I enjoy getting insights out of data using programming tools. During my PhD I’ve learned how to approach complex problems and communicate the results to both expert and non-expert audiences. I have strong data wrangling and visualization skills and typically come up with first results sooner than expected. I’m always keen to expand my technical toolkit and learn new concepts to get more angles while tackling a problem.
- R (Tidyverse adept, sparklyr, Rmarkdown, …)
- Python (Monte carlo individual based simulations, Pandas, sklearn, …)
- SQL (Impala, Oracle, MSSQL, PostgreSQL, Postgis)
- Spark (SparkSQL, streaming)
Machine learning engineer & head of ML education, Faktion
- Created the course Dimensionality reduction in Python
Sensor data analysis:
- Building condition classifiers for high throughput telemtry sensor data
- Setting up machine learning pipeline within lambda architecture
- Product recommendation engine
- Time series forecasts (ARIMA)
- Customer segmentation
- Propensity model
- Solar panel detector from aerial images (Dockerized PostgreSQL + Postgis database for aerial image preprocessing -> Microsoft Azure custom vision API)
- Winner of the 2018 Vinci Energies Hackathon detecting emergency situations from video data
- AI maturity assessment
- AI strategy development
Data Scientist, MCS solutions
Sensor data analysis:
- Combining data sets from multiple sources (Hadoop, Oracle, local files (xml, csv, json, excel…))
- Building R-packages
- Tailoring visualizations for any data set
- Building interactive dashboards
- Getting actionable insights from data and report to clients
- Setting up logging and email alerts for scheduled analyses
- Forecasting comfort conditions (CO2, temperature, humidity)
- Forecasting battery drainage
- Applying a range of machine learning techniques (Random forests, GLM, PCA, Clustering, regression, Anomaly detection,…) + Experiment with neural nets
- Vulnerability assessment of global supply chain
- Optimize office layout based on employee activities
- Geo tagging addresses and spatial analysis
- Survey analysis
PhD student, Ghent university
Building complex individual based Monte Carlo models in a spatial context (using Python) and run them on High Performance Computing infrastructure. Scientific writing and talks. Mentoring students during their Master thesis and teaching. International collaborations.
- Ghent university - PhD in eco-evolutionary dynamics, 2015
- Antwerp university - MS in evolutionary biology, 2010 (Magna Cum Laude)
- Antwerp university - BS in biology, 2008 (Cum Laude)
Jeroen Boeye, Justin M.J. Travis, Robby Stoks, Dries Bonte (2013) More rapid climate change promotes evolutionary rescue through selection for increased dispersal distance. Evolutionary Applications 2013, 6(2).
Jeroen Boeye, Alexander Kubisch, Dries Bonte (2014) Habitat structure mediates spatial segregation and therefore coexistence. Landscape Ecology 2014, 29(4).
Jasmijn Hillaert, Jeroen Boeye, Robby Stoks, Dries Bonte (2015) The evolution of thermal performance can constrain dispersal during range shifting. Journal of Biological Dynamics 2015, 9(1).
Raoul Van Damme, Katrien Wijnrocx, Jeroen Boeye, Kathleen Huyghe, Stefan Van Dongen (2015) Digit ratios in two lacertid lizards: sexual dimorphism and morphological and physiological correlates Zoomorphology 2015, 134(4).
Katrien H. P. Van Petegem, Jeroen Boeye, Robby Stoks, Dries Bonte (2016) Spatial Selection and Local Adaptation Jointly Shape Life-History Evolution during Range Expansion. Journal of Biological Dynamics 2016, 188(5).