Alisa Sheinkman

ML researcher.

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That's me.

Machine Learning Engineer and Researcher with a PhD in Statistics and strong proficiency in Python, PyTorch, TensorFlow and JAX. Experienced in designing efficient learning schemes and implementing complex neural networks. Passionate about building reliable probabilstic ML systems and bridging the gap between theory and production-ready code.

During my PhD, I was working under the supervision of Sara Wade and developed advances in Bayesian deep modelling. Namely, I studied efficient inference schemes with a focus on scalable variational inference algorithms such as stochastic and black box variational inference. My thesis addresses the challenge of architecture specification of Bayesian neural networks, Bayesian model choice and model combination in the realms of big data and overparametrized deep models.

Publications

Probabilistic inference in Bayesian neural networks, A. S., PhD dissertation, 2025.

SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era, E. Semenova, A. S., T. J. Hitge, S.M. Hall, J. Cockayne, NeurIPS, 2025 (accepted, to appear).

Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural Networks, A. S., S. Wade, in Proceedings of the ECML PKDD, 2025. Corresponding code.

Variational Bayesian Bow tie Neural Networks with Shrinkage, A. S., S. Wade, arXiv:2411.11132, 2024. Corresponding code.

Deep learning techniques for noise annoyance detection: results from an intensive workshop at the alan turing institute, A. Mitchell, E. Brown, R. Deo, Y. Hou, J. Kirton-Wingate, J. Liang, A. S., C. Soelistyo, H. Sood, and A. Wongprommoon The Journal of the Acoustical Society of America, 153(3_supplement): A262-A262, 2023.

More info

List of activities

Tutoring

  • Fundamentals of probability 24/25, King’s College London
  • Probability and statistics 23/24, King’s College London
  • Fundamentals of probability 23/24, King’s College London
  • Into to the dynamical systems 22/23, King’s College London
  • Fundamentals of probability 22/23, King’s College London
  • Honors Algebra 21/22, University of Edinburgh
  • Calculus and Applications 20/21, University of Edinburgh
  • Calculus 17/18, HSE Moscow

I began my PhD in algebraic geometry and non-commutative geometry. But the PhD path is far from being linear and after the first 1.5 years, I switched to a different topic. Before that transfer, I gave two talks - one on Hopf Algebras at GLAMs examples seminar and another on Khovanov homologies at the Knot homologies reading group. Now I am happily studying Bayesian statistics and deep learning whilst the research problem I left without an answer was solved by Patrick Kinnear and his paper Skein module dimensions of mapping tori of the 2-torus