Computational Ethics & Logic 2027
The third edition of our symposium on logic in autonomous agents. Registration is open now; the call for papers closes 30 November 2026.
North Campus Auditorium
Add to calendarAnnouncements, upcoming symposia, and highlights from the Institute for Computational Synthesis.
The third edition of our symposium on logic in autonomous agents. Registration is open now; the call for papers closes 30 November 2026.
MAR 13–14, 2027
North Campus Auditorium
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The third edition of our symposium on logic in autonomous agents. Registration is open now; the call for papers closes 30 November 2026.
North Campus Auditorium
Add to calendarInvited speakers from Intel, IBM, and leading European labs present recent advances in edge AI hardware, including results from the centre's analog testbed.
Room 402, Building 42
Add to calendarTours of the neuromorphic testbed and verification lab, plus a poster session from graduate students and a Q&A for prospective PhD applicants.
Building 42, Ground Floor
Add to calendarSeats for the Winter Seminar on 27 August 2026 are limited to the capacity of Room 402. Members save 20%.
Submissions on logic, accountability, and autonomous agents are invited until 30 November 2026, with the symposium held 13–14 March 2027.
A four-year award supports formal guarantees for autonomous fleets, jointly with two industry partners and the robotics group.
The runtime verification framework for tool-using language models was recognised among the top submissions in Oakland.
Members confirmed the 2026–2029 research plan and elected two new advisory board members at the meeting held on 21 May.
The centre's spiking-hardware facility is now bookable by partner labs following the ISCA 2026 results on energy-proportional transformers.
Keynotes and workshop sessions from the 14–15 March symposium are now available to members and registered attendees.
The fellowship recognizes her sustained contributions to safe autonomous systems and multi-agent coordination.
The collaboration will focus on energy-efficient training algorithms for sparse neural networks.