Alisa Sheinkman

I am a recent doctoral graduate willing to advance probabilistic machine learning and, more broadly, artificial intelligence in the age of big data and ever-expanding models.

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.

Got my PhD degree! July 2025, Worldwide

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, arXiv:2502.06753, 2025.

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 (to appear). 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.