About

Associate Professor Aleksandr Aravkin's research focuses on optimization, robust statistics, health metrics, and machine learning. He is involved in developing comprehensive meta-analytic approaches for analyzing relationships between risks and outcomes, as well as working on COVID-19 modeling and health metrics. His work spans various fields including health metrics, tracking and navigation, seismic imaging, computational finance, neuroscience, and computational medicine.

Research topics

• Medicine
• Environmental health
• Demography
• Internal medicine
• Gerontology
• Geography
• Economics
• Psychiatry
• Immunology
• Mathematics
• Nursing
• Econometrics
• Virology
• Pediatrics
• Risk analysis (engineering)
• Intensive care medicine
• Microbiology
• Statistics
• Biology

Selected publications

• Sparse and nonparametric estimation of equations governing dynamical systems with applications to biology

  Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2026-04-15

  preprintOpen access

  Abstract Data-driven discovery of model equations is a powerful approach to understanding the behaviour of dynamical systems in many scientific fields. In particular, the ability to learn mathematical models from data would benefit systems biology, where the complex nature of these systems often makes a bottom up approach to modelling unfeasible. In recent years, sparse estimation techniques have gained prominence in system identification, primarily using parametric paradigms to efficiently capture system dynamics with minimal model complexity. In particular, the Sindy algorithm has successfully used sparsity to estimate nonlinear systems by extracting from a library of functions only a few key terms needed to capture the dynamics of these systems. However, parametric models often fall short in accurately representing certain nonlinearities inherent in complex systems. To address this limitation, we introduce a novel framework that integrates sparse parametric estimation with nonparametric techniques. It captures nonlinearities that Sindy cannot describe without requiring a priori information about their functional form. That is, without expanding the library of functions to include the one that is trying to be discovered. We illustrate our approach on several examples related to estimation of complex biological phenomena.

  PublisherDOI

• Global burden of metabolic dysfunction-associated steatotic liver disease, 1990–2023, and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2023

  ˜The œLancet. Gastroenterology & hepatology · 2026-04-14 · 2 citations

  article
  PublisherDOI

• NeoGeographyToolkit/StereoPipeline: 2026-01-07-daily-build

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-07

  otherOpen access

  Recent additions log: https://stereopipeline.readthedocs.io/en/latest/news.html

  PublisherDOI

• NeoGeographyToolkit/StereoPipeline: 2026-01-03-daily-build

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-03

  otherOpen access

  Recent additions log: https://stereopipeline.readthedocs.io/en/latest/news.html

  PublisherDOI

• NeoGeographyToolkit/StereoPipeline: 2026-01-05-daily-build

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-05

  otherOpen access

  Recent additions log: https://stereopipeline.readthedocs.io/en/latest/news.html

  PublisherDOI

• NeoGeographyToolkit/StereoPipeline: 2026-01-01-daily-build

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-01

  otherOpen access

  Recent additions log: https://stereopipeline.readthedocs.io/en/latest/news.html

  PublisherDOI

• Global burden of cancer in children and adolescents aged 0–19 years, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023

  The Lancet · 2026-04-01 · 1 citations

  article
  PublisherDOI

• NeoGeographyToolkit/StereoPipeline: 2026-01-02-daily-build

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-02

  otherOpen access

  Recent additions log: https://stereopipeline.readthedocs.io/en/latest/news.html

  PublisherDOI

• NeoGeographyToolkit/StereoPipeline: 2026-01-04-daily-build

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-04

  otherOpen access

  Recent additions log: https://stereopipeline.readthedocs.io/en/latest/news.html

  PublisherDOI

• NeoGeographyToolkit/StereoPipeline: 2026-01-06-daily-build

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-06

  otherOpen access

  Recent additions log: https://stereopipeline.readthedocs.io/en/latest/news.html

  PublisherDOI

Frequent coauthors

• Simon I Hay

  290 shared

• Christopher J L Murray

  University of Washington

  286 shared

• Ali H. Mokdad

  234 shared

• Peng Zheng

  Institute for Health Metrics and Evaluation

  221 shared

• Stephen S Lim

  217 shared

• Xiaochen Dai

  210 shared

• Theo Vos

  167 shared

• G Anil Kumar

  158 shared

Education

• Ph.D., Mathematics (Optimization)

  University of Washington

  2010

• M.S., Statistics

  University of Washington

  2010

• B.S., Mathematics and Computer Science

  University of Washington

  2004