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Experimental Optimization and Scientific Informatica

Scientific Informatics; Experimental Optimization; Natural Computing; Statistical Learning; Computational Intelligence in Physical Sciences; Quantum Control; Quantum Information

Dr. Ofer Shir

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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).




2008-2010 Postdoctoral Research Associate, Chemistry Department, 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

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

Selected Publications

On the Covariance-Hessian Relation in Evolution Strategies.

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

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

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

Statistical Learning in Soil Sampling Design Aided by Pareto Optimization.

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

On the Statistical Learning Ability of Evolution Strategies

In Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms FOGA-2017 ,2017, ACM Press Ed. ,ACM Press New York, NY, US, Pages 127–138 Shir, O.M., Yehudayoff, A. Read online

Searching for the Pareto frontier in multi-objective protein design.

Biophysical Reviews 2017 Volume 9 Issue 4 Pages 339—344 Nanda, V., Belure, S.V., Shir, O.M. Read online

Solving Structures of Pigment-Protein Complexes as Inverse Optimization Problems using Decomposition.

In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2017, ,2017, Ed. ,ACM Press New York, NY, USA,, Pages 1169–1176 Lahav, Y., Shir, O.M., Noy, D. Read online

Exploring the complexity of quantum control optimization trajectories.

Physical Chemistry Chemical Physics 2015 Volume 17 Issue 1 Pages 334—347 Nanduri, A., Shir, O.M., Donovan, A., Ho, T.-S., Rabitz, H. Read online

Efficient retrieval of landscape Hessian: Forced optimal covariance adaptive learning

Physical Review E 2014 Volume 89(6) 063306 Shir, O.M., Roslund, J., Whitley, D., Rabitz, H. Read online

Quantum Control Experiments as a Testbed for Evolutionary Multi-Objective Algorithms

Genetic Programming and Evolvable Machines 2012 Volume 13 Issue 4 Pages 445—491 Shir, O.M., Roslund, J., Leghtas, Z., Rabitz, H. Read online

Niching in Evolutionary Algorithms

Handbook of Natural Computing: Theory, Experiments, and Applications 2012 Pages 1035—1069 Shir, O.M. Read online

Adaptive Niche-Radii and Niche-Shapes Approaches for Niching with the CMA-ES

Evolutionary Computation 2010 Volume 18 Issue 1 Shir, O.M., Emmerich, M., Bäck, T. Read online

Accelerated Optimization and Automated Discovery with Covariance Matrix Adaptation for Experimental Quantum Control

Physical Review A (Atomic, Molecular, and Optical Physics) 2009 Volume 80 Issue 4 Pages 043415 Roslund, J., Shir, O.M., Bäck, T., Rabitz, H. Read online

On the Diversity of Multiple Optimal Controls for Quantum Systems

Journal of Physics B: Atomic, Molecular and Optical Physics 2008 Volume 41 Issue 7 Shir, O.M., Beltrani, V., Bäck, T., Rabitz, H., Vrakking, M.J. Read online


Post-graduate and post-doctoral research training


Laboratories and research projects


Business opportunities and technology transfer