The Svedberg seminar series: Ass. Prof. Prashant Singh
October 18 @ 15:15 – 16:15 CEST
SciLifeLab Fellow at Uppsala University
Prashant Singh is a SciLifeLab fellow and Assistant Professor hosted by the Division of Scientific Computing, Department of Information Technology, Uppsala University. His research interests involve developing machine learning and optimization methods to enable fast, data-efficient analysis and processing of scientific data, particularly in the domain of life sciences.
Scalable Likelihood-Free Parameter Inference of Stochastic Biochemical Reaction Networks
Abstract: Parameter inference of stochastic time series models, such as gene regulatory networks in the likelihood-free setting is a challenging task, particularly when the number of parameters to be inferred is large. Recently, data-driven machine learning models (neural networks in particular) have delivered encouraging results towards addressing the scalability, efficiency and parameter inference quality of the likelihood-free parameter inference pipeline. In particular, this talk will present a detailed discussion on neural networks as trainable, expressive and scalable summary statistics of high-dimensional time series for parameter inference tasks.
M. Akesson, P. Singh, F. Wrede and A. Hellander, “Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation,” in IEEE/ACM Transactions on Computational Biology and Bioinformatics, doi: 10.1109/TCBB.2021.3108695.
Host: Prof. Elisabeth Larsson, Uppsala University