Albert Jiménez Blanco
Degree in Mathematics and Master's Degree in Research and Innovation in Computational Science and Engineering
Acceleration of pangenomic workloads in HPC architectures
Acceleration of pangenomic workloads in HPC architectures
Description
This project focuses on accelerating genome-wide workloads by applying optimization and performance engineering techniques to computational biology and genomics applications. His work explores how heterogeneous accelerators, including multicore CPUs and emerging ASICs, can be leveraged to overcome computational bottlenecks in large-scale genome graph analysis. Combining algorithmic insight, mathematical modeling, and hardware-level optimization, Albert contributes to building efficient pipelines capable of handling the complexity of population-scale genomic data.Background
Albert Jiménez Blanco obtained his Bachelor's degree in Mathematics in 2023 from the Polytechnic University of Catalonia (UPC). In 2022, he completed a Master's degree in Research and Innovation in Computational Science and Engineering at the Autonomous University of Barcelona (UAB), graduating with an average grade of 9.69/10. During his Master's studies, he specialized in Advanced High Performance Computing and Engineering Technology, Innovation and Research. His Master's Final Project, focused on the acceleration of core bioinformatics algorithms for sequence alignment to graphs using HPC techniques, received the maximum final grade of 10/10. Albert's technical expertise and multidisciplinary training combine theoretical mathematics and applied computer science, focusing on the optimization and performance engineering of computationally intensive applications. This solid foundation allows him to design and tune software that fully exploits modern heterogeneous architectures in the context of computational genomics.Motivation
Advances in computational biology are essential to advance medical research. For this reason, the proposal of more efficient and accurate bioinformatics analysis tools has direct and indirect impacts on the speed and accuracy of medical studies. Albert is motivated by the challenge of applying high-performance computing and software-hardware accelerators to improve pangenome analysis, one of the most computationally demanding tasks in modern genomics. Combining mathematics, performance engineering and hardware-level optimization, his goal is to make large-scale DNA and RNA studies more efficient and accurate. This directly contributes to advances in biomedical research, from personalized medicine to tracking viral evolution and early detection of emerging variants. Albert's research interests focus on pangenome analysis, high-performance computing, performance engineering, and optimization techniques applied to computational biology and genomics.
Host Organization
Supervisors
Santiago Marco-Sola
UPC supervisor
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