I'm a data scientist focused on data-driven decision support, multiobjective optimization, and explainable AI. My work combines advanced computational modeling with real-world decision needs in domains such as forestry, sustainability, and manufacturing.
Lead Use Case: Forest Biodiversity Digital Twin (EU BioDT)
Open Source: Contributor to DESDEO – interactive decision support framework
Recognition: Google Prize for Outstanding Paper (IEEE CITREx 2025)
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Forest Biodiversity Digital Twin – BioDT Use Case
Predictive modeling workflows for forest planning using LANDIS-II, HMSC, and spatial ecological data. Built for EU BioDT and deployed on LUMI supercomputer. -
DESDEO Framework
Open-source platform for interactive multiobjective optimization and explainable decision support tools. -
Explainable Optimization – KKT-LIME
Award-winning method combining Karush–Kuhn–Tucker (KKT) optimality with LIME to explain interactive multiobjective optimization.
Received the Google Prize for Outstanding Paper (IEEE CITREx 2025)
- R, Python, Git, Jupyter
- LANDIS-II, HMSC, QGIS, LUMI HPC
- Predictive modeling, XAI, optimization, reproducible workflows
- Spatial analytics, stakeholder-driven AI
