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Dr inż. Karolina Nowak to Deliver Keynote at European Conference on Machine Intelligence (ECMI 2026)
Associate Professor Karolina Nowak has been invited to deliver a keynote address on Sound Neuro-Symbolic Model Checking at ECMI 2026 in Munich.
Category
NEWS
Date
2026-08-15
Location / Venue
Warsaw / Munich
Announced By
BBQ Press & Communications Office
Overview & Background
The organizing committee of the 14th European Conference on Machine Intelligence (ECMI 2026) announced the keynote program featuring Dr inż. Karolina Nowak.
Key Highlights & Facts
Keynote Speaker: Dr inż. Karolina Nowak (BBQ Institute)
Conference: 14th European Conference on Machine Intelligence (ECMI 2026, Munich, Germany)
Keynote Title: Bridging the Continuous-Discrete Gap: Sound Verification for Neural Controllers in Safety-Critical Robotics
Event Program & Proceedings
The keynote will synthesize findings from the BBQ Autonomous Systems & Formal Verification Lab.
The talk presents the latest integration of differentiable SMT solvers with real-time robotic hardware.
Keynote Announcement
Warsaw, August 15, 2026 — Dr inż. Karolina Nowak, Associate Professor and Head of the Autonomous Systems & Formal Verification Lab at BBQ Institute, has been invited to present a keynote lecture at the 14th European Conference on Machine Intelligence (ECMI 2026) to be held in Munich, Germany this September.
Her talk, titled “Bridging the Continuous-Discrete Gap: Sound Verification for Neural Controllers in Safety-Critical Robotics,” will showcase novel algorithms that allow engineers to compute tight mathematical safety boundaries around deep reinforcement learning policies controlling unmanned aerial vehicles and autonomous ground rovers.
Registration & Logistics
Conference dates: September 14–18, 2026.
Pre-print of the keynote paper available on the BBQ publications archive.
Mark D. Wilkinson, Michel Dumontier, Tomasz Wiśniewski, Mateusz Wójcik, Barend Mons
Scientific Data (Nature Springer) Vol. 11(1), pp. 18-34(2024). DOI: 10.1038/sdata.2016.18
This foundational work establishes actionable principles ensuring that digital research objects—including datasets, algorithms, and computational workflows—are Findable, Accessible, Interoperable, and Reusable (FAIR) for both humans and automated computational agents.