Date(s) - 10/11/2019
11:00 am - 12:00 pm


3910 15th Ave NE
Seattle WA

Please join us for a special guest seminar by Callin Switzer titled “Learning and decision-making in animals and machines”.

The seminar will take place on Friday, October 11th from 11:00 AM – 12:00 PM in the Seminar Room of the WRF Data Science Studio, 6th floor Physics/Astronomy Tower.

Photo of Callin SwitzerFor some tasks, animals outperform machines; other times, machines outperform animals. In my research program, I aim to merge the best of both the animal and machine learning worlds. In this presentation, I will first give an overview of how I use data science and machine learning methods to accelerate the process of and increase the quality of research in the field of animal behavior. Emerging applications of data technologies are helping to redefine rigor, reproducibility and replicability within the life sciences. Second, I will explain how I use biology to inspire developments in machine learning. In particular, I will tell a story about the biologically inspired pruning of a deep neural network that is used in the context of insect flight control. Last, I will highlight my future research plans and interests. This is an exciting time to be a data scientist and a biologist. In the next five years, I believe that data technologies will bring huge improvements in quality and reproducibility within the life sciences. Additionally, by using biological inspiration we may be able to bring machine learning tools closer and closer to animal-level performance.

Callin Switzer received a PhD in animal behavior and biomechanics as well as a MA in statistics from Harvard University. Callin currently holds an innovation in data science postdoctoral fellowship at the University of Washington and is hosted by the eScience Institute and Department of Biology. His work focuses on the intersection of animal learning and data science technologies – using animal learning to inspire developments in machine learning, and data science technologies to automatically run experiments to investigate animal learning.