We are happy to announce that Giacomo Verardo successfully defended his doctoral thesis on May 20, 2026! As usual, Assoc. Prof. Marco Chiesa has done a great job as a co-advisor, and we are especially grateful to Prof. Gerald Q. Maguire Jr. for his continued stellar insights. Prof. Thomas Schön (Uppsala University) has done an outstanding job as the opponent, and Assoc. Prof. Artur Podobas served as the chairperson. Giacomo ’s thesis is available online:

“Optimizing and Adapting Neural Network Models for Healthcare and Federated Learning”

Below are a few images from the event.

Giacomo during the discussion with Prof. Thomas Schön, with grading board members Maxime Sermesant (Inria) and Giovanna Sannino (ICAR-CNR) participating over Zoom (photo taken by Marco Chiesa).

Giacomo during the discussion with Prof. Thomas Schön (photo taken by Marco Chiesa).

Assoc. Prof. Nicolai Spicher (TU Denmark), chair of the grading board, hands the diploma to Giacomo (Zoom screengrab)

Giacomo with the opponent, and committee plus Dejan over Zoom, celebrating the announcement that he passed (photo taken by Artur Podobas who is also visible on the screen!)

Giacomo attended  the STACOM (Statistical Atlases and Computational Modelling of the Heart)  workshop to present our paper titled: “Reducing the number of leads for ECG Imaging with Graph Neural Networks and meaningful latent space”. The work demonstrates a potential for removing a large number of leads (more than half!) during ECG Imaging.

This is joint work with Giacomo Verardo (KTH),  Daniel F. Perez-Ramirez, (RISE/KTH), Samuel Bruchfeld (KI),  Magnus Boman (KI), Marco Chiesa (KTH), Sabine Koch (KI), Gerald Q. Maguire Jr. (KTH), and Dejan Kostic (KTH).

The paper is available here, while the full abstract is below:

ECG Imaging (ECGI) is a technique for cardiac electrophysiology that allows reconstructing the electrical propagation through different parts of the heart using electrodes on the body surface. Although ECGI is non-invasive, it has not become clinically routine due to the large number of leads required to produce a fine-grained estimate of the cardiac activation map. Using fewer leads could make ECGI practical for clinical patient care. We propose to tackle the lead reduction problem by enhancing Neural Network (NN) models with Graph Neural Network (GNN)-enhanced gating. Our approach encodes the leads into a meaningful representation and then gates the latent space with a GNN. In our evaluation with a state-of-the-art dataset, we show that keeping only the most important leads does not increase the cardiac reconstruction and onset detection error. Despite dropping almost 140 leads out of 260, our model achieves the same performance as another NN baseline while reducing the number of leads. Our code is available at github.com/giacomoverardo/ecg-imaging.

We are happy to announce that at ICPRAI 2024 Giacomo presented our paper titled “FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior knowledge”. This work shows the benefit of taking the underlying model of the heart (via Frequency Modulated Möbius waves) into account when performing ECG anomaly detection. Our model achieves up to 0.31 increase in area under the ROC curve (AUROC) when compared to the state of the art models. Moreover, the processing time of our model is four orders of magnitude lower than solving an optimization problem to obtain the same parameters, thus making it suitable for real-time ECG parameter extraction and anomaly detection.

This is joint work with Giacomo Verardo (KTH), Magnus Boman (KI), Samuel Bruchfeld (KI), Marco Chiesa (KTH), Sabine Koch (KI), Gerald Q. Maguire Jr. (KTH), and Dejan Kostic (KTH).

The paper received an Honorable Mention in the competition for the Best Paper Award, and is available at this link, while the full abstract is below:

Detecting anomalies in electrocardiogram data is crucial to identify deviations from normal heartbeat patterns and provide timely intervention to at-risk patients. Various AutoEncoder models (AE) have been proposed to tackle the anomaly detection task with ML. However, these models do not explicitly consider the specific patterns of ECG leads, thus compromising learning efficiency. In contrast, we replace the decoding part of the AE with a reconstruction head (namely, FMM-Head) based on prior knowledge of the ECG shape. Our model consistently achieves higher anomaly detection capabilities than state-of-the-art models, up to 0.31 increase in area under the ROC curve (AUROC), with as little as half the original model size and explainable extracted features. The processing time of our model is four orders of magnitude lower than solving an optimization problem to obtain the same parameters, thus making it suitable for real-time ECG parameters extraction and anomaly detection. The code is available at: https://github.com/giacomoverardo/FMM-Head.

We are happy to announce that Giacomo Verardo successfully defended his licentiate thesis (licentiate is a degree at KTH half-way to a PhD)! As usual, Marco Chiesa has done an excellent job as a co-advisor, and we are truly grateful to Prof. Gerald Q. Maguire Jr. for his stellar insights. Dr. Maxime Sermesant was a superb opponent at the licentiate seminar, with Prof. Vlad Vlassov as the examiner. Giacomo ’s thesis is available online:

“Optimizing Neural Network Models for Healthcare and Federated Learning”

Giacomo presenting during the defense (image taken by Massimo Girondi).

Dejan congratulates Giacomo once Prof. Vlassov announced that Giacomo passed his defense (image taken by Massimo Girondi).

Dejan hands the traditional gift to Giacomo (image taken by Voravit Tanyingyong).

Group image with Networked Systems Laboratory members (image taken by Sanna Jarl).

We are excited to announce that in June 2022, Giacomo Verardo presented our  first paper (in this project) titled “Fast Server Learning Rate Tuning for Coded Federated Dropout“ at the International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2022 (FL-IJCAI’22).

This is joint work with Giacomo Verardo, Daniel Barreira, Marco Chiesa, Dejan Kostic, and Gerald Quentin Maguire Jr.. Full abstract is below:

In cross-device Federated Learning (FL), clients with low computational power train a common machine model by exchanging parameters via updates instead of potentially private data. Federated Dropout (FD) is a technique that improves the communication efficiency of a FL session by selecting a subset of model parameters to be updated in each training round. However, compared to standard FL, FD produces considerably lower accuracy and faces a longer convergence time. In this paper, we leverage coding theory to enhance FD by allowing different sub-models to be used at each client. We also show that by carefully tuning the server learning rate hyper-parameter, we can achieve higher training speed while also achieving up to the same final accuracy as the no dropout case. For the EMNIST dataset, our mechanism achieves 99.6% of the final accuracy of the no dropout case while requiring 2.43x less bandwidth to achieve this level of accuracy.