I am most interested in using the newest advances in engineering (i.e. underwater camera systems for studying plankton) and data analysis (including deep learning and machine learning) to study the ocean and plankton ecology. In our current projects at the Hatfield Marine Science Center Plankton Ecology Lab we collect hundreds of millions to several billions of plankton images over a few weeks, using the ISIIS imager. While these billions of images have to be analyzed and automatically identified using vast computational resources which in itself is at the cutting edge of data science and oceanography, these new types of data (i.e., plankton distributions at centimeters to 1 m vertical resolution and over undulating transects of up to 100 km) give us the ability to tackle many ecological questions from new angles, and also open up completely new possibilities. One of my favorite things to do apart from just playing with those data, is visualizing it. For that I use a combination of ggplot2 in R, plot.ly as well as Grafana.
For my PhD I worked on plankton ecology in the Canadian Arctic. Based on images from the LOKI underwater imaging system I developed an automatic identification model, utilizing machine learning. I then used automatically identified data to study diel vertical migration, seasonal vertical migration, individual lipid content of copepods, and how these features are coupled.
Prior to studying at Université Laval for my PhD I worked at the University of Alaska Fairbanks under Prof. Falk Huettmann where I started to develop my interest for machine learning algorithms and applying them to ecological questions. I started working on predicting mossy habitats on Haida Gwaii, BC, Canada which are invaluable for marbled murrelets (Brachyramphus marmoratus) as nesting sites. I then went on to write my actual M.Sc. thesis on predicting vertical and horizontal zooplankton distribution into the future. This was accomplished by analyzing a vast pan-Arctic dataset of physical oceanography parameters provided by Prof. Igor Polyakov and predicting oceanographic layers such as the mixed layer depth into the future. Using those layers as well as future data from the Canadian Earth System Model (CanESM2) RCP85 on chlorophyll concentration and similar I predicted various zooplankton features (e.g. preferred depth distribution, presence/absence) into the year 2100. For the modeling, the machine learning algorithms RandomForest and TreeNet were used.
I am generally interested in sea- and landscapes and how man-made disturbances affect them.
Below you can see a 2018 presentation on one of our projects as well as some other examples of past and present research.