Computational Intelligence

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.

Overview

We are primarily interested in Operational Research and Machine Learning aspects of experimental domains, that is, algorithmic design for practical physical- and life-sciences problems whose computerized models are unavailable or too expensive to be executed, and thus require real-world measurements when applying search/learning.

We foresee Computational Intelligence algorithms as facilitators that will assist experimental scientists in achieving optimal behavior of their systems and in identifying targeted phenomena. Our long-term research plan is to establish algorithmically-guided discovery tools for bio-systems. A primary pathway, adhering to this plan, is actively to devise specific algorithms for experimental optimization and learning of specific bio-systems.

Enlisted below are ongoing research projects at the Shir group:

Benchmarking Quantum Computers on Combinatorial Optimization Problems (with Alberto Moraglio)
Experimental Combinatorial Optimization of Postharvest Technology (Dan Gamrasni)
Mixed-Integer Evolution Strategies (with Thomas Bäck and Michael Emmerich)

RESEARCH TEAM