contributions & citation

Li, L., Nagy, M., Graving, J.M., Bak-Coleman, J., Guangming X., Couzin, I.D. (2020).

Press: Physics World, SciShow (Hank Green)

Analyzed high-resolution posture data of schooling fish to test energy-optimization strategies predicted by robotic models.
Applied deep learning-based pose estimation and mechanistic modeling to quantify fluid-mediated interactions and coordinated motion patterns.

Citation

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

@article{li2020vortex,
  title={{Vortex phase matching as a strategy for schooling in robots and in fish}},
  author={Li, Liang and Nagy, M{\'a}t{\'e} and Graving, Jacob M and Bak-Coleman, Joseph and Xie, Guangming and Couzin, Iain D},
  journal={Nature Communications},
  volume={11},
  number={1},
  pages={5408},
  year={2020},
  publisher={Springer Science and Business Media LLC},
  doi={10.1038/s41467-020-19086-0}
}

Paper

2019eLife

DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning

Authors, contributions & citation

Graving, J.M., Chae, D., Naik, H., Li, L., Koger, B., Costelloe, B.R., Couzin, I.D. (2019).

Press: Quanta Magazine, Nature Methods, Nature

Code: github.com/jgraving/deepposekit

Created a general-purpose, few-shot deep learning framework for high-speed, high-accuracy animal pose tracking in Python/TensorFlow.
Supervised development of key components including a custom annotation GUI and augmentation tools for low-data regimes.

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. doi:10.7554/elife.47994

@article{graving2019deepposekit,
  title={{DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning}},
  author={Graving, Jacob M and Chae, Daniel and Naik, Hemal and Li, Liang and Koger, Benjamin and Costelloe, Blair R and Couzin, Iain D},
  journal={eLife},
  volume={8},
  pages={e47994},
  year={2019},
  publisher={eLife Sciences Publications, Ltd},
  doi={10.7554/elife.47994}
}

Paper Code

2018Methods in Ecology and Evolution

An automated barcode tracking system for behavioural studies in birds

Authors, contributions & citation

Alarcón-Nieto, G., Graving, J.M., Klarevas-Irby, J.A., Maldonado-Chaparro, A.A., Mueller, I., Farine, D.R. (2018).

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. doi:10.1111/2041-210x.13005

@article{alarcon2018automated,
  title={{An automated barcode tracking system for behavioural studies in birds}},
  author={Alarc{\'o}n-Nieto, Gustavo and Graving, Jacob M and Klarevas-Irby, James A and Maldonado-Chaparro, Adriana A and Mueller, Inge and Farine, Damien R},
  journal={Methods in Ecology and Evolution},
  volume={9},
  number={6},
  pages={1536--1547},
  year={2018},
  publisher={Wiley},
  doi={10.1111/2041-210x.13005}
}

Paper

2017Journal of Comparative Physiology A

Development of site fidelity in the nocturnal amblypygid, Phrynus marginemaculatus

Authors, contributions & citation

Graving, J.M., Bingman, V.P., Hebets, E.A., Wiegmann, D.D. (2017).

Citation

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(5), 313-328. doi:10.1007/s00359-017-1169-5

@article{graving2017development,
  title={{Development of site fidelity in the nocturnal amblypygid, Phrynus marginemaculatus}},
  author={Graving, Jacob M and Bingman, Verner P and Hebets, Eileen A and Wiegmann, Daniel D},
  journal={Journal of Comparative Physiology A},
  volume={203},
  number={5},
  pages={313--328},
  year={2017},
  publisher={Springer Science and Business Media LLC},
  doi={10.1007/s00359-017-1169-5}
}

Paper

2017Journal of Experimental Biology

Importance of the antenniform legs, but not vision, for homing by the neotropical whip spider Paraphrynus laevifrons

Authors, contributions & citation

Bingman, V.P., Graving, J.M., Hebets, E.A., Wiegmann, D.D. (2017).

Press: Discover

Citation

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(5), 885-890. doi:10.1242/jeb.149823

@article{bingman2017importance,
  title={{Importance of the antenniform legs, but not vision, for homing by the neotropical whip spider Paraphrynus laevifrons}},
  author={Bingman, Verner P and Graving, Jacob M and Hebets, Eileen A and Wiegmann, Daniel D},
  journal={Journal of Experimental Biology},
  volume={220},
  number={5},
  pages={885--890},
  year={2017},
  publisher={The Company of Biologists},
  doi={10.1242/jeb.149823}
}

Paper

2016Frontiers in Behavioral Neuroscience

Amblypygids: model organisms for the study of arthropod navigation mechanisms in complex environments

Authors, contributions & citation

Wiegmann, D.D., Hebets, E.A., Gronenberg, W., Graving, J.M., Bingman, V.P. (2016).

Citation

@article{wiegmann2016amblypygids,
  title={{Amblypygids: model organisms for the study of arthropod navigation mechanisms in complex environments}},
  author={Wiegmann, Daniel D and Hebets, Eileen A and Gronenberg, Wulfila and Graving, Jacob M and Bingman, Verner P},
  journal={Frontiers in Behavioral Neuroscience},
  volume={10},
  pages={47},
  year={2016},
  publisher={Frontiers Media SA},
  doi={10.3389/fnbeh.2016.00047}
}

Paper

Selected talks

3 Dec 2025

Adaptive Behavior as Probabilistic Inference

Invited ScyPhy Seminar, University of Konstanz

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Seminar listing

2 Dec 2025

Adaptive Behavior as Probabilistic Inference

Invited Institute Seminar, Max Planck Institute of Animal Behavior

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Seminar announcement

2 Jul 2019

Revealing the Behavioral Algorithms of Social Animals

Invited talk, Princeton Neuroscience Institute, Princeton University

16 Mar 2018

Perception and Motion in Locust Swarms

Invited talk, Integrated Behavioral Research Group (IBRG), Princeton University

Teaching

2023

Konstanz School of Collective Behavior

University of Konstanz

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Designed and led a workshop on “Probabilistic Machine Learning” for 30+ international PhD students, covering Bayesian inference, causal inference, and information theory in practice. https://www.exc.uni-konstanz.de/kscb/schedule-2023/

2022

Deep Learning Workshop

Max Planck Institute of Animal Behavior

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Led an applied workshop introducing PyTorch and deep learning fundamentals to 15 researchers.

2019

ASAB 2019 Summer Conference

University of Konstanz

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Gave an invited seminar on “Machine Learning in the Behavioral Sciences” to an audience of 400+.
Co-organized a workshop on behavioral quantification and modeling using ML methods (27 participants).

2016-2020

University of Konstanz, Department of Biology

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Co-developed and taught an intensive research course on collective behavior for Master’s students.
Supervised five Master’s thesis projects across behavior, computation, and data science.
Delivered lectures and practicals on computer vision for behavior analysis, data processing, and Python programming.

2013-2015

Graduate teaching

Department of Biological Sciences, Bowling Green State University

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Supported teaching in biostatistics, population and community ecology, and introductory biology; gave a guest lecture on arthropod navigation.