Building with Tel-Hai logo

faculty member

  • Prof. Ofer Shir
  • Associate Professor
  • Computer Science
  • Biotechnology - M.Sc.
  • [email protected]
  • 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.

  • Currently:
    * Visiting Associate Professor, Faculty of Mathematics, Technion - Israel Institute of Technology.
    * Associate Professor, Computer Science Department, Tel-Hai College.
    * Principal Investigator, Computational Sciences, Migal-The Galilee Research Institute.

    Previously:
    * Head, Computer Science Department, Tel-Hai College.
    * 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)
    • 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

  • CV download
    • 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
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