AAMAS paper accepted!

Our paper on Uncoupled Learning of Differential Stackelberg Equilibria with Commitments has been accepted for publication at AAMAS. Congratulations, Robert and Mert!

Preprint is accessible here.

PhD position on reinforcement learning for controlling critical infrastructure

We are looking for a PhD candidate to work on several fundamental questions necessary for allowing AI methods to support human operators in critical infrastructure. For example, can machine learning tools be combined with conventional optimisers to improve safety and data efficiency? Can complex, structured decisions be made jointly by a human operator and an artificial agent? Can such algorithms deal with hierarchies in decision making, and how can decisions or models be explained and verified? Full details and instructions to apply can be found on this page.

New project on using AI to support human operators to control critical infrastructure

As part of consortium of 8 European countries, I will be involved in AI4REALNET (AI for real-world network operations). In this project, we will investigate how AI can be used to support decision making by human operators to improve safety and efficiency in the energy and transport sectors. AI4REALNET receives funding from the European Union’s Horizon Europe Research and Innovation programme.

Postdoc positions on AI for sustainable molecules and materials

The university of Amsterdam is looking for 4 postdocs on the topic of AI for meta-materials. One of the positions will be co-advised by me, on the topic of “Machine Learning-based models of plant protein mixtures for sustainable food design”. Two other positions are co-advised by AMLab colleagues. Deadline for applications is 16 May. Apply is only possible through the form here.
(Note that another postdoc position at the VU on learning and reasoning for medical decision making is also still open, see below).

TMLR paper accepted

David Kuric’s paper on learning re-useable options was accepted to TMLR. Paper, code, and video are available on OpenReview. It proposes a gradient-based meta learning approach to discover sub-policies that are useful for rapid adaptation to different MDPs in a family of tasks. Congrats, David!

ICLR paper accepted

Qi and Marco’s paper got accept for presentation at ICLR. Congratulations! The submitted version is available here, camera-ready version is coming soon.