About me#

I am a PhD candidate in Informatics at the University of Amsterdam working with Max Welling. My research is on structure-aware machine learning for scientific systems.

I am interested in models that make useful structure explicit: symmetries and graphical structure, with reflections in tensor-network factorizations and Markov kernels.

I was happy to collaborate with and learn from Roberto Bondesan and Kirill Neklyudov.

Current status I expect to complete my PhD in November 2026. In 2025 I worked with Qualcomm AI Research in Amsterdam on efficient structured computation for attention-like layers.

Research direction My research program is structure-aware machine learning for scientific systems, with a current focus on quantum systems. I connect probabilistic inference, efficient neural architectures, tensor-network ideas, and quantum-inspired algorithms.

Hot off The Press Myosotis SPIGM workshop poster

An interlude about structure computation, SSM, and attention: Myosotis. I hope to tell more in this line of work later; see the SPIGM workshop poster.

Selected Papers#

Quantum Systems and Structured ML#

  • Myosotis: Structured Computation for Attention-Like Layers (NeurIPS 2025 SPIGM Workshop)
    Evgenii Egorov, Hanno Ackermann, Markus Nagel, Han Cai
    arXiv

  • An Equivariant Machine Learning Decoder for 3D Toric Codes (QCNC 2025)
    Oliver Weissl, Evgenii Egorov
    arXiv

  • The END: An Equivariant Neural Decoder for Quantum Error Correction (ICLR 2023 Workshop on Physics for ML)
    Evgenii Egorov, Roberto Bondesan, Max Welling
    Code arXiv

Probabilistic machine learning, MCMC, and generative models#

  • Ai-sampler: Adversarial Learning of Markov Kernels with Involutive Maps (ICML 2024)
    Evgenii Egorov, Riccardo Valperga, Efstratios Gavves
    Code ICML PMLR arXiv

  • Involutive MCMC: a Unifying Framework (ICML 2020)
    Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov
    ICML PMLR arXiv

  • MaxEntropy Pursuit Variational Inference (ISNN 2019)
    Evgenii Egorov, Kirill Neklydov, Ruslan Kostoev, Evgeny Burnaev
    Springer arXiv

  • The Implicit Metropolis-Hastings Algorithm (NeurIPS 2019)
    Kirill Neklyudov, Evgenii Egorov, Dmitry Vetrov
    NeurIPS arXiv

  • BooVAE: Boosting Approach for Continual Learning of VAE (NeurIPS 2021)
    Evgenii Egorov, Anna Kuzina, Evgeny Burnaev
    Code NeurIPS arXiv

  • Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems (Frontiers in Neuroscience 2019)
    Anna Kuzina, Evgenii Egorov, Evgeny Burnaev
    DOI

Ongoing Work#

I am thinking about tensor-network contraction, classical shadows, and neural quantum states. Let’s see…

Teaching and Mentoring#

  • Main supervisor, MSc thesis: Structured Neural Shadow Quantum State Tomography, Francien Barkhof, 2025-2026. Grade: 9/10.
  • Main supervisor, MSc thesis: Equivariant Machine Learning Decoder for 3D Toric Codes, Oliver Weissl, 2024-2025. Grade: 8.5/10.
  • Developed materials for the graduate course Deep Bayesian Machine Learning, Skoltech CDISE, autumns 2018-2020.
  • Delivered practical seminars for Bayesian Machine Learning, HSE, HSE Computer Science, 2020, complementing lectures by Dmitry Vetrov.

Experience#

  • PhD Candidate, University of Amsterdam, 2021-present.
  • Research Associate / Research Intern, Qualcomm AI Research, Amsterdam Office, Feb-Aug 2025.
  • Junior Researcher, Skoltech, 2017-2020.

Email#

You can reach me at email dot evgenii dot egorov at gmail dot com. The word email is part of the address.

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