Boro Sofranac
Quantagonia

My experiences include industry and research posts in the fields of Mathematical Optimization, High-Performance & Quantum Computing, as well as Scientific Simulations and Software Development. For a detailed list of my experiences, head over to my LinkedIn page.

My current position is Head of Development at Quantagonia, with a focus on quantum applications-specific development. At Quantagonia, we are developing hybrid classical-HPC-Quantum algorithms targeting problems from Mathematical Optimization, Artificial Intelligence, and Scientific Simulations, which are made available via our cloud platform. For more details, head over to our website.

I hold a master's degree in Computational Science and Engineering from Technical University Munich and a bachelor's degree in Mechanical Engineering from the University of Montenegro. I am currently working towards a PhD in Computer Science at the Technical University Berlin.

Publications:

Preprints:

1. Bestuzheva et al. (2021). The SCIP Optimization Suite 8.0. Preprint. [arXiv]

Refereed conference proceedings:

1. Sofranac, B., Gleixner, A., & Pokutta, S. (2021). An Algorithm-Independent Measure of Progress for Linear Constraint Propagation. In L. D. Michel (Red), 27th International Conference on Principles and Practice of Constraint Programming (CP 2021) (bll 52:1-52:17). doi:10.4230/LIPIcs.CP.2021.52. [arXiv][code][slides][video]
2. Sofranac, B., Gleixner, A., & Pokutta, S. (2020). Accelerating Domain Propagation: An Efficient GPU-Parallel Algorithm over Sparse Matrices. 2020 IEEE/ACM 10th Workshop on Irregular Applications: Architectures and Algorithms (IA3), 1–11. doi:10.1109/IA351965.2020.00007. [arXiv][code][summary][slides][video]

Refereed journals:

1. Sofranac, B., Gleixner, A., & Pokutta, S. (2022). An Algorithm-Independent Measure of Progress for Linear Constraint Propagation. Constraints (2022). doi:10.1007/s10601-022-09338-9 [arXiv][code][slides][video]
2. Sofranac, B., Gleixner, A., & Pokutta, S. (2022). Accelerating domain propagation: An efficient GPU-parallel algorithm over sparse matrices. Parallel Computing, 109, 102874.  doi:​10.1016/j.parco.2021.102874 [arXiv][code][summary][slides][video]