Projects and Collaborators

research lines

Opening the Black Box of Deep Learning via Coupled Dynamical Systems

Deep learning models achieve impressive performance across many domains, yet their internal mechanisms remain largely opaque. Learning is typically framed as a purely optimization-driven process, and trained models are often treated as black boxes, making their behavior difficult to interpret, predict, or control, especially in nonstationary and continually evolving settings.

This research program adopts a dynamical perspective on deep learning, viewing models as systems in which fast-evolving internal states and slowly adapting parameters interact over multiple time scales. From this viewpoint, learning, adaptation, and forgetting emerge from the joint dynamics of states and parameters, rather than from parameter optimization alone. Tasks are not encoded as static objects, but as structured dynamical configurations whose stability and persistence depend on time-scale structure, gradient propagation, and stochastic training effects.

Recurrent neural networks provide a natural starting point due to their close connection with dynamical systems and temporal processing. However, the underlying principles extend to a wide range of modern architectures that rely on gating, multiplicative modulation, and depth-induced time-scale separation. By opening the black box of deep learning through the lens of coupled dynamical systems, this research aims to provide mechanistic insight into how models learn tasks, adapt to new information, and sometimes forget, laying the groundwork for more stable, interpretable, and energy-efficient learning algorithms.

References

Analysis of graph sequences and graph-structured data

Today, it is possible to observe natural and man-made complex systems (e.g., protein and metabolic networks, smart grids, brain networks) involving spatio-temporal interactions of many elements on multiple scales. A prominent example is provided by the brain and the possibility to simultaneously record the activity of neurons from multiple electrodes. A first step toward understanding such systems from data requires the use of complex data representations, such as sequences of graphs, for encoding their spatio-temporal behaviour. Accordingly, data-driven procedures, like prediction and change detection methods, need to be designed with the ability to process sequences of graph-structured data.

References

Prediction and generation of molecular structures

Understanding the thermodynamics and kinetics of protein-ligand interactions play a fundamental role in protein science and in the early stages of drug discovery. From a thermodynamic point of view, protein-ligand interactions are described by the binding free-energy surface (BFES). Due to the numerous possible structural configurations assumed by both proteins and ligands, such a surface is high-dimensional and non-linear, and its accurate computation requires significant computational resources. On the other hand, while research on protein-ligand interaction focused on computing the most likely binding pose (represented by the lower energy minima in the BFES), more recently it has been discovered that other factors contribute to the effectiveness of their interactions over time. These factors are typically described by the so-called dissociation rate, which is a property quantifying the time-dependent stability of the ligand-protein interaction. Such a property can be investigated by analyzing particular saddle points of the BFES. However, such special saddles represent transition states that are elusive to both simulations and experiments and thus difficult to characterize.

Recently, machine learning methodologies based on deep neural networks yielded fundamental breakthroughs in several scientific and technological fields. Of particular interest are graph (convolutional) neural networks, which allow us to process graph-data as structured inputs by means of conventional neural processing mechanisms. Combined with the possibility to produce structured outputs through generative models, modern neural networks allow us to explore the high-dimensional conformational space proper of chemical structures and hence to investigate complex protein-ligand binding mechanisms.

References

The goal of this research project is to develop a framework for the diagnostics and control of a GGP, leveraging state-of-the-art models for deep learning on graphs to automatically deal with the complexity of the graph space. Although the framework is general, we will focus on the context of epileptic seizures and neuromodulation by analysing intracranial EEGs recorded from drug-resistant patients.

The research project is composed of four different milestones, tightly related to each other but addressing different learning tasks:

• M1: The prediction of events occurring in a GGP, developing models that are able to operate both in supervised and unsupervised learning settings

• M2: The localisation of nodes and edge that are involved in an event, as well as the identification of the salient time steps in the sequence that led to that event

• M3: The explanation of an event through causal inference, using the information extracted in M2 to build a chain of cause-effect relationships that led to a particular event

• M4: The control of a GGP to prevent or resolve undesired events, including the identification of the state space, action space, and reward functions of the process, by using control theory and reinforcement learning to learn a behaviour policy for the GGP

References

Collaborators

• Dr. Cesare Alippi, Politecnico di Milano, Italy, and Universita' della Svizzera italiana, Switzerland

• Dr. Robert Jenssen and Dr. Filippo Maria Bianchi, UiT the Arctic University of Norway

• Dr. Peter Ashwin and Dr. Krasimira Tsaneva-Atanasova, University of Exeter, UK

• Dr. Taufik Valiante and Dr. David Groppe, University of Toronto, Canada

• Dr. Naoki Masuda, University at Buffalo, USA

• Dr. Vittorio Limongelli, Universita' della Svizzera italiana, Switzerland

• Dr. Stanislaw Drożdż and Dr. Pawel Oświȩcimka, Polish Academy of Sciences, Poland

• Dr. Alessandro Giuliani, Istituto Superiore di Sanita', Italy

• Dr. Witold Pedrycz, Universit of Alberta, Canada