Spark Sessions 005

Spark Sessions 005

The Spark Sessions are a recurring event of the Data Science Initiative at the Faculty of Computer and Information Science. They are designed to facilitate the exchange of ideas and inspiration. The event consists of several snappy (5-7 min) presentations, followed by a casual gathering with refreshments:

1. A new and improved traffic model for Slovenia - leveraging mobile positioning data by Leon Hvastja (Data scientist, Medius)

Are we able to replace classic survey-based traffic models using mobile data? Mobile telecommunication logs generate vast datasets on user positions, offering a powerful resource for modeling daily movement patterns. However, effectively harnessing this data presents significant challenges, including complex noise reduction, the absence of a ground truth for validation, and the need for computationally efficient algorithms to handle the sheer volume of data.

2. Sunsei: 5-Day Solar Forecasts, Powered by a Novel Regional AI Model - NWPsolarNet by Marko Rus (Data scientist, Medius)

NWPsolarNet revolutionizes solar forecasting, moving beyond localized, data-intensive methods. This innovative model, trained on over 300 diverse sites, provides accurate 5-day forecasts for virtually any location, currently focused on Slovenia, without needing historical site-specific data. We'll detail NWPsolarNet's technical aspects and introduce Sunsei, a public tool offering powerful forecasting for solar energy optimization.

3. Lessons from Real-World Automated Grading and Simulation Systems by Frenk Dragar (Founder, Trivial Group)

In this talk, we’ll explore practical lessons from deploying automated grading and simulation systems in real-world educational settings. How do grader bias, incomplete data, and student feedback shape our system development? How to make automated feedback not only accurate and scalable, but also constructive and fair? We’ll share insights from production-ready interactive learning experiences.

4. AI Real-Time Feedback in Sports by Maja Kolar (Data scientist, Valira AI)

Traditional gym training lacks immediate, objective feedback on exercise technique and performance. We developed a real-time computer vision system that tracks multiple athletes simultaneously, automatically recognizes exercises, counts repetitions, and provides instant technique corrections during workouts. In this talk, we will present the core challenges of building the end-to-end pipeline while maintaining real-time performance on live video streams.

5. Snacks and drinks and discussions

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