ANDRES DE BUSTOS

About

I am a scientist (PhD. in Physics 2012) with sixteen years of experience in research in the field of magnetic confinement fusion and data science. My areas of expertise are numerical study of plasma transport, developing simulation codes, data analysis and comparison with experiments. From Nov. 2016 to Dec. 2019 I worked as programmer and analyst at Next Limit in the field of network science and computer vision for autonomous driving. From Dec. 2019 to Sep 2022 I worked as a researcher in the Department of Technology of CIEMAT, participating in several projects related to scientific computation. In 2023 I worked in the National Fusion Laboratory (CIEMAT), developing software for fusion plasma transport. Since Jan. 2024 I am a senior researcher at the Technology Dep. at CIEMAT.

I am a person able to work autonomously in new challenges and learn new skills, well organized and detail oriented in my work. Finally, I consider myself a kind person to deal with, having experience working in international teams.

Areas of knowledge:
* Scientific Computing, Numerical simulation. PhD in Fusion Plasmas.
* Data Science, Complex Networks, Stochastic Calculus, Statistics
* Electrodynamics, Statistical Physics, Bioinformatics
* Mostly Python programming
* Grid technologies, HPC usage
* Linux, HPC administration

Employment

• CIEMAT Permanent Researcher
  2024 - Present
• CIEMAT
  2023 - 2024
• Centro de Investigaciones Energéticas Medioambientales y Tecnológicas Laboral Temporal
  2019 - 2022
• Next Limit
  2016 - 2019
• Universidad Carlos III de Madrid PostDoc
  2015 - 2015
• Max Planck Institute -IPP
  2012 - 2014
• CIEMAT PhD. Student
  2007 - 2012
• Universidad Carlos III de Madrid

Education

• Universidad Complutense de Madrid Doctor en Física
  2007 - 2012
• Universidad Complutense de Madrid Master en Física Fundamental
  2006 - 2007
• Erasmus - Universidad de Uppsala
  2004 - 2005
• Universidad Complutense Madrid Licenciado en Física
  ? - 2006

Projects & Funding

Projects & funding information is unavailable.

Publications (39)

• An AI-based system to assist session leader during stellarator operations—a prototype
  Plasma Physics and Controlled Fusion 2025 DOI: 10.1088/1361-6587/adfd80
  +Save
• A novel unsupervised machine learning algorithm for automatic Alfvénic activity detection in the TJ-II stellarator
  Nuclear Fusion 2024 DOI: 10.1088/1741-4326/ad85f4
  +Save
• Density profiles in stellarators: an overview of particle transport, fuelling and profile shaping studies at TJ-II
  Nuclear Fusion 2024 DOI: 10.1088/1741-4326/ad67ef
  +Save
• Response of HPC hardware to neutron radiation at the dawn of exascale
  Journal of Supercomputing 2023 DOI: 10.1007/s11227-023-05199-y
  +Save
• Unsteady RANS-based DMD analysis of airfoil NACA0015 with Gurney flap
  International Journal of Heat and Fluid Flow 2023 DOI: 10.1016/j.ijheatfluidflow.2022.109099
  +Save
• Energy Consumption Studies of WRF Executions with the LIMITLESS Monitor
  Communications in Computer and Information Science 2022 DOI: 10.1007/978-3-031-04209-6_2
  +Save
• Error resilience of three GMRES implementations under fault injection
  Journal of Supercomputing 2022 DOI: 10.1007/s11227-021-04148-x
  +Save
• Unsteady Rans-Based Dmd Analysis of Airfoil Naca0015 with Gurney Flap
  SSRN 2022 DOI: 10.2139/ssrn.4261712
  +Save
• Analysis of mental health disease trends using Begraph software in Spanish health care centers: Case study
  JMIR Medical Informatics 2021 DOI: 10.2196/15527
  +Save
• Analyzing Mental Health Diseases in a Spanish Region Using Software Based on Graph Theory Algorithms
  Susel Góngora Alonso, A. Bustos, Sofiane Hamrioui +4 more
  Advances in Intelligent Systems and Computing 2021 DOI: 10.1007/978-981-15-5113-0_57
  +Save
• Automatic identification of MHD modes in magnetic fluctuation spectrograms using deep learning techniques
  Plasma Physics and Controlled Fusion 2021 DOI: 10.1088/1361-6587/ac08f7
  +Save
• Dynamically Distributing Tasks from an Unattended Parallel Compiler with Cloudbook
  Communications in Computer and Information Science 2021 DOI: 10.1007/978-3-030-68035-0_1
  +Save
• Automatic identification of MHD modes in magnetic fluctuations spectrograms using deep learning techniques
  arXiv 2020 DOI: 10.48550/arxiv.2011.12615
  +Save
• Non-diffusive nature of collisionless α -particle transport: Dependence on toroidal symmetry in stellarator geometries
  Physics of Plasmas 2020 DOI: 10.1063/5.0006209
  +Save
• Analysis of Mental Health Disease Trends Using BeGraph Software in Spanish Health Care Centers: Case Study (Preprint)
  2019 DOI: 10.2196/preprints.15527
  +Save
• Explaining the isotope effect on heat transport in L-mode with the collisional electron-ion energy exchange
  Nuclear Fusion 2017 DOI: 10.1088/1741-4326/aa65b3
  +Save
• Semianalytical calculation of the zonal-flow oscillation frequency in stellarators
  Plasma Physics and Controlled Fusion 2017 DOI: 10.1088/1361-6587/aa6990
  +Save
• Semianalytical calculation of the zonal-flow oscillation frequency in stellarators
  arXiv 2017 DOI: 10.48550/arxiv.1701.02722
  +Save
• Isotope effect on confinement unraveled for L-mode in dimensionless scan
  43rd European Physical Society Conference on Plasma Physics, EPS 2016 2016
  +Save
• Quasi-symmetry and the nature of radial turbulent transport in quasi-poloidal stellarators
  Physics of Plasmas 2016 DOI: 10.1063/1.4965231
  +Save