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Chen ChenPhD StudentAdviser: Malcolm A. MacIver

How does animal sample its environment efficiently? We often seen animal wiggle their sensors around the target of interest during hunting or tracking behavior. The nature of such movements have been shown to relate sensory acquisition, nonetheless, the theory of the underlying mechanism by which such wiggle behavioral pattern is derived is under-developed. My main research focus is to apply information theoretic frameworks and Bayesian learning to provide a new candidate phenomenological model for the wiggle. Under the lens of information theory, we have found that wiggle behaviors enhances the robustness of information harvesting, especially under weak sensory signals or high uncertainty. Furthermore, systematic analysis on the energetic cost of tracking movement reveals a regime of information-energy trade-off - the wiggle movement demands more mechanical energy to execute but in-turn trades more information about the target of interest that ultimately lead to improved tracking accuracy which implies higher fitness ethologically speaking, e.g. higher success rate in pray capture.

Chen Chen's CV

Other Interests

  • Virtual Reality and Augmented Reality
  • Computer Vision
  • Game Engine and Computer Graphics

B.E. Biomedical Engineering, Zhengzhou University, 2013

ME 224 (Fall 2018) - Experimental Engineering: Learning Python with Embedded System and Jupyter Notebook

DSGN 395 - Data as Art (jointly held by Northwestern University Segal Design Institute and the Art Institute of Chicago )

BME 301 (Fall 2015) - Systems Physiology - Nervous System

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