Research

Graph Statistical Framework

We introduce surrogate framework based on stationary process and give guiding examples and an application in which we compare performance of our framework with existing undirected graph surrogates and show that graph phase randomized surrogates are more suitable to account for directionality. This framework is valuable in the study of network communication and with application to fields such as neuroimaging where biological priors dictate the directionality of graph edges

  • Paper: Chan, C. H. M., Cionca, A., & Van De Ville, D. . In preparation (2025).

Graph Hilbert Transform

We propose a generalization of the Hilbert transform interpreted over the newfound cycle cover, which re-establishes intuitions from traditional Hilbert transform, equivalent to the generalized Hilbert transform on a single cycle. This generalization leads to a number of simple and elegant recipes to effectively exploit the phase information of graph signals provided by the graph Fourier transform. The feasibility of the approach is demonstrated on several examples.

  • Paper: Chan, C. H. M., Cionca, A., & Van De Ville, D. (2025). Hilbert Transform on Graphs: Let There Be Phase. IEEE Signal Processing Letters.

Community detection for directed networks revisited using bimodularity

Here, we leverage recent work on community detection for directed graphs and propose a community-driven signal processing approach.

  • Paper: Cionca, A., Chan, C. H. M., & Van De Ville, D. Community detection for directed networks revisited using bimodularity. Submitted to PNAS (2025).

Community-Driven Signal Processing On Directed Brain Graphs

Here, we leverage recent work on community detection for directed graphs and propose a community-driven signal processing approach.

  • Paper: Cionca, A.*, Chan, C. H. M.*, Preti, M. G., Jedynak, M., Gomez, Y. A., David, O., Hagmann, P. & Van De Ville, D. . *co-first author. Community-Driven Signal Processing On Directed Brain Graphs (2025) 33rd European Signal Processing Conference (EUSIPCO 2025). IEEE.

Encoding and Decoding brain activity while watching movies

We propose an end-to-end deep neural encoder-decoder model to encode and decode brain activity in response to naturalistic stimuli using functional magnetic resonance imaging (fMRI) data.

  • Paper: David, F.*, Chan, C. H. M.*, Morgenroth, E., Vuilleumier, P., & Van De Ville, D. . *co-first author. Deep Neural Encoder-Decoder Model to Relate fMRI Brain Activity with Naturalistic Stimuli (2025) 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE.

Emotion-Informed Brain Functional Gradients

In this work we study the relation between individual differences, in particular, the state anxiety and the openness scores, and brain activity during the processing of various emotional scenes in films, through functional gradients.

  • Paper: Chan, C. H. M., Vilaclara, L., Vuilleumier, P., Van De Ville, D., & Morgenroth, E. . Individual differences of cortical and subcortical emotion-informed functional gradient. Submitted to Imaging Neuroscience (2024)

Digital, Analog, or Hybrid: Comparing Strategies to Support Self-Reflection

Our objective was to build an better understanding of design paradigms’ role in introspection. Through formative itera- tions, informed by Self-Determination Theory (SDT), we designed and developed diferent tool formulations (Analogue, Digital, and Hybrid) for comparison.

  • Paper: Arnera, J., Chan, C. H. M., & Cherubini, M. . Digital, Analog, or Hybrid: Comparing Strategies to Support Self-Reflection. Proceedings of the 2024 ACM Designing Interactive Systems Conference

A convolutional neural network segments yeast microscopy images with high accuracy

We present a convolutional neural network (CNN) named YeaZ, the underlying training set of high-quality segmented yeast images (>10 000 cells) including mutants, stressed cells, and time courses, as well as a graphical user interface and a web application (www.quantsysbio.com/data-and-software) to efficiently employ, test, and expand the system

  • Paper: Dietler, N., Minder, M. ..., Chan, C. H. M., ... & Rahi, S. J. . A convolutional neural network segments yeast microscopy images with high accuracy. Nature communications (2020)