We are interested in learning and optimization questions related to systems within the Natural Sciences. Our group is especially interested in Machine/Statistical Learning and Global Optimization aspects of experimental domains, that is, practical physical-, life- and agro-sciences search-problems whose simulated models are unavailable and thus require real-world experimentation.
On the Behavior of the Mixed-Integer SMS-EMOA on Box-Constrained Quadratic Bi-Objective Models
Perspective: Towards AI Research Agents in the Experimental Sciences
Saliency Can Be All You Need In Contrastive Self-Supervised Learning
Toward an ImageNet Library of Functions for Global Optimization Benchmarking
arXiv preprint (2022)
Research Group Leader
Prof. Ofer Shir