Two preprints by Ramin

Congrats to Ramin for posting no less than two preprints on arXiv:

Both papers result from a collaboration between Ramin and Dariush Salami at the Ambient Intelligence group at Aalto University, and introduce new graph neural network-based methods for analyzing point cloud data.

Great to see when students independently initiate projects and push them through to completion (exposing the limits of their supervisor’s knowledge in the process 🙂

CEDAS conference 2021

Does your work involve data science? Are you curious about research in data science in Bergen? The CEDAS conference 2021 will be a 2-day event featuring talks and discussion by leading international scientists and local experts on the interaction between data science, statistics, machine learning, and AI, as well as their applications in science and society. The conference will also provide a friendly virtual poster session to present your own work, and an opportunity to connect (meeting physically if regulations allow) with like-minded data scientists from around Bergen.

Welcome Mariyam!

A warm welcome to Mariyam Khan who joins us today as a PhD student!

Mariyam has a master degree in Mathematical Data Science from the University of Göttingen and will work on the NFR funded project “Intelligent systems for personalized and precise risk prediction and diagnosis of non-communicable diseases”.

The pandemic being what it is, Mariyam joins us remotely at first, and we look forward to welcoming her in Bergen properly once border restrictions ease.

Welcome again, Mariyam!

INTRePID project funded by NFR

Very pleased that the Norwegian Research Council will fund our project “Intelligent systems for personalized and precise risk prediction and diagnosis of non-communicable diseases” as part of its IKTPLUSS initiative.

The aim of this project is to create computer methods for risk prediction and diagnosis of non-communicable diseases using multi-omics data, by developing, implementing and validating novel algorithms for structure learning and inference in large-scale, multi-organ causal Bayesian gene networks. The project will integrate unique multi-omics data from three Nordic studies for a proof-of-concept application in cardiovascular medicine:

The project partners are:

Frontiers Genetics paper

Lingfei’s paper, “High-dimensional Bayesian network inference from systems genetics data using genetic node ordering” has been published in Frontiers in Genetics, in a Special Topic on Machine Learning and Network-Driven Integrative Genomics.

In this paper, we present a highly efficient approach for reconstructing Bayesian gene regulatory networks when prior information for the inclusion of edges exists or can be inferred from the available data. The method is implemented in the Findr software.