Team

Massimo Panella (massimo.panella@uniroma1.it) was born in Rome, Italy, in 1971. He received the five-year Dr.Eng. degree with honors in Electronic Engineering in 1998 and the Ph.D. degree in Information and Communication Engineering in 2002, both from the University of Rome "La Sapienza", Italy. He is currently Director of the Department of Information Engineering, Electronics and Telecommunications (DIET) of the University of Rome "La Sapienza", where he is Full Professor holding courses on electrical engineering and applied machine learning. Effective March 2018, he is also qualified to the role of Full Professor in Computer Science Engineering. His research activities concern computational intelligence and quantum computing for the modeling, optimization and control of real-world systems, namely the use of neural networks, fuzzy logic, evolutionary algorithms and quantum circuits for solving both supervised and unsupervised learning problems, for time series analysis, and for the general processing of signals and data. Applications range from the development of learning algorithms in federated and distributed environments to the design of computational models and architectures for deep learning, from the implementation of neural networks in smart sensors and embedded systems to the synthesis of circuits and algorithms for quantum machine learning. The application areas mainly focus on energy, ICT, bioengineering, economy, aerospace and security, especially considering complex systems and networked services such as smart grids, IoT, logistics, smart sensor networks, etc.

Antonello Rosato (antonello.rosato@uniroma1.it) was born in 1990. He received his Telecommunication Engineering degree (M.Sc.) with Honors from the University of Rome “La Sapienza”, Italy, in 2015. In 2018, he received the Ph.D. degree in Information and Communications Technologies from the same university. He is currently an Assistant Professor (RTD-A) at the Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome “La Sapienza”. His research interests include machine learning techniques for prediction of complex behaviors, neural and fuzzy-neural models and systems, distributed clustering algorithms, randomized neural networks performance. His current activities are in the field of applied deep learning for practical implementation in the energy and biomedical domains.

Alessio Fioravanti (alessio.fioravanti@uniroma1.it) was born in Rome, Italy, in 1978. He obtained the five-year degree in Computer Engineering in 2006 at the University of Rome "La Sapienza", Italy, and a PhD in Biomedical Engineering in 2016 at the Polytechnic University of Madrid, Spain. His experience is focused on technological platforms for monitoring people with chronic diseases through intelligent biomedical sensors, wireless technologies and smart platforms. He has worked on communication protocols in the field of telemedicine, interoperability of biomedical devices and communication between sensors. He is also an expert in systems based on vision and augmented reality techniques, as well as on Image Object Detection systems based on machine learning techniques. He joined as technical manager various national and European research and development projects. He is currently Research Fellow at the Department of Information, Electronics and Telecommunications Engineering (DIET) of the University of Rome "La Sapienza", Italy.

Federico Succetti (federico.succetti@uniroma1.it) was born in Rome in 1992. He received both his B.Sc. and M.Sc. in Electronic Engineering from the University of Rome “La Sapienza”, Italy, in May 2016 and October 2019, respectively. From November 2019 to October 2021, he worked as a Research Fellow at the University of Rome “La Sapienza”. His main task was the development of deep learning algorithms for the management and control of complex systems. He has gained specific experience in the development of deep neural networks for time series prediction purposes in the renewable energy field. In November 2021, he started his Ph.D. in Information and Communication Technologies at the University of Rome “La Sapienza”.

Francesco Di Luzio (francesco.diluzio@uniroma1.it) was born in 1996. He received in 2018 the bachelor degree in Industrial Engineering and in 2020 the master degree in Industrial Engineering (Business Intelligence and Analytics curriculum), both from the University of Rome “La Sapienza”, where he is currently a research fellow on topics relating to deep learning and explainable AI applied to the analysis of multivariate and multimedia data in real-world applications. His research interests include machine learning and deep learning algorithms and models for emotion recognition and behavioral analysis, computational intelligence, and time series prediction in the renewable energy sources field.

Andrea Ceschini (andrea.ceschini@uniroma1.it) was born in 1996. He received his Management Engineering degree (M.Sc.) with Honors from the University of Rome “La Sapienza”, Italy, in 2020. In the same year, he obtained the Professional Engineer Qualification. Currently, he is studying for the Ph.D. degree in Information and Communications Technologies from the same university. He is performing his research at the Dept. of Information Engineering, Electronics and Telecommunications (DIET). His research interests include quantum machine learning algorithms on Noisy Intermediate-Scale Quantum (NISQ) devices, machine learning techniques for prediction of energy time series, neural circuit models and systems, signal processing algorithms on big data. His ongoing activities concern the development of novel quantum and quantum-inspired neural networks, as well as the implementation of deep learning models in the energy and biomedical domains.

Alessio Verdone (alessio.verdone@uniroma1.it) was born in Rome, Italy, in 1994. He obtained the Bachelor's degree in Management Engineering in 2017 and the Master degree with honors in Artificial Intelligence and Robotics in 2020 at the University of Rome "La Sapienza", Italy. In 2021, he worked at DXC as a web developer. At the end of the same year he began the Ph.D. in Information and Communication Technologies (ICT) at the Department of Information, Electronics and Telecommunications Engineering (DIET) of the University of Rome "La Sapienza", Italy. His current research interests concern the study and development of Deep Learning algorithms, in particular Graph Neural Networks, applied to renewable energy contexts.

Federica Colonnese (federica.colonnese@uniroma1.it) was born in Rome, Italy, in 1997. She received both the bachelor degree in Industrial Engineering in 2020 and then the Master Degree in Industrial Engineering (Business Intelligence & Analytics curriculum) in 2022 from University of Rome “La Sapienza”, Italy. She is currently a Ph.D. student in Information and Communications Technologies from the same University. Her main interests concern the development of machine learning and deep learning models applied to the biomedical and neuropsychiatric field.

Francesca De Falco (francesca.defalco@uniroma1.it) was born in Rome, Italy, in 1999. She received both the Bachelor’s degree with honors in Electronic Engineering in 2021 and then the Master’s degree with honors in Electronic Engineering in 2023 from University of Rome “La Sapienza”, Italy. She is currently a Ph.D. student in Information and Communications Technologies from the same University. Her main interests concern the development of quantum machine learning algorithms with a particular attention to quantum generative models. 

Simone Piperno (simone.piperno@uniroma1.it) was born in Rome, Italy, in 1998. He received his Bachelor's degree in Physics in 2020 and his Master's degree in Data Science in 2023, both from the University of Rome "La Sapienza". He is currently a Ph.D. student at the Department of Information Engineering, Electronics and Telecommunications (DIET). His research interests concern quantum machine learning algorithms on Noisy Intermediate-Scale Quantum (NISQ) devices, in particular, he has so far worked on Quantum Graph Neural Networks applied to molecular physics problems and is currently working on Quantum Simplicial Neural Networks.

Leonardo Lavagna (leonardo.lavagna@uniroma1.it) was born in 1995. He graduated in mathematics with a specialization in Data Science from the University of Rome "La Sapienza." Currently, he is a Ph.D. student in Information and Communication Technologies (ICT) at the Department of Information Engineering, Electronics, and Telecommunications (DIET) of the University of Rome "La Sapienza." Before embarking on his doctoral journey, he worked in consulting in the pharmaceutical and biotechnology industry as an expert in statistical modeling and machine learning. His research interests encompass the evolution processes in quantum computers (from encoding techniques to the definition of observables and related measurements) and the corresponding algorithms. He also focuses on quantum optimization, particularly in the context of variational circuits in hybrid settings and techniques inspired by thermodynamics (adiabatic computing, quantum annealing, etc.). Additionally, he explores quantum information theory and its applications to machine learning models.