Optimal decision making
Exploring how movement, beliefs, and spatial geometry shape animal decisions.
In collaboration with Iain Couzin · MPI-AB / University of Konstanz.
Hi, I’m Jake
I’m a research scientist working at the intersection of natural and artificial intelligence, data science, and animal behavior.
My research focuses on modeling and theory, using computational methods like Bayesian statistics, causal inference, and machine learning to understand animal behavior. A central focus of my work is building AI systems that go beyond prediction — tools that help test hypotheses, reveal mechanisms, and support scientific understanding — with the goal of turning computational insights into scientific discoveries.
My current work explores decision making as probabilistic inference: how animals use uncertain information to weigh alternatives and select actions. I study these questions through mathematical models and comparisons with experimental and observational data. An interactive model of spatial decision making illustrates part of this work.
My projects have spanned a broad range of topics, from how individual decisions give rise to collective motion and how animals navigate using sensory information, to using deep learning and computer vision to measure behavior in the lab and in the wild. Across these topics, I develop and adapt computational approaches to fit the scientific question. I collaborate with neuroscientists, biologists, physicists, and engineers, contributing across the research process, from shaping questions and designing experiments to developing methods and interpreting results. Current and past projects are described below.
I completed my PhD with Iain Couzin at the Max Planck Institute of Animal Behavior and the University of Konstanz. Before that, I completed my MSc with Dan Wiegmann and Vern Bingman at BGSU.
Max Planck Institute of Animal Behavior
CASCB, University of Konstanz
Department of Biology, University of Konstanz
Konstanz, Germany
Making sense of direction
Developing Bayesian circular statistics to model directional data and quantify uncertainty in animal behavior.
In collaboration with James Foster · University of Konstanz.
Movement decisions in the wild
Studying how Grévy’s and plains zebras combine terrain, social context, and internal state when choosing where to move.
In collaboration with Blair Costelloe · Mara Elephant Project.
Inside the honeybee nest
Modeling how brood and food stores are arranged across three-dimensional nests as colonies develop.
In collaboration with Michael Smith · Auburn University.
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How a swarm moves
Testing how individual decisions give rise to collective motion in locusts.
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
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Tracking groups with drones
Reconstructing individual trajectories and environmental context with computer vision.
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
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The mechanics of moving together
How fish coordinate their tailbeats to exploit the vortices produced by their neighbors.
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
Measuring animal posture
Tracking body landmarks across species with deep learning, using DeepPoseKit.
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
Tracking birds’ social lives
Using lightweight barcodes and computer vision to map individual movements and social networks.
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
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Finding a way in the dark
Exploring how whip spiders navigate using touch and smell.
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)