Experimental Optimization and Scientific Informatica

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.

Dr. Ofer Shir

Title
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 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)

Principle Researcher

Dr. Ofer Shir

Dr. Ofer Shir
Experimental Optimization and Scientific Informatica
972-4-7700527

The advent of modern laboratory and field experiments, as well as of computerized systems, enables researchers to control experiments and analyze their big-data in high-speed rates.

Team

Assaf Israeli

B.Sc. 2010, Tel-Hai College. Israel
Research assistant and M.Sc. candidate

Naama Horesh

Research assistant
B.Sc. 2017, Ben-Gurion University, Israel

Collaborations

Funding

Latest Publications

Bayesian Performance Analysis for Black-Box Optimization Benchmarking.

Calvo, B., Shir, O.M., Ceberio, J., Doerr, C., Wang, H., Bäck, T., Lozano, J.A.
In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO-2019 ,2019, Ed. ,ACM Press New York, NY, USA, Pages 1789?1797
2019

Benchmarking Discrete Optimization Heuristics with IOHprofiler.

Doerr, C., Ye, F., Horesh, N., Wang, H., Shir, O.M., Bäck, T.
In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO-2019 ,2019, Ed. ,ACM Press New York, NY, USA, Pages 1798?1806
2019

On the Covariance-Hessian Relation in Evolution Strategies.

Shir, O.M., Yehudayoff, A.
Theoretical Computer Science 2019 Volume 801 Pages 157-174
2019

Predict or Screen Your Expensive Assay? DoE vs. Surrogates in Experimental Combinatorial Optimization.

Horesh, N., Bäck, T., Shir, O.M.
In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2019 ,2019, Ed. ,ACM Press New York, NY, USA, Pages 274?284
2019