faculty member

I am interested in learning and optimization questions related to systems within the Natural Sciences. Specifically, my fields of interest encompass Statistical Learning in Theory and in Practice, Experimental Optimization, Theory of Randomized Search Heuristics, Scientific Informatics, Natural Computing, Computational Intelligence in Physical Sciences, Quantum Control and Quantum Information. 

Currently:
* Associate Professor, Computer Science Department, Tel-Hai College.
* Principal Investigator, Computational Sciences, Migal-The Galilee Research Institute.

Previously:
* Senior Lecturer, Computer Science Department, Tel-Hai College.
* Research Scientist, IBM-Research.
* Postdoctoral Research Associate, Princeton University. Host: Hersch Rabitz.
* MSc and PhD in Computer Science, Leiden University. Advisors: Thomas Bäck and Marc Vrakking.
* BSc in Physics and Computer Science, Hebrew University of Jerusalem.

research @ Migal : https://www.migal.org.il/en/computational-intelligence 

• The C++ Programming Language (121503; Fall)
• Introduction to Computational Intelligence (199811; Fall)
• Advanced Topics in Object-Oriented Programming (199414; Spring)

2004-2008: FOM PhD Scholarship, AMOLF / Leiden University, The Netherlands

SELECTED PEER-REVIEWED PUBLICATIONS

Shir, O.M., Yehudayoff, A.: On the covariance-Hessian relation in evolution strategies. Theoretical Computer Science 801(2020) 157—174

Doerr, C., Ye, F., Horesh, N., Wang, H., Shir, O.M., Bäck, T.: Benchmarking Discrete Optimization Heuristics with IOHprofiler. Applied Soft Computing 88 (2020) 106027

Horesh, N., Bäck, T., Shir, O.M.: Predict or Screen Your Expensive Assay? DoE vs. Surrogates in Experimental Combinatorial Optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2019, New York, NY, USA, ACM Press (2019) 274–284

Israeli, A., Emmerich, M., Litaor, M., Shir, O.M.: Statistical Learning in Soil Sampling Design Aided by Pareto Optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2019, New York, NY, USA, ACM Press (2019) 1198–1205

Calvo, B., Shir, O.M., Ceberio, J., Doerr, C., Wang, H., Bäck, T., Lozano, J.A.: Bayesian Performance Analysis for Black-Box Optimization Benchmarking. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO-2019, New York, NY, USA, ACM Press (2019) 1789–1797

Shir, O.M., Doerr, C., Bäck, T.: Compiling a benchmarking test-suite for combinatorial black-box optimization: a position paper. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO-2018, New York, NY, USA, ACM Press (2018) 1753–1760

Shir, O.M., Yehudayoff, A.: On the Statistical Learning Ability of Evolution Strategies. In: Proceedings of the workshop on Foundations of Genetic Algorithms, FOGA-2017, New York, NY, USA, ACM Press (2017) 127-138

Nanda, V., Belure, S.V., Shir, O.M.: Searching for the Pareto frontier in multi-objective protein design. Biophysical Reviews 9(4) (2017) 339—344

Nanduri, A., Shir, O.M., Donovan, A., Ho, T.-S., Rabitz, H.: Exploring the complexity of quantum control optimization trajectories. Physical Chemistry Chemical Physics 17(1) (2015) 334—347

Shir, O.M., Roslund, J., Whitley, D., Rabitz, H.: Efficient Retrieval of Landscape Hessian: Forced Optimal Covariance Adaptive Learning. Physical Review E 89(6) (2014) 063306

Zadorojniy, A., Masin, M., Greenberg, L., Shir, O.M., Zeidner, L.: Algorithms for Finding Maximum Diversity of Design Variables in Multi-Objective Optimization. Procedia Computer Science 8 (2012) 171-176

Shir, O.M., Roslund, J., Leghtas, Z., Rabitz, H.: Quantum Control Experiments as a Testbed for Evolutionary Multi-Objective Algorithms. Genetic Programming and Evolvable Machines 13(4) (2012) 445—491 

Shir, O.M.: Niching in Evolutionary Algorithms. In: Handbook of Natural Computing: Theory, Experiments, and Applications. Springer-Verlag, Berlin-Heidelberg, Germany (2012) 1035—1069

Shir, O.M., Emmerich, M., Bäck, T.: Adaptive Niche-Radii and Niche-Shapes Approaches for Niching with the CMA-ES. Evolutionary Computation 18(1) (2010) 97–126

Shir, O.M., Beltrani, V., Bäck, T., Rabitz, H., Vrakking, M.J.: On the Diversity of Multiple Optimal Controls for Quantum Systems. Journal of Physics B: Atomic, Molecular and Optical Physics 41(7) (2008) 074021

COMPLETE LIST OF PUBLICATIONS

• Algorithmically-Guided Scientific Discoveries (Migal's Talk, 2021):
  
• Mathematical Programming as a Complement to Bio-Inspired Optimization (PPSN'18 tutorial; Coimbra PORTUGAL, September 2018) pdf
• Introductory mathematical programming for EC (GECCO'18 tutorial; Kyoto JAPAN, July 2018) ACM/dL
• Sequential experimentation by evolutionary algorithms (GECCO'18 joint tutorial with Thomas Bäck; Kyoto JAPAN, July 2018) ACM/dL
• On the Statistical Learning Ability of Evolution Strategies (Foundations of Genetic Algorithms Workshop FOGA-XIV, January 2017) pdf
• Computational Intelligence in the Natural Sciences: Machine Learning, Optimization, and Heuristic Search (Princeton University Seminar, July 2016) pdf
• Pareto Automated Recommendation (LIACS Colloquium, Dec. 2014) pdf