PeopleofSuperintelligence

Researcher

Aleksandr Aravkin

Also known as Sasha Aravkin, Aleksandr Y. Aravkin

Aleksandr Aravkin is an applied mathematician at the University of Washington whose optimization and machine learning work combines physical models with data-driven models. He earned a mathematics doctorate and a statistics master's at Washington in 2010, worked at the University of British Columbia and IBM, and joined Washington's faculty in 2015. In 2018, he coauthored SR3, a framework for sparse regression used in machine learning and scientific model discovery. At the Institute for Health Metrics and Evaluation, he helped develop COVID-19 forecasting tools in 2020 and coauthored the Burden of Proof methods paper in 2022 to assess health risks. As of October 2026, he is Professor of Applied Mathematics at Washington and Director of Mathematical Sciences at IHME.

Timeline

  1. 2004–2010

    Degrees at Washington

    He earns a bachelor's degree in mathematics and computer science at the University of Washington in 2004. In 2010, he completes a PhD in mathematics, focused on optimization, and a master's degree in statistics at Washington.

  2. 2010

    Seismic research at UBC

    In 2010, he joins the University of British Columbia's Seismic Laboratory for Imaging and Modeling as a postdoctoral fellow, using statistical models and optimization to solve seismic inverse problems. His doctoral research with Jim Burke develops robust formulations and optimization algorithms for dynamic inference.

  3. 2012

    IBM research and Columbia teaching

    He joins the IBM Watson Research Center as research staff in 2012, collaborating on machine learning, computer vision, speech recognition and other applications. During his IBM appointment, he also teaches three master's-level machine learning courses as an adjunct professor at Columbia University.

  4. 2013

    Robust estimation and Kalman smoothing

    In 2013, he coauthors a Journal of Machine Learning Research paper with James V. Burke and Gianluigi Pillonetto. It develops a statistical modeling framework and numerical methods for learning and estimation with nonsmooth loss functions.

  5. 2015

    Joining Washington and eScience

    In 2015, he accepts an assistant professorship in the University of Washington's Department of Applied Mathematics. The appointment comes through a joint initiative with the eScience Institute.

  6. 2018

    Sparse regression with SR3

    In 2018, he coauthors SR3 with Peng Zheng, Travis Askham, Steven L. Brunton and J. Nathan Kutz. The framework uses a relaxed optimization problem to solve sparse regression tasks in machine learning and scientific model discovery, with examples in compressed sensing and matrix completion.

  7. 2020

    Tools for COVID-19 forecasts

    In 2020, he leads the Mathematical Sciences team at the Institute for Health Metrics and Evaluation, which develops methods and software for IHME's COVID-19 model. The model forecasts infections, deaths and equipment demand; its initial version uses the CurveFit tool.

  8. 2022

    The Burden of Proof studies

    He coauthors the Burden of Proof methods paper in Nature Medicine in 2022. The approach combines evidence across studies to compare health risks and translates an assessment of each risk–outcome relationship into a star rating.

  9. 2025

    A JASA Reproducibility Award

    In 2025, he shares the JASA Reproducibility Award with Zihang Wang, Irina Gaynanova and Benjamin B. Risk. Their paper applies sparse independent component analysis to cortical surface fMRI data in autism.

  10. As of October 2026

    Professor and IHME director

    As of October 2026, he is Professor in the Department of Applied Mathematics at the University of Washington and Director of Mathematical Sciences at IHME. He leads IHME's Mathematical Sciences and Computational Algorithms team. His work combines physics-based and data-driven models and focuses on optimization, machine learning and data science.

Sources and credits

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  1. Aleksandr Aravkin UW Applied Mathematics uw-amo.github.io. Checked 27 October 2026.
  2. Structured Optimization for Big Data and Machine Learning amath.washington.edu. Checked 27 October 2026.
  3. Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory jmlr.org. Checked 27 October 2026.
  4. Dr. Aleksandr (Sasha) Aravkin joins faculty as Assistant Professor amath.washington.edu. Checked 27 October 2026.
  5. A Unified Framework for Sparse Relaxed Regularized Regression: SR3 arxiv.org. Checked 27 October 2026.
  6. Fighting COVID-19 with Math artsci.washington.edu. Checked 27 October 2026.
  7. The Burden of Proof studies: assessing the evidence of risk nature.com. Checked 27 October 2026.
  8. JASA Reproducibility Awardees jasa-acs.github.io. Checked 27 October 2026.
  9. Aleksandr Aravkin – Department of Health Metrics Sciences depts.washington.edu. Checked 27 October 2026.