Current SNIC AI/ML Projects

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

Mathematics (3)
Computer and Information Sciences (24)
Physical Sciences (1)
Earth and Related Environmental Sciences (1)
Biological Sciences (2)
Other Natural Sciences (1)
Civil Engineering (1)
Electrical Engineering, Electronic Engineering, Information Engineering (3)
Medical Engineering (2)
Other Engineering and Technologies (1)
Basic Medicine (1)
Health Sciences (1)
Medical Biotechnology (2)
Economics and Business (1)
Political Science (1)
Media and Communications (1)

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