Welcome to the UCR Computational Entomology Page

Our Motivation:

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.

Personnel:

Eamonn Keogh: Principal Investigator. Professor of Computer Science, University of California - Riverside.

Yanping Chen:  Graduate Student, Department of 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.

Christian R. Shelton, Associate Professor of Computer Science, University of California - Riverside.

 Selected Papers on Computational Entomology by our Team:

• Yanping Chen, Adena Why, Gustavo Batista, Agenor Mafra-Neto, Eamonn Keogh. Flying Insect Classification with Inexpensive Sensors. Journal of Insect Behaviour 2014. [pdf] [project page].
• Yanping Chen , Adena Why, Gustavo Batista, Agenor Mafra-Neto, Eamonn Keogh. Flying Insect Detection and Classification with Inexpensive Sensors. Journal of Visualized Experiments 2014 (Accepted, in Press).
• Gustavo Batista, Eamonn Keogh and Agenor Mafra Neto (2011) Sensors and Software to allow Computational Entomology, an Emerging Application of Data Mining. SIGKDD 2011 Demo Paper.
• Y. Rheem, K. Spencer, L. Silva, D. Zeni, C. Bernardi, L. Stoltman, R. O. D. Silva, R. Borges, L. E. J. Mafra, E. Keogh, A. Mafra-Neto (2011). Rapid data collection technologies: Zumba Mosquito TrapTM and hand-held nanosensory. ISID-NTD International Meeting on Neglected Tropical Diseases. Boston, Massachusetts, USA, July 8-10, 2011.
• Gustavo Batista, Eamonn Keogh and Agenor Mafra Neto (2011). Counting and classifying mosquitoes from a distance with ultra cheap sensors. American Mosquito Control Association 77th conference.
• Gustavo Batista, Bilson Campana, Eamonn Keogh: Classification of Live Moths Combining Texture, Color and Shape Primitives. ICMLA 2010: 903-906

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).

• If you want our entire unedited data collection we will need to ship you a DVD or USB stick, contact Dr. Keogh with your shipping details. 
• If you are an entomologist, you may be interested in our growing library of circadian rhythms. See also Dr. Brian Taylor's page. 

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