CV

Short bio

I am a computer scientist with B.Sc. (2007) and M.Sc. (2010) obtained from the Computer Science Dept. at Sapienza University of Rome. In 2013, I have obtained a Ph.D. from the Dept. of Information Engineering, Electronics, and Telecommunications (DIET) at Sapienza University of Rome. During my studies, I have extensively worked in the ICT industry. From January 2014 to April 2016, I have worked as Post Doctoral Fellow at Ryerson University, Canada. From May 2016 to September 2016, I have worked as Post Doctoral Fellow at Politecnico di Milano and USI (Switzerland). From 2016 to 2019, I was an Assistant Professor at the University of Exeter, UK. From 2019 to October 2023, I was an Associate Professor a Tier 2 Canada Research Chair with the Department of Computer Science at the University of Manitoba, Canada. Currently, I am the Dean and Program Head at the Open Institute of Technology (OPIT).

Research interests

Dr. Livi's main research interests lie at the intersection of machine learning and complex dynamical systems, with particular emphasis on graph-based methods. He works on both theoretical and methodological aspects of machine learning focusing on the analysis of input spaces with no trivial geometric structure, such as spaces of graphs. Dr. Livi's research relies on methods based on (spectral) graph theory, information theory, and meta-heuristic global optimization algorithms. Dr. Livi is also interested in problems involving the analysis of (real-world) complex dynamical systems by means of advanced methods of time series analysis. Examples include multi-fractal analysis of time series, recurrence analysis, and complex networks generated from time series. More recently, he started investigating (artificial) recurrent neural networks with the aim of analyzing their dynamical properties and related computational capability. In this direction, he is focusing on developing methods based on nonlinear and nonautonomous dynamical systems theory to explain the functioning of recurrent neural networks. A further current research line involves the design of methodologies for dealing with change and anomaly detection problems on sequences of attributed graphs (aka temporal networks, time-varying graphs).

Although Dr. Livi's research is mostly focused on theory and methods, he also worked on applications involving the analysis of biochemical networks and biophysical systems, with particular focus on protein molecules. He worked on both classical structure-function problems (e.g., prediction of solubility degree given protein structure) and on characterization of dynamical features like diffusion of energy and information in protein structures.

Currently, he is working on data coming from molecular dynamics simulations with the aim to exploit graph and hypergraph representations of molecules to investigate the free energy landscape of molecular systems.

Dr. Livi also collaborated on applications involving the analysis of ECG and EEG signals. The ECG data was used to develop methods for the prediction of the onset of critical heart conditions, such as atrial fibrillation and myocardial infarction. The EEG data was used to develop machine learning methods for the prediction and localization of epileptic seizures in humans.

Scientific activities and services

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