LiDAR Wind Field Reconstruction (GloBE)
Developed machine learning algorithms using Gaussian Processes to optimize LiDAR scanning patterns and reconstruct 3D wind fields for characteristic atmospheric events.
I — Selected work
Featured Projects
Research and engineering projects across wind energy, remote sensing, and computational fluid dynamics.
Wake Modeling for Large Wind Farms (SAWOP, CliF)
Wind farm flow control optimization for improved yield in offshore wind farms. Developed a flow estimation model using SCADA data from turbines.
Anomaly Detection Tool
Developed tools for outlier and trend detection using Isolation Forest and clustering algorithms for sensor data processing.
Settling Velocity Density Function of Cohesive Particles
Designed the measurement setup and data analysis procedure to estimate the particle velocity density function of deep-sea sediment samples from turbidity measurements.
II — Output
Publications
Peer-reviewed papers, conference presentations, and technical reports.
Peer-reviewed paper
Advancements in Wind Turbine Wake Modelling using 3D scanning LiDAR measurements
Journal of Physics: Conference Series · 2025
Conference presentation
Wind Direction Measurement Uncertainty Driven Analysis in a Sensor-Equipped Wind Farm
EERA DeepWind · 2023
MSc thesis
Settling Velocity Density Function of Cohesive Particles from Turbidity Measurements
EPFL · 2025
Technical report
Application of Gaussian Processes to Dual-Doppler LiDAR scanning measurements for high frequency wind field reconstruction
TNO · 2021
III — Toolkit
Technical Skills
Programming & Data
Python · MATLAB · R · SQL · Git · HPC clusters
Machine Learning
PyTorch · TensorFlow · scikit-learn · Gaussian processes · deep and convolutional neural networks
Wind Energy
Flow and wake modeling · wind farm control · SCADA, LiDAR and sensor analysis · resource assessment
Optimization & Control
Optimization algorithms · control theory · energy management · grid integration · cost analysis
IV — Domains
Research Areas
Core domains of expertise spanning atmospheric sciences, remote sensing, and machine learning.
i
Atmospheric & Flow Modeling
I work on understanding flow behavior in and around wind farms, focusing on wake dynamics, turbulence, and mesoscale effects. This links to CFD and mesoscale models such as WRF, to capture the larger patterns that drive flow predictions and turbine behavior.
ii
Remote Sensing
I develop strategies for LiDAR and ADCP data analysis. These methods aim to capture phenomena such as flow patterns, vertical stratification, blockage, jets, and shear layers at higher resolution, with direct relevance for wind turbine and wind farm operations.
iii
Machine Learning
I explore data-driven models across natural flow modeling, forecasting, and system data analysis from SCADA and other sensors. By integrating ML with physical models, I am interested in building hybrid solutions to understand and model the world.
V — Full record
Curriculum Vitae
Complete details of my education, experience, and publications.
Download CV (PDF)Last updated: January 2026
VI — Get in touch
Contact
Direct
Open to conversations about wind energy research, flow modeling, and data-driven engineering roles. The form goes straight to my inbox — my address never appears in this page’s source, so scrapers can’t harvest it.