Welcome to the UCR Computational Entomology Page
The history of humankind is intimately connected to insects. Insect borne diseases kill millions of people and destroy tens of billions of dollars worth of crops annually. At the same time beneficial insects pollinate the majority of crop species that we eat. Given the importance of insects in human affairs, it is surprising that computer science has not had a larger impact in entomology. We believe that recent advances in sensor technology will change this, and a new field of Computational Entomology will emerge. We argue that computational entomology is a sub-field of data mining, because it requires contributions from virtually every data mining technique, including classification, clustering, spatiotemporal analyses and rule discovery.
Our Mission Statement:
To assist in the transition of entomology from a data-poor to a data-rich computational science and produce actionable knowledge that will improve lives.
Eamonn Keogh: Principal Investigator. Professor of Computer Science, University of California - Riverside.
Agenor Mafra-Neto: CEO of ISCA Technologies, Riverside CA.
Gustavo E. A. P. A. Batista: University of Sao Paulo - USP.
Bing Hu: Graduate Student, Department of of Computer Science, University of California - Riverside.
Yuan Hao: Graduate Student, Department of of Computer Science, University of California - Riverside.
Learn about our contest!
We have pictures of our sensor, and the various traps ways it can be used here [PDF, PPTX].
What does the sensor data sound like? Here is a sample in a Youtube video. (sound starts at 16 seconds, you may want to turn up the volume).
Dr. Keogh's talk to the UCR Citizens University Committee on What can Computer Science do for Malaria Research? is a good high-level overview of projects goals. [PDF, PPTX, Youtube video]
Our work has receive some popular press attention, including an article in the Chronicle of Higher Education (local version).
We are using a Bayesian Classifier for this project, what is a Bayesian Classifier and why do we think it is most suitable for the task at hand?
We are committed to giving free access to all our data and code.
Our labeled data were generated using this simple utility (v1.00). It was designed to search an audio file, identify insects crossing the laser and calculate the wingbeat frequency.
We also made a few videos that explain our research:
Our publications on computational entomology.
Acknowledgments: Our research is funded by Vodafone's Wireless Innovation Project. We thank them for their incredible support.
Our early research was funded by a Bill & Melinda Gates Foundation Grand Challenge Award, a Smarter Planet Award from IBM and a UCR Chancellors Award, for which we are very grateful.
We also acknowledge all those that have donated data, ideas, insects and equipment to the project. We are particularly grateful to CadSoft for donating their incredible EAGLE software