I currently lead research and development for perception and modeling at Aescape.
As a Postdoctoral Researcher in the GRASP Laboratory at the University of Pennsylvania, I worked to advance our understanding of machine perception and animal locomotion. I created deep learning and computer vision tools for multi-view animal pose estimation, tracking, and ReID with Marc Schmidt and Kostas Daniilidis.
I also worked as a Postdoctoral Scholar at UC Davis with Stacey Combes to investigate the biomechanics and behavior of maneuvering flight and obstacle avoidance in bees. I performed experiments to measure bees’ preferences and flight performance when presented with choices between open, windy, and cluttered environments, and I developed deep learning algorithms for tracking 3D wing and body motion over hundreds of thousands of frames of high speed video.
![](https://www.ocf.berkeley.edu/~badger/wp-content/uploads/2016/06/tracks.gif)
![](https://www.ocf.berkeley.edu/~badger/wp-content/uploads/2016/06/keypoints.gif)
During my PhD in Integrative Biology at UC Berkeley, where I was advised by Robert Dudley, I used hummingbirds as a model system to understand how locomotion through cluttered environments influences flight morphology, behavior, and performance. By examining the relationships between flight maneuvers, learning, and whole-organism performance metrics, I worked to extract principles that will lead to new ecological and evolutionary hypotheses and to implement these principles to improve the locomotion capabilities of bio-inspired robots in cluttered environments. My graduate work at UC Berkeley was funded by a Graduate Research Fellowship from the National Science Foundation and a CiBER IGERT Traineeship, and was recently featured in National Geographic Magazine.
![](https://www.ocf.berkeley.edu/~badger/wp-content/uploads/2021/04/NatGeo_Anand_Varma_MM8350_161120_33629-Edit.jpg)
Throughout my research I have developed computational tools to facilitate high throughput data collection and analysis. I have implemented techniques from deep learning and computer vision to automatically track and analyze hummingbird and bee motion in 3D using multi-camera high speed video.
Contact: marc.badger@gmail.com
![Marc_winter](https://www.ocf.berkeley.edu/~badger/wp-content/uploads/2016/06/Marc_winter-292x300.jpg)