TraDE-Opt’s ESRs will have a unique opportunity to excel in the academic and nonacademic career. TraDE-Opt’s training of the involved ESRs will be personalized and learning centric, and traditional formats will be complemented with a “learning by doing” approach. The adopted methodology has the goal of developing and improving each one’s skills, knowledge, and interests, as a means of maximizing each one’s potential and career prospects.

ESR1. Cristian Vega

Fast iterative regularization through dynamical systems

Host Institution:UniGe

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ESR2. Cheik Traoré

Exploiting geometry in optimization for data science

Host Institution: UniGe

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ESR3. Gabriele Scrivanti

Efficient convex relaxations of non-convex problems arising in signal processing and computer vision

Host Institution: CS

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ESR4. Mouna Gharbi

Accelerated unfolded MM approaches

Host Institution: CS

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ESR5. Lionel Tondji

Incremental methods for huge scale image reconstruction

Host Institution: TUBS

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ESR6. Emanuele Naldi

Flexible hierarchical splitting of convex optimization problems

Host Institution: TUBS

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ESR7. Rodolfo Assereto

Non-stationary preconditioning and multiscale approaches for variational imaging

Host Institution: U-GRAZ

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ESR8. Enis Chenchene

Regularized algebraic reconstruction techniques

Host Institution: U-GRAZ

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ESR9. Flavia Chorobura

Scalable optimization algorithms for huge-scale optimization problems

Host Institution: UPB

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ESR10. Yassine Nabou

Significant advances in the state of the art of decision-making for complex network systems

Host Institution: UPB

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ESR11. Giovanni Bruccola

Global optimization tools for big data problems

Host Institution: SRI PAS

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ESR12. Hung Tran

Projection methods in convex splitting algorithms for huge data problems

Host Institution: SRI PAS

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ESR13. Yassine Kamri

Performance estimation and design of optimal optimization methods in data science

Host Institution: UCL

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ESR14. Sofiane Tanji

Automated method selection and tuning for optimization problems in data science

Host Institution: UCL

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ESR15. Jonathan Chirinos Rodriguez

Unsupervised features learning from multivariate time series

Host Institution: CAMELOT

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“Nothing in life is to be feared, it is only to be understood. Now is the time to understand more, so that we may fear less. -Marie SKŁODOWSKA-CURIE”