SNIC SUPR
Current SNIC AI/ML Projects

This page lists current SNIC AI/ML projects. Use the left-side menu to view other types.

Research Areas

The area is based on the three-digit level (forskningsämnesgrupp) from Standard för svensk indelning av forskningsämnen applied to the primary classification.

Mathematics (3)

Creating new ML numerical ODE/ODE solvers

Alexandros Sopasakis, Lunds universitet
2021-12-10 – 2023-01-01

Microstructures and mass transport - a machine learning approach

Magnus Röding, Chalmers tekniska högskola
2021-11-01 – 2022-11-01

Scalable optimization for machine-learning

Mikael Johansson, Kungliga Tekniska högskolan
2021-12-14 – 2023-01-01

Computer and Information Sciences (24)

AI for improved decision making in healthcare

Fredrik Johansson, Chalmers tekniska högskola
2021-09-01 – 2022-09-01

Alvis Introduction Workshop

Viktor Rehnberg, Chalmers tekniska högskola
2021-10-22 – 2022-11-01

Computational Experiments for Automatic Program Repair, Projet SELFHEALING, Prof Monperrus

Martin Monperrus, Kungliga Tekniska högskolan
2022-01-01 – 2023-01-01

Deep Learning for Natural Language Processing

Ehsan Doostmohammadi, Linköpings universitet
2021-10-07 – 2022-10-01

Deep Learning for Robot Vision

Michael Felsberg, Linköpings universitet
2021-10-01 – 2022-10-01

Deep learning models for modelling genetic variation

Carl Nettelblad, Uppsala universitet
2021-09-01 – 2022-09-01

EIStwo

Tomas Nordström, Umeå universitet
2021-12-10 – 2023-01-01

Efficient communication with Deep reinforcement learning

Emil Carlsson, Chalmers tekniska högskola
2021-12-03 – 2022-12-01

Explainable AI for Air Quality Prediction

Karl Andersson, Luleå tekniska universitet
2021-10-01 – 2022-10-01

Federated Reinforcement Learning

Hongyi Zhang, Chalmers tekniska högskola
2021-09-06 – 2022-10-01

Generative models for model based reinforcement learning

Emilio Jorge, Chalmers tekniska högskola
2021-09-15 – 2022-10-01

Geometry of Linear Regions for Deep ReLU Networks

Mårten Björkman, Kungliga Tekniska högskolan
2021-11-02 – 2022-11-01

HASTE: Hierarchical Analysis of Spatial and Temporal Data

Ankit Gupta, Uppsala universitet
2021-12-14 – 2023-01-01

ML for Computer Vision

Fredrik Kahl, Chalmers tekniska högskola
2021-11-09 – 2022-12-01

Machine learning for improved decision making

Newton Mwai Kinyanjui, Chalmers tekniska högskola
2021-09-01 – 2022-09-01

Molecular fragment similarity

Niclas Ståhl, Högskolan i Jönköping
2021-10-28 – 2022-11-01

Natural Language Processing Research (year 2)

Lovisa Hagström, Chalmers tekniska högskola
2021-11-01 – 2022-11-01

Processing of Historical Handwritten Manuscripts using Deep Learning Algorithms

Raphaela Heil, Uppsala universitet
2021-10-01 – 2022-10-01

Product Recognition Systems for Retail

Tobias Pettersson, Högskolan i Skövde
2021-11-19 – 2022-12-01

ProteinSequenceGeneration

Tobias Karlsson, Chalmers tekniska högskola
2021-09-13 – 2022-10-01

Prototyping and learning for Chalmers E-commons research engineers

Vilhelm Verendel, Chalmers tekniska högskola
2021-10-27 – 2022-11-01

Resource Efficent AI in Networks

Ali Beikmohammadi, Stockholms universitet
2021-10-22 – 2022-11-01

Structured multilinguality for natural language processing

Robert Östling, Stockholms universitet
2021-10-27 – 2022-11-01

Transferable concepts

Adam Breitholtz, Chalmers tekniska högskola
2021-09-01 – 2022-09-01

Physical Sciences (1)

Machine learning for topological codes and topological matter

Mats Granath, Göteborgs universitet
2021-09-01 – 2022-09-01

Earth and Related Environmental Sciences (1)

Assessing Deep Learning Models for Large-Scale Burn Severity Mapping with 10 Years' Landsat Satellite Data

Xikun Hu, Kungliga Tekniska högskolan
2021-09-21 – 2022-10-01

Biological Sciences (2)

A system for automated analysis of subsea movies using citizen science and machine learning

Matthias Obst, Göteborgs universitet
2022-01-01 – 2023-01-01

Alphafold-assisted enzyme discovery and characterization

Johan Larsbrink, Chalmers tekniska högskola
2021-10-22 – 2022-11-01

Other Natural Sciences (1)

Gauge Equivariant Convolutional Neural Networks

Daniel Persson, Chalmers tekniska högskola
2021-11-01 – 2022-11-01

Civil Engineering (1)

Modeling traffic flow using deep learning methods

Hossein Ehteshami, Uppsala universitet
2021-08-26 – 2022-09-01

Electrical Engineering, Electronic Engineering, Information Engineering (3)

Machine learning for dynamic obstacle predictions

Knut Åkesson, Chalmers tekniska högskola
2021-10-22 – 2022-11-01

Motions and voice

Andreas Jakobsson, Lunds universitet
2021-12-03 – 2022-12-01

Semi-supervised Learning for Medical Image Analysis

Roman Naeem, Chalmers tekniska högskola
2021-12-30 – 2023-01-01

Medical Engineering (2)

Implementation of innovative image analysis tools in the radiological workflow

Rolf A Heckemann, Göteborgs universitet
2022-01-01 – 2023-01-01

Trustworthy AI-based decision support in cancer diagnostics

Joakim Lindblad, Uppsala universitet
2021-11-05 – 2022-12-01

Other Engineering and Technologies (1)

Explainable deep learning methods for human-human and human-robot interaction

Ginevra Castellano, Uppsala universitet
2021-12-13 – 2022-12-01

Basic Medicine (1)

AlphaFold2 for predicting protein interaction surfaces

Gemma Atkinson, Lunds universitet
2021-11-17 – 2022-12-01

Health Sciences (1)

Deep learning, Machine learning, and Bayesian computational in epidemiology

Rani Basna, Göteborgs universitet
2021-10-01 – 2022-10-01

Medical Biotechnology (2)

Deep Autoencoder Neural Network for identification of Doxorubicin-induced gene expression profile in stem cell-based cardiomyocytes

Dario Melguizo Sanchis, Högskolan i Skövde
2021-10-08 – 2022-11-01

new VAE formulations for single cell analysis

Johan Henriksson, Umeå universitet
2021-11-09 – 2022-12-01

Economics and Business (1)

Project on solving high-dimentional dynamic model by deep learning

Tianze Liu, Uppsala universitet
2021-12-10 – 2023-01-01

Political Science (1)

Methods for the analysis of spreading phenomena in networks, with a focus on the online spreading of political ideas through visual content

Matteo Magnani, Uppsala universitet
2022-01-01 – 2023-01-01

Media and Communications (1)

Using machine learning to analyze content aspects of online political influence campaigns

Nils Holmberg, Lunds universitet
2022-01-01 – 2023-01-01