August 26, 2015: Congratulations to UIC Computer Science faculty Brian Ziebart (PI) and Tanya Berger-Wolf (co-PI) on new $554K NSF grant

Congratulations to UIC Computer Science Assistant Professor Brian Ziebart (PI) and Associate Professor Tanya Berger-Wolf (co-PI) on receiving a new 3-year NSF grant starting September 1 for $554,348 entitled ?III: Medium: Collaborative Research: Computational tools for extracting individual, dyadic, and network behavior from remotely sensed data.?

This is part of an overall collaborative effort with Meg Crofoot of UC Davis, with combined funding totaling $956,285.


Recent technological advances in location tracking, video and photo capture, accelerometers, and other mobile sensors provide massive amounts of low-level data on the behavior of animals and humans. Analysis of this data can teach us much about individual and group behavior, but analytical techniques that lead to insight about that behavior are still in their infancy. In particular, these new data can provide an unprecedented window into the lives of wild animals, augmenting the traditional time-consuming first-hand observations from field biologists. Unfortunately, the interpretation of low-level (i.e., unprocessed) data from animal-borne electronic sensors still poses a significant bottleneck in leveraging all of the available data to better understand the individual, pairwise, and group behavior of animal populations. This project will develop tools for scaling the expert knowledge needed to interpret high-level behaviors from low-level sensor data using tools from statistical machine learning and network analysis. These data and analytical tools promise to fundamentally change our understanding why animals do what they do, at high resolution and across multiple scales, from individuals to entire populations. The results of the project will be applicable in many settings where massive sensor data is overwhelming traditional insight derived from observational approaches. As part of the project, unique data on primate behavior that will bridge the low-level data and expert knowledge will be collected at Mpala Research Centre, Kenya. Undergraduate, graduate, and postdoctoral students from computer science and animal behavior will collaborate across continental and disciplinary boundaries.

The technical aims of this project include developing structured prediction methods that improve behavior recognition at multiple levels (individual, pair-wise, and group), using network properties to improve the identification of group activities, and advancing active learning in the structured prediction setting so that ?expensive? expert knowledge and supplemental data collection will be judiciously utilized for maximum benefit in learning behavior recognition models. Recognizing animal behavior from low-level sensor data is hierarchical in this approach, with individual activities recognized directly from data and the context of these data, the inferred individual activities informing pair-wise behavior recognition, and inferred pair-wise behavior informing group-level activity recognition. The benefits of improving the accuracy of individual and pair-wise behavior for recognizing group-level behavior will enable expert annotations to be requested that improve behavior recognition the most across all levels. These advances will enable field-biologists to investigate new hypotheses about fundamental evolutionary, ecological, and population processes at scale without the burdens of complete manual annotation of collected data. The methods will be applicable beyond field biology to understanding the hierarchy of behavior from individual entities to groups, from humans to cells, in scientific, educational, and business contexts. The team will leverage the interdisciplinary and international nature of the project to continue its ongoing work to increase participation of women and minorities in STEM research at undergraduate and graduate levels.

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