Dr. Ofer Shir

Dr. Ofer Shir
Principal Investigator
Senior Lecturer, Department Head
Experimental Optimization and Scientific Informatica
Phone
972-4-7700527
Research interests:

Computational Intelligence, Experimental Optimization, Statistical Learning, Theory of Randomized Search Heuristics, Quantum ML

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. Yet, algorithmic design of intelligent experiments for optimization and learning of prescribed objectives may constitute the next level. Very little is known today about the general usefulness of optimization heuristics and statistical learning methods in laboratory experiments, about their strengths and weaknesses when compared to traditional Design-of-Experiments approaches, or about any kind of guidelines which approach to prefer, depending upon the dimensionality of the search-space, the levels of uncertainty and the number of available trials.

We are interested in learning and optimization questions related to systems within the Natural Sciences. In particular, Operational Research and Machine Learning aspects of experimental domains, that is, practical physical- and life-sciences problems whose computerized models are unavailable or too expensive to be executed, and thus necessitate real-world experiments toward the end of their global optimization.
We have specific interest in the realms of Chemistry and Bio-Molecules' Production; Our targeted family of methodologies stem from Computational Intelligence, and especially Natural Computing.

Our current activities include Experimental Combinatorial Optimization (with Dr. Noy), Benchmarking Randomized Search Heuristics (with Prof. Bäck and Dr. Doerr), Geo-Statistical Learning (with Prof. Litaor), Effective Detection of Foliage Diseases in Vineyards (with Dr. Sharon), and Deep Learning of a Complementary Ensemble of Sporadic Input Maps (with Prof. Linker and Dr. Chen).

CV

Education

2008-2010 Postdoctoral Research Associate, Princeton University, USA
2008 Ph.D., Computer Science, Leiden University, The Netherlands
2004 M.Sc., Computer Science, Leiden University, The Netherlands
2003 B.Sc., Physics and Computer Science, The Hebrew University of Jerusalem, Israel

Academic and research positions

2019- Head, Department of Computer Science, Tel-Hai College
2016- Senior Lecturer, Department of Computer Science, Tel-Hai College
2013- Principal Investigator, MIGAL-Galilee Research Institute
2012-2016, Faculty Lecturer, Department of Computer Science, Tel-Hai College
2010-2013, Research Staff Member, IBM-Research

Scientific 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

Statistical Learning in Soil Sampling Design Aided by Pareto Optimization.

Israeli, A., Emmerich, M., Litaor, M., Shir, O.M.
In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2019 ,2019, Ed. ,ACM Press New York, NY, USA, Pages 1198?1205
2019