Computational Intelligence

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 protein expression systems (with Dr. Noy/Migal)
• Precision agriculture: geo-statistical learning (with Prof. Litaor/Migal)
• Benchmarking randomized search heuristics (with Dr. Doerr/Sorbonne and Prof. Bäck/Leiden-U)
• Effective Detection of Foliage Diseases in Vineyards (with Dr. Sharon/Migal)
• Deep Learning of a Complementary Ensemble of Sporadic Input Maps (with Prof. Linker/Technion-IIT and Dr. Chen/Migal)