Projects & Research

Open Source Projects

pyaerocom

Python Tools for Climate and Air Quality Model Evaluation

Contributed to this comprehensive Python package for processing, analyzing, and visualizing aerosol and air quality model data. Serves as the backend for the Aeroval evaluation portal and is used operationally by the Copernicus Atmosphere Monitoring Service (CAMS).

• Tech Stack: Python, xarray, pandas, cartopy
• Contributions: Data fusion algorithms, model evaluation metrics, uncertainty quantification
• Impact: Powers operational air quality evaluation portal, used by European forecasting services

DeepTreeMRA

Scalable Spatial Modeling for Massive Datasets

High-performance implementation of the Multi-Resolution Approximation (MRA) for spatial statistical modeling. Designed to handle hundreds-of-millions of observations using deep tree structures and parallel computing.

• Tech Stack: MATLAB, MPI, High-Performance Computing
• Key Features: 75% computation time reduction, scalable to 100M+ observations
• Applications: Climate data analysis, environmental modeling, large-scale spatial prediction

optimparallel

Parallel Optimization for Python

Python package providing a parallel computing interface to the L-BFGS-B optimizer, enabling efficient optimization of complex objective functions.

• Tech Stack: Python, NumPy, parallel computing
• Use Cases: Parameter estimation, model fitting, optimization problems

Professional Work

Statkraft (Current)

Developing data-driven systems for energy management and trading operations. Focus areas include:

• Large-scale weather and hydrological data pipelines
• Operational analytics and decision support systems

Norwegian Meteorological Institute

Developed machine learning-based air pollution forecasting models and contributed to the Copernicus Atmosphere Monitoring Service (CAMS):

• ML models for air quality prediction
• Data fusion techniques for air quality modeling
• Model evaluation and uncertainty quantification

NCAR Research

Created scalable algorithms for massive spatial datasets:

• Parallel computing methods reducing computation by 75%
• Analysis of satellite data (100M+ observations)
• Statistical modeling for climate and environmental data

Technical Interests

• Quantitative Modeling: Time series analysis, forecasting, optimization
• Machine Learning: Deep learning, ensemble methods, statistical learning
• High-Performance Computing: Parallel algorithms, distributed systems
• Data Engineering: Large-scale data pipelines, real-time analytics