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:

Experimental combinatorial optimization of Postharvest protocols (with Dr. Dan Gamrasni)
Automated identification of enzymes with desirable traits for agriculture use in extreme climate conditions using machine learning (with Dr. Itai Sharon and Dr. Livnat Afriat-Jurnou)
Multi-Objective Mixed-Integer Quadratic Models (with Prof. Michael Emmerich)
Benchmarking randomized search heuristics (with Dr. Carola Doerr/Sorbonne and Prof. Thomas Bäck/Leiden-U)
Effective Detection of Foliage Diseases in Vineyards (with Dr. Rakefet Sharon)

RESEARCH TEAM