Prof. Ofer Shir

I am interested in learning and optimization questions related to systems within the Natural Sciences. Specifically, my fields of interest encompass Statistical Learning within Optimization and Deep Learning in Practice, Self-Supervised Learning, Algorithmically-Guided Experimentation, Combinatorial Optimization and Benchmarking (White/Gray/Black-Box), Quantum Optimization and Quantum Machine Learning.

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)
• Workshop on Operations Research and Optimization Methods (199833; Spring)

Tel-Hai College is an Academic Partner of Visual Paradigm, and is granted the use of Visual Paradigm's online UML tool and BPMN editor for educational use.

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

SELECTED PEER-REVIEWED PUBLICATIONS:

• Shir, O.M., Israeli, A., Caftory, A., Zepko, G., Bloch, I.: Algorithmically-guided discovery of viral epitopes via linguistic parsing: Problem formulation and solving by soft computing. Applied Soft Computing 129(2022) 109509
• Shir, O.M., Yazmir, B., Israeli, A., Gamrasni, D.: Algorithmically-Guided Postharvest by Experimental Combinatorial Optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO-2022, New York, NY, USA, ACM Press (2022) 2027–2035
• Kocaman, V., Shir, O.M., Bäck, T.: Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study. In: Proceedings of the 25th International Conference on Pattern Recognition, ICPR2020 (2021) 10404–10411
• Shir, O.M., Xi, X., Rabitz, H.: Multi-level evolution strategies for high-resolution black-box control. Journal of Heuristics 27(6) (2021) 1021—1055
• 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, 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, 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, NY, USA, ACM Press (2019) 1789–1797
• 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, 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
• 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
• 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

COMPLETE LIST OF PUBLICATIONS

Algorithmically-Guided Scientific Discoveries (Migal's Talk, 2021):

• Introductory Mathematical Programming for EC (GECCO'21 tutorial; Lille FRANCE, July 2021) ACM/dL
• Sequential Experimentation by Evolutionary Algorithms (GECCO'21 joint tutorial with Thomas Bäck; Lille FRANCE, July 2021) ACM/dL
• Fundamentals of ESs' Statistical Learning (Dagstuhl Seminar on Theory of Randomized Optimization Heuristics, 17191, May 2017) pdf
• 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

• Assaf Israeli, Multiobjective Optimization of Soil Sampling Design Using Information Metrics. MSc Thesis Tel-Hai College (joint supervision with Iggy Litaor, 2020): dissertation for download
• Chen Erlich, Experimental Combinatorial Optimization of Phycobiliproteins' Expression in E.coli. MSc Thesis Tel-Hai College (joint supervision with Dror Noy, 2019): dissertation for download
• IOHprofiler: Iterative Optimization Heuristics Profiler: web tool, GitHub, flyer
• COST Action 15140: Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO) (MC Member IL; WG3)
• Dagstuhl Seminar on Theory of Randomized Optimization Heuristics (17191, May 2017)
• Niching in Evolution Strategies: An Introduction (old page summarizing doctoral work on multimodal optimization)
• Leiden Institute of Advanced Computer Science (LIACS)
• Natural Computing Group at LIACS (PhD Advisor I: Prof. Thomas Bäck)
• AMOLF Amsterdam (PhD Advisor II: Prof. Marc Vrakking)
• Rabitz Group, Princeton University (Postdoc Host: Prof. Herschel Rabitz)