Локон Амескита Руфино Арольдо
Московский институт электроники и математики им. А.Н. Тихонова
Профессиональные интересы
Должности
- Стажер-исследователь — Московский институт электроники и математики им. А.Н. Тихонова, Международная лаборатория суперкомпьютерного атомистического моделирования и многомасштабного анализа
- Аспирант — Московский институт электроники и математики им. А.Н. Тихонова, Международная лаборатория суперкомпьютерного атомистического моделирования и многомасштабного анализа
Био
- · Начал работать в НИУ ВШЭ в 2026 году.
Опыт работы
- · 2023: ВШЭ Стажер-исследователь лаборатории по ИИ для вычислительной биологии. Проект: Парализация поиска пептидов, как часть Crux Mass Spectrometry Analysis Toolkit
- · 2020: Sigfox Guatemala (филиал WND Latin America) /
- · 2021: Sigfox Network Support and IoT Device Integrator
Идентификаторы исследователя
- ORCID:
0009-0002-6524-5490 - Google Scholar: https://scholar.google.com/citations?user=AxCce8gAAAAJ&hl=es
Публикации (2)
Parallel Implementation of Voronoi Tessellation in the Julia Programming Language
2026 · CHAPTER · en
Voronoi tessellation is an important concept for analyzing the distribution of particles in physical systems. In this paper, we present an implementation of Voronoi tessellation in a Julia programming language library. The core engine is Voro++, a C++ library for threedimensional Voronoi tessellations. The C++ interface is made available in Julia through the CxxWrap.jl package. Additionally, the algorithm is parallelized using native Julia task-based multithreading. The parallelization strategy employs block domain decomposition. The parallel performance and scalability of the implementation are tested on model problems.
Fast and Memory-Efficient Searching of Large-Scale Mass Spectrometry Data Using Tide
2025 · ARTICLE · en
Over the past 30 years, software for searching tandem mass spectrometry data against a protein database has improved dramatically in speed and statistical power. However, existing tools can still struggle to analyze truly massive data sets when either the number of spectra or the number of proteins being analyzed grows too large. Here, we describe enhancements to the Tide search engine that allow it to handle data sets containing >10 million spectra and databases containing >7 billion peptides on commodity hardware. We demonstrate that the new Tide architecture is around 2–7 times faster than the previous version and is now comparable to MSFragger and Sage in speed while requiring much less memory. Tide is open source and is publicly available as precompiled binaries for Windows, Linux, and Mac.
Курсы (0)
Нет курсов.