Alisa Sheinkman
PhD candidate at the University of Edinburgh.
I am a 4th year PhD student working under the supervision of Sara Wade. My research aims to develop advances in Bayesian deep modelling. Namely, I study efficient inference schemes with a focus on scalable variational inference algorithms such as stochastic and black box variational inference; My work 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
The Architecture and Evaluation of Bayesian Neural Networks, A. Sheinkman, S. Wade, arXiv:2503.11808, 2025. Corresponding code.
Variational Bayesian Bow tie Neural Networks with Shrinkage, A. Sheinkman, 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. Sheinkman, C. Soelistyo, H. Sood, and A. Wongprommoon The Journal of the Acoustical Society of America, 153(3_supplement): A262-A262, 2023.