Moore-Sloan and Sackler Postdoctoral Scholar of data science and biophysics at UW, Seattle

I study biological questions that tie together ecology, biomechanics, and neurobiology into engaging narratives of natural history. At the intersection of these topics is animal behavior, and with insects comprising over a third of all animal biomass, I have made them my focus. Of particular interest to me are the fruit fly and mosquito. Fruit flies have co-habited with humans throughout our evolution, and have developed a strong desire for alcohol thanks to our addiction to brewing it. Meanwhile, mosquitoes have forever plagued humanity as vectors of disease including Malaria, Dengue and Yellow Fever, and most recently, Zika.

My research is focused on understanding the details of how insects can be so efficient at finding our fermenting fruit, and ourselves, despite their numerically limited brains. To answer these questions I build novel systems for automatic observation of insects in virtual worlds with computer controlled visual, olfactory, and thermal stimuli. This lets me answer complex ecological questions with huge, detailed, and carefully controlled datasets. By studying the behavior of these animals in detail, I hope to gain inspiration for novel methods for analyzing big multi-factorial datasets that can be applied to machine learning.

Because both flies and mosquitoes are genetic model systems, I am also able to take advantage of sophisticated genetic tools to determine where in the brain their behaviors are controlled. This unique convergence of techniques allows me to bring together ecology and neurobiology in my research.

Laboratory research can only take us so far, however. Two of my most recent projects involve taking the lab to the field, in order to study the behavior and biomechanics of the alkali flies of Mono Lake, CA, and long distance migration of Drosophila.

I am currently a Postdoctoral Scholar at the University of Washington, funded by a joint Moore-Sloan Data Science fellowship at the eScience institute, and a Sackler Scholarship in biophysics. My mentors are Jeff Riffell in the Department of Biology and Nathan Kutz in Applied Math.

Current & Past Research

Data driven modeling of insect behavior using modular and open source architectures

With a quickly growing literature on insect behavior it is challenging to understand how individual behavioral “modules”—e.g. zigzagging upwind to follow odor plumes, following visual and thermal cues, and using visual information to decelerate and land safely (see Figure below)—interact in natural environments. To organize this knowledge, I am working on bringing together control-theoretic and machine-learning tools to build data-driven open-source models of insect behavior. The methods I am developing will be equally applicable to extracting feedback control models for any type of biological, or robotic system.

To locate human hosts, mosquitoes use seven behavioral “modules”, described above. Each of these behaviors is independent in that they can occur in different orders, depending on the order in which they encounter odors, visual cues, and thermal signatures. The behaviors are, however, indirectly linked to one another through the animal’s interaction with its environment. For example, approaching visual features increases a mosquito’s chance of encountering a thermal plume. These indirect linkages lead to nonlinear interactions, making it difficult to predict what insects will do in novel environments, or how the neural circuits underlying their behavior might interact in the natural world. Graphic from my paper on how mosquitoes integrate sensory modalities.

Long distance migration of Drosophila melanogaster

Fly trap, equipped with multiple illuminated odor sources and trapping chambers, and a timelapse camera to record the time of arrival of insects.

Thirty years ago ecologist Jerry Coyne and collaborators demonstrated that flies are capable of flying over 10km; I am working on replicating these results with technologically advanced traps to find out how. Work in progress in collaboration with Kate Leitch and Michael Dickinson at Caltech.

Right: Fly trap, equipped with multiple illuminated odor sources and trapping chambers, and a time lapse camera to record the time of arrival of insects.

Optimal search with unreliable cues – CO2 attraction in Drosophila

Trajectory of a fly searching around an odor source for food. Color encodes time, starting at yellow and ending at purple.

The olfactory system in Drosophila is a key model system for studying both innate and learned behavior in animals. Within this framework, CO2 has become the canonical odorant for studying innate aversive behaviors. However, this result goes the natural intuition that a fruit fly, which is attracted to CO2-emitting fermentation processes, should in fact be attracted to CO2.

I used automated computer vision systems to collect over 50,000 fly-hours of data to finally resolve this longstanding paradox. I found that flies only exhibit aversion to CO2 in relatively unnatural conditions like those imposed by the popular T-maze assay. When flies are allowed to acclimate to their environment, and are in an active foraging state, they exhibit strong attraction to CO2. To provide confidence in our results, we repeated our experiments in four independent behavioral assays including traps, free flight studies, a landing platform, and constrained walking arenas.

Publication: van Breugel, F., Huda, A., and Dickinson, M. Drosophila have distinct activity-gated pathways that mediate attraction and aversion to CO2. (2017) BioRxiv preprint.

Diving Flies of Mono Lake

Alkali fly, safely underwater in Mono Lake, CA, in a protective air bubble.
Alkali fly, safely underwater in Mono Lake, CA, in a protective air bubble.

In late summer, the shores of Mono Lake, California, are bustling with small flies, Ephydra hians, which dive under water inside small air bubbles to feed. Despite Mark Twain’s charismatic description of them in his book Roughing It, we still do not understand how they are able to perform this entertaining and miraculous feat.

“You can hold them under water as long as you please–they do not mind it–they are only proud of it. When you let them go, they pop up to the surface as dry as a patent office report, and walk off as unconcernedly as if they had been educated especially with a view to affording instructive entertainment to man in that particular way.”  – Mark Twain, 1872

Funded by a National Geographic CRE grant, I used a combination of high speed videography, force measurements, scanning electron microscopy, and manipulations of water chemistry to figure out how these flies do it, and what makes them unique.

Publication:  van Breugel, F and Dickinson, M. Superhydrophobic diving flies (Ephydra hians) and the hypersaline waters of Mono Lake. (2017) PNAS.

PressWashington Post, National Geographic, New York TimesKPCCNew Scientist, Science, Popular ScienceReuters, Gizmodo, Mercury NewsDaily Mail, Newsweek, IFL Science


Odor Plume Tracking

Long exposure of an illuminated fly as it approaches a fermenting strawberry.
Long exposure of an illuminated fly as it approaches a fermenting strawberry.

When flies, mosquitoes, and other insects encounter an attractive odor, they turn upwind. Because odor plumes are broken apart by turbulent flows, the insect invariably exits the plume, sometimes after just a few milliseconds. This triggers zigzagging back and forth, until they re-encounter the odor plume. This strategy generally leads them close to the odor source, however, visual and other cues are necessary for the final stage of search. Read more about how flies use odors, and how mosquitoes integrate multiple sensory modalities.



van Breugel, F., Riffell, J., Fairhall, A., and Dickinson, M. H. Mosquitoes use vision to associate odor plumes with thermal targets. (2015). Current Biology.

van Breugel, F. and Dickinson, M. H. Plume-Tracking behavior of flying Drosophila emerges from a set of distinct sensory-motor reflexes. (2014). Current Biology.

Press: BBC (mosquitoes)

Research hardware

Wind tunnel equipped with 13 cameras for automated 3D tracking of flying insects.

I build my own behavioral arenas and write my own software to run experiments on freely behaving insects.

Left: Wind tunnel equipped with 13 cameras for automated 3D tracking of flying insects, and a projector for displaying dynamic visual stimuli.

Arena to examine the behavior of walking flies in response to different odors. The black cylinders are red LED's for activating neurons using red shifted channel rhodopsin.

Working with Drosophila melanogaster, a genetic model system, makes it possible to use genetic tools to probe which neural circuits are responsible for controlling different behaviors. Optogenetic tools, such as red shifted channel rhodopsin (e.g. Chrimson), allow me to use these tools on freely behaving animals. This approach makes it possible to bring together ecology and neurobiology in the laboratory.

Right: Arena to examine the behavior of walking flies in response to different odors. The black cylinders are red LED’s for optogenetic activation of neurons.


To collect data on walking flies, I wrote my own 2D real-time multi-target tracking package. This software makes it possible to record video, and trajectories, of flies over the long periods of time – 24 hours or more. It is open source, and available at:

FigureFirst. Two of my colleagues and I are actively working on a software package to make the design of scientific data figures easy, fun, and dynamic. Our open source software package bridges the gap between existing GUI programs, like Inkscape, and python plotting libraries, like MatPlotLib. More info:


Lindsay, T., Weir, P., and van Breugel, F. FigureFirst: A Layout-first Approach for Scientific Figures. (2017). Scipy 2017.

Visual control of flight and landing using optic flow

Fly_LandingUsing 3D tracking, closed loop control of high speed cameras, and genetic tools, I explored landing and the neural basis for flight speed control of Drosophila in free flight. Inspired by my results, I developed a novel algorithm for distance estimation from a single camera using nonlinear control theory.


van Breugel, F., Suver, M. P., and Dickinson, M. H. Octopaminergic modulation of the visual flight speed regulator of Drosophila(2014). J. Exp. Biol.

van Breugel, F., Morgansen, K. A., and Dickinson, M. H. Monocular distance estimation from optic flow during active landing maneuvers. (2014). Bioinspiration and Biomimetics.

van Breugel, F. and Dickinson, M. H. The visual control of landing and obstacle avoidance in the fruit fly, Drosophila melanogaster(2012). J. Exp. Biol.

Press: BBC (landing)

Bioinspired design of flapping hovering MAV’s

Screen Shot 2015-11-12 at 12.30.24 AMAs an undergraduate, I used genetic algorithms and simulations to design wing stroke patterns for flapping flight. Subsequently, I built physical flapping systems, culminating in the first passively stable flapping hovering machine (left).


van Breugel, F., Regan, W., and Lipson, H. From insects to machines. (2008). Robotics and Automation Magazine.

Regan, W., van Breugel, F., and Lipson, H. Towards evolvable hovering flight on a physical ornithopter. (2006). Alife X conference proceedings.

van Breugel, F., and Lipson, H. Evolving buildable flapping ornithopters. (2005). GECCO conference proceedings.