Academic Notice · Autumn 2026

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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

  1. The keynote will synthesize findings from the BBQ Autonomous Systems & Formal Verification Lab.
  2. The talk presents the latest integration of differentiable SMT solvers with real-time robotic hardware.

Keynote Announcement

Warsaw, August 15, 2026Dr 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.

Related Literature & Publications

Peer-Reviewed Journal · 2024Open Access

The FAIR Guiding Principles for Scientific Data Management and Stewardship

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.