Category: Prize

Mirja Granfors received the Best Early Career Researcher Presentation Award at Emerging Topics in Artificial Intelligence (ETAI) 2025 held in San Diego, from 3 to 7 August 2025.

The award, which includes a certificate, a cash prize of $300, and a T-shirt, is presented by the organizers of the conference in collaboration with SPIE Optics + Photonics.

Mirja was awarded the prize for her presentation titled “DeepTrack2: physics-based microscopy simulations for deep learning”. Below is the full abstract of her presentation:

DeepTrack2 is a flexible and scalable Python library designed to generate physics-based synthetic microscopy datasets for training deep learning models. It supports a wide range of imaging modalities, including brightfield, fluorescence, darkfield, and holography, enabling the creation of synthetic samples that accurately replicate real experimental conditions. Its modular architecture empowers users to customize optical systems, incorporate optical aberrations and noise, simulate diverse objects across various imaging scenarios, and apply image augmentations. DeepTrack2 is accompanied by a dedicated GitHub page, providing extensive documentation, examples, and an active community for support and collaboration: https://github.com/DeepTrackAI/DeepTrack2.

Mirja Granfors won the best early career researcher presentation award at AnDi+ 2025 workshop (AI for Bioimaging Beyond Trajectory Analysis) held in Gothenburg, from 2 June – 5 June 2025.

The award, consisting of a certificate and a cash prize of 250€, is sponsored by Nanophotonics.

Mirja was awarded the prize for her presentation titled “DeepTrack2: Physics-based Microscopy Simulations for Deep Learning & Deeplay: Enhancing PyTorch with Customizable and Reusable Neural Networks”. In her presentation, she presented the Python libraries DeepTrack2 and Deeplay, both developed by the Soft Matter Lab to support AI-driven microscopy.

DeepTrack2 is a flexible and scalable Python library designed to generate physics-based synthetic microscopy datasets for training deep learning models. It supports a wide range of imaging modalities, including brightfield, fluorescence, darkfield, and holography, enabling the creation of synthetic samples that accurately replicate real experimental conditions. Its modular architecture empowers users to customize optical systems, incorporate optical aberrations and noise, simulate diverse objects across various imaging scenarios, and apply image augmentations.

Deeplay is a flexible Python library for deep learning that simplifies the definition and optimization of neural networks. It provides an intuitive framework that makes it easy to define and train models. With its modular design, Deeplay enables users to efficiently build and refine complex neural network architectures by seamlessly integrating reusable components.

In this poster, Mirja presented her recent work on the development of a graph autoencoder. This graph autoencoder effectively summarizes graph structures while preserving important topological details through multiple hierarchical pooling steps. This enables the extraction of physical parameters describing the graphs. She demonstrated the performance of the graph autoencoder across diverse graph data originating from complicated systems, including the classification of protein assembly structures from single-molecule localization microscopy data, as well as the analysis of collective behavior and correlations between brain connections and age.

The award, consisting of a certificate, and a cash prize of 250 €, is sponsored by Nanophotonics.

Mirja was awarded the prize for her presentation titled “Global graph features unveiled by unsupervised geometric deep learning”. In her presentation, she introduced a novel graph autoencoder designed to capture complex relationships modelled by graphs. She demonstrated the performance of the network across a spectrum of datasets, including the classification of protein assembly structures from single-molecule localization microscopy data, as well as the analysis of collective behavior and correlations between brain connections and age.