Selected Publications
Ellen, J. S., Graff, C. A., and Ohman, M. D. Improving plankton image classification using context metadata.
Limnology and Oceanography - Methods (2019)439-461DOI 10.1002/lom3.10324
Ohman, M. D., Davis, R. E., Sherman, J. T., Grindley, K. R., Whitmore, B. M., Nickels, C. F., and Ellen, J. Zooglider: an autonomous vehicle for optical and acoustic sensing of zooplankton.
Limnology and Oceanography: Methods (2018): 69-86.DOI 10.1002/lom3.10301
Ellen, J., Li, H., and Ohman, M. D. (2015). Quantifying California Current Plankton Samples with Efficient Machine Learning Techniques.
Proceedings of OCEANS.15 MTS/IEEE Washington. (pp. 1-9) DOI: 10.23919/OCEANS.2015.7404607
Graff, C., and Ellen, J. (2016). Correlating Filter Diversity with Convolutional Neural Network Accuracy.
Proceedings of 15th IEEE International Conference on Machine Learning and Applications (ICMLA). (pp. 75-80) IEEE Computer Society DOI: 10.1109/ICMLA.2016.0021
(all publications)
Presentations
Ellen, J. and Ohman, M. D. (2020) In-situ Dynamics of Marine Snow: Assessment by an Autonomous Zooglider
Moving Beyond Technology: How Quantitative Image-Based Methods Help Reveal Ocean Ecology Session @ AGU/ASLO Ocean Sciences Meeting. Feb 16-21, 2020. San Diego, CA.
Ellen, J. (2019) Improving plankton image classification using context metadata.
International Workshop:Artificial Intelligence for Characterizing Plankton Traits from Images, Apr 24-26 2019. Villefranche-sur-Mer, France
Ellen, J. (2019) Where, When, and Water Conditions - Plankton Image Classification with Context Metadata.
Small Scale Spatial and Temporal Patterns in Particles, Plankton, and Other Organisms Session @ ASLO Aquatic Sciences Meeting, Feb 23-Mar 2, 2019. San Juan, PR
Ellen, J. and Ohman, M. D. (2018) One Size Fits All? Determining the Impact of Image Size on Deep Learning Classifications of Marine Zooplankton.
Machine Learning in Biological Oceanography Session @ AGU/ASLO Ocean Sciences Meeting, Feb 11-16, 2018. Portland, OR
Ellen, J. and Ohman, M. D. (2017) Leveraging Context to Improve Machine Learning Classifications of Marine Zooplankton.
New Directions In Plankton Ecology Session @ ASLO Aquatic Sciences Meeting, March 1, 2017. Honolulu, HI
Acknowledgements
I'm grateful for the support I have recieved from:
I'm also thankful for the many people who have supported me including:
- Benjamin Whitmore for Zooglider data extraction and general officemate/buddy support
- Casey Graff for year-round coding wizardry
- Ohman Lab Members (esp.
Amanda Netburn,
Cat Nickels,
Jennifer Brandon,
Laura Lilly,
Stephanie Sommer,
and Tristan Biard
) for the hours they spent sorting images and tolerating my curiosity
- Linsey Sala, PIC manager for taxonomic expertise and being the personification of my algorithm's aspirations
- Emma Tovar, Kayla Blincow, Kelsey Gilmore, Todd Langland, Jean-Baptiste Romagnan, Alison Cawood; ZooScan operators and underappreciated heros
- Students/Staff @ California Current Ecosystem LTER site for help with the little things that matter
- SIO & Southwest Fisheries Science Center CalCOFI sea-going / tech staff for collecting samples
- Shonna Dovel for PRPOOS net ops/collections
- Hongyu 'Chris' Li for helping unravel original data formats and execute experiments
- Chris Tosh for being the best cohort companion; providing a sounding board, centering influence, and comic relief
- LorRaine Duffy, Shibin Parameswaran, Aidan MacDonald, Jesse Zhou, and Kevin Dela Rosa: My professional collaborators who provided ample distraction and motivation
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