The 33rd Jyvaskyla Summer School will be organized on August 5-16, 2024 at the University of Jyväskylä, Finland.
The Summer School welcomes students from all around the world to learn from top-level scientists, expand their professional network, and create new memories. The Summer School offers courses for advanced Master's students, PhD students and post-docs in various fields of natural sciences, mathematics and information technology including multiobjective optimization. All courses are taught in English by distinguished researchers. Participation in all Summer School courses is free of charge. For more information about the Summer School, please visit https://www.jyu.fi/jss
Deadline for applications to attend courses: end of April. For more information, see https://www.jyu.fi/en/study-with-us/summer-and-winter-schools/jyvaskyla-summer-school/how-to-apply-to-jyvaskyla-summer-school
Information about the courses available: https://www.jyu.fi/en/study-with-us/summer-and-winter-schools/jyvaskyla-summer-school/jyvaskyla-summer-school-course-programme
For example, we offer the following course related to multiobjective optimization on August 12-16:
COM2: Beyond Conventional Optimization: Data-Driven Multiobjective Bayesian Optimization
Lecturer: Dr. Tinkle Chugh (University of Exeter, UK)
Many real-world optimization problems involve computationally (or financially) expensive evaluations. For example, the shape design of a component in an air intake ventilation system [1] and the stator design in a hydrodynamic pump [2], involve computationally expensive fluid dynamics simulations. Another example of a computationally expensive problem involving a hydrogeological simulation model is the management of hydraulic barriers in coastal aquifers [3]. All these problems are black boxes without any information on gradients and closed-form expressions of objectives. Bayesian optimization is an efficient tool for solving such kinds of problems. The course will bridge the gap between traditional optimization limitations and the demands of modern decision-making. By exploring advanced Bayesian techniques integrated with data-driven approaches, the course will cover different approaches for solving complex multiobjective optimization problems [4] and provide methods to make informed decisions efficiently. The potential of Bayesian optimization will be shown by providing examples of real-world applications. The course aims to provide a comprehensive understanding of cutting-edge optimization methodologies and their potential in solving real-world optimization problems.
References:
[1] T. Chugh, T. Kratky, K. Miettinen, Y. Jin, P. Makkonen P. (2019) Multiobjective Shape Design in a Ventilation System with a Preference-driven Surrogate-assisted Evolutionary Algorithm, In the proceedings of The Genetic and Evolutionary Computation Conference, Pages 1147-1155, 2019
[2] T. Kratky. Shape optimization of hydraulic surfaces of the impeller and stator parts of hydrodynamic pumps, 2021. Available from: https://theses.cz/id/6ihxiw/. Doctoral theses, Dissertations. Palacky University Olomouc, Faculty of Science.
[3] S. Saad, AA Javadi, T. Chugh, R. Farmani (2022) Optimal management of mixed hydraulic barriers in coastal aquifers using multi-objective Bayesian optimization, Journal of Hydrology, volume 612, pages 128021-128021, 2022
[4] T. Chugh. Mono-surrogate vs multi-surrogate in multi-objective Bayesian optimisation. In the proceedings of The Genetic and Evolutionary Computation Conference, Pages 2143-2151, 2022.
For further information, see COM2 at https://www.jyu.fi/en/study-with-us/summer-and-winter-schools/jyvaskyla-summer-school/jyvaskyla-summer-school-course-programme
There are also many other courses, for example, introduction to quantum computing.
Please, forward information about the summer school to colleagues and students who could be interested in attending.
With best regards, Kaisa Miettinen