Ken Yoda
Papers
2
Total Citations
12
H-Index
2
About
Ken Yoda is a leading researcher in animal behavior and movement ecology, with a particular focus on seabird trajectory analysis. His work bridges biology and computational science, developing machine learning methods to decode the complex movement patterns of animals in their natural environments. Yoda’s major contributions include pioneering the use of sparse subsequence pattern-based classification for comparative animal trajectory data analysis, a method that converts time series data into symbolic sequences to extract biologically meaningful insights. This work, published in 2019, has garnered 10 citations and represents a significant advance in applying robotics-inspired algorithms to ecological questions. He has also introduced travel time-dependent maximum entropy inverse reinforcement learning for predicting seabird trajectories, addressing the challenge of modeling goal-directed movement while accounting for environmental constraints. Though this 2017 paper has 2 citations, it showcases his innovative approach to trajectory prediction, a problem central to computer vision and robotics. Yoda’s research not only deepens our understanding of animal navigation and foraging behavior but also provides tools that can be adapted for comparative studies across species, making him a key figure in the emerging field of computational ethology.
Research Focus
Key Achievements
Top Papers
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- 2