In this year’s ICML, some interesting work was presented on Neural Processes. In this blog post, I discuss what Neural Processes are and how they behave as a prior over functions.
I have a broad interest in statistical machine learning and Bayesian inference techniques with a focus on tackling real problems in genomics and healthcare. My PhD research focuses on enabling interpretability within probabilistic latent variable models (such as Gaussian Process Latent Variable Models and Variational Autoencoders). By synthesising ideas from statistics and machine learning, my goal is to embed interpretable structure within such non-linear models, in order to make them more useful for biomedical applications.
I am also enthusiastic about R and data visualisation, and I co-organise the Oxford R user group meetups. Additionally, in my research I like to make use of automatic differentiation frameworks (such as TensorFlow and PyTorch) and I am enthusiastic about probabilistic programming frameworks (such as Stan, PyMC3, or Edward).
Prior to the PhD, I studied for BSc and MSc at the University of Tartu in Estonia. There I was part of the BIIT research group at the Institute of Computer Science, where I worked on statistical modelling in genomics under the supervision of Raivo Kolde and Leopold Parts.
R package implementing the Polya-Gamma augmentation scheme
Here you can find course material (in Estonian!) on Data Science and Visualisation, which we created together with Tanel Pärnamaa. This course “Statistiline andmeteadus ja visualiseerimine” is centered around a number of interesting case studies, and it focuses on teaching good practices of data science in R by applying statistical methods to solve these real-life problems.