I am Vsevolod Nedora, an astrophysicist turned data scientist with a passion for solving complex problems through innovative technology and collaboration. Raised in Vladivostok, Russia, my journey began with an early fascination for engineering and physics, leading me to pursue a career dedicated to understanding the universe and creating tools for scientific discovery.

During my academic career at the Max Planck Institute for Gravitational Physics, I focused on multimessenger and high-energy astrophysics, specializing in software development, data analysis, and numerical modeling. My contributions include:

• Numerical Simulations: Lead developer of PyBlastAfterglow, a numerical code for modeling gamma-ray burst (GRB) and kilonova afterglows, and the post-processing pipeline bns-ppr-tools for the general relativistic hydrodynamic code WhiskyTHC.
• Data Modeling: Statistical analysis and modeling of neutron star merger ejecta properties, culminating in a widely adopted model and dataset.
• Machine Learning: Designed a conditional variational autoencoder (CVAE) for fast inference on synthetic GRB data, laying the foundation for efficient data processing pipelines.

For more details, see my arXiv and INSPIRE profiles.

Now, I am focused on leveraging data science and machine learning to tackle real-world challenges, particularly in the energy sector. My current portfolio project is an MLOps pipeline for electricity price forecasting in Germany. Key features of this project include:

• Novelty and Impact: Forecasting energy market quantities up to a week ahead, enabling participants to optimize operations and reduce costs by proactively responding to long-term trends.
• Automation: Data collection from APIs (e.g., SMARD, openmeteo) and web scraping (e.g., epexspot) are fully automated with GitHub Actions.
• Scalability: Plans to extend the pipeline to forecast prices across multiple European countries using additional collectors (nordpool, energy-charts).
• CI/CD Integration: Hosting and deployment using GitHub Pages, showcasing real-world applications of DevOps principles.

In addition, I aim to integrate generative AI tools into the project for automated report generation, merging insights from forecasted data and external sources like news and market analyses.

I am enthusiastic about connecting with professionals and researchers who share my passion for technology, data science, and impactful projects. My journey from academia to industry has been shaped by a commitment to growth and collaboration. Whether you're interested in open-source projects, innovative forecasting techniques, or interdisciplinary discussions, I’d love to hear from you!