06.11.2025
Industry Pitch Talks Recap
With the Stadtwerke München (SWM)
On 4 November 2025, Stadtwerke München (SWM) welcomed representatives from the Munich Center for Machine Learning (MCML) for a focused session of Industry Pitchtalks. The event highlighted the growing synergy between academic research and real-world applications of artificial intelligence in energy, utilities, and process analytics.
The afternoon began with Gabriel Tavares (MCML) presenting “Leveraging Explainability for Business Processes.” His talk introduced a framework for interpretable process-variant analysis that combines global and local explanation techniques to reveal behavioural patterns hidden within large-scale business data.
Next, Denis Bytschkow (SWM) offered an in-depth look at “AI Forecasting for EV Charging Solutions and Virtual Power Plants.” He demonstrated how SWM is using AI-based forecasting to optimise electric-vehicle charging infrastructure and integrate virtual power plants into sustainable energy management systems.
Nefta Kanilmaz (MCML) then explored innovative methods for making sense of complex workflows in “Untangling Process Chaos — Leveraging Trace Clustering for Process Analysis.” Her presentation focused on advanced trace-clustering techniques that identify recurring structures within process data, helping to transform apparent operational chaos into meaningful insight.
The session concluded with an informal networking reception, giving researchers and practitioners the opportunity to exchange ideas, discuss potential collaborations, and reflect on the shared challenges of applying AI responsibly in high-stakes industrial environments.
This event demonstrated how research-driven AI can drive tangible operational improvements in energy and infrastructure systems. It underscored the growing importance of explainability, process transparency, and adaptive forecasting in managing complex data ecosystems. For MCML members, it offered valuable exposure to real-world industry use cases; for SWM, it opened new pathways for applied research partnerships and innovation.
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