PeopleofSuperintelligence

Researcher

Ivan Selesnick

Also known as Ivan W. Selesnick

Ivan Selesnick is a professor whose research uses sparse signal models and optimization to analyze biomedical data. He earned electrical-engineering degrees at Rice and joined Polytechnic University in 1997. He published the Slantlet transform in 1999 and coauthored a 2005 paper on dual-tree complex wavelets for multiscale signal and image processing. A 2024 paper with three coauthors studies non-convex regularization for high-dimensional signal recovery, and in June 2026 he lectures at an Erasmus+ school on machine learning for image analysis. IEEE named him a Fellow in 2016 for wavelet and sparsity-based signal processing; as of October 2026, he is a professor of electrical and biomedical engineering at NYU Tandon.

Works
304
Citations
17,641
h-index
60

Publication numbers from OpenAlex, as of 15 October 2026.

Timeline

  1. 1990–1996

    Electrical engineering at Rice

    He earns a bachelor's degree in electrical engineering at Rice in 1990, a master's degree in 1991, and a PhD in 1996.

  2. 1997

    Joining Polytechnic University

    He joins Polytechnic University in 1997, now NYU Tandon School of Engineering.

  3. 1999

    The Slantlet transform

    He publishes the IEEE paper “The slantlet transform” in 1999.

  4. 2005

    A dual-tree wavelet framework

    With Richard Baraniuk and Nick Kingsbury, he publishes an article in 2005 on the dual-tree complex wavelet transform for multiscale signal and image processing. The transform offers near shift invariance, directional selectivity, and improved energy compaction.

  5. 2016

    An IEEE Fellowship

    IEEE elevates him to Fellow in 2016 for contributions to wavelet and sparsity-based signal processing.

  6. 2024

    Sparse signal recovery

    He and Akari Katsuma, Seisuke Kyochi, and Shunsuke Ono submit a 2024 paper on multilayered non-convex regularization for high-dimensional signal recovery. Its experiments demonstrate bias reduction in image recovery and principal component analysis.

  7. April 2026–June 2026

    Sparse methods in applied analysis

    At an NYU Tandon talk in April 2026, he presents sparse signal models as tools for biomedical time-series analysis. In June 2026, he lectures at an Erasmus+ International PhD Summer School on mathematics and machine learning for image analysis.

  8. As of October 2026

    Professor at NYU Tandon

    As of October 2026, he is a professor of electrical and computer engineering and biomedical engineering at NYU Tandon School of Engineering.

Sources and credits

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  1. Ivan Selesnick | NYU Tandon School of Engineering engineering.nyu.edu. Checked 15 October 2026.
  2. 52 SPS Members Elevated to Fellow | IEEE Signal Processing Society signalprocessingsociety.org. Checked 15 October 2026.
  3. Ivan Selesnick eeweb.engineering.nyu.edu. Checked 15 October 2026.
  4. [2409.14768] Sparsity-Enhanced Multilayered Non-Convex Regularization with Epigraphical Relaxation for Debiased Signal Recovery arxiv.org. Checked 15 October 2026.
  5. From Noise to Insight: Sparse Methods for Biomedical Signal Analysis | NYU Tandon School of Engineering engineering.nyu.edu. Checked 15 October 2026.
  6. Ivan Selesnick — Mathematics and Machine Learning for Image Analysis — ERASMUS+ International PhD Summer School site.unibo.it. Checked 15 October 2026.