Maria Evelina Mognaschi is an Associate Professor at the Department of Industrial and Information Engineering, Faculty of Engineering, University of Pavia. Her research focuses on finite element simulation of electrical and magnetic devices, particularly in biomedical applications, and the automatic optimization of industrial electromagnetic systems. She also explores the integration of deep learning techniques for solving identification problems and developing surrogate models in electromagnetic fields. Her work bridges computational electromagnetics with machine learning, emphasizing industrial and biomedical applications. She is affiliated with the CAD Lab at the university and contributes to educational programs through course development and exam management.
Tommaso Cucinotta is an Associate Professor at the Real-Time Systems Laboratory (ReTiS) within the TECIP Institute of Scuola Superiore Sant'Anna, Pisa, Italy. He earned a MSc and PhD in Computer Engineering from University of Pisa and Scuola Superiore Sant'Anna, respectively. His career spans academic and industrial roles, including researcher positions at Alcatel-Lucent Bell Labs (2012-2014) and Software Development Engineer at Amazon DynamoDB (2014-2016). He coordinates real-time and embedded systems research at ReTiS since 2019. Born in 1974, Potenza, Italy MSc in Computer Engineering, University of Pisa (2000) with 110 cum laude PhD in Computer Engineering, Scuola Superiore Sant'Anna (2004) His research focuses on real-time systems in cloud environments, including adaptive resource management, AI-driven performance monitoring, secure computing, and scalable NoSQL databases. He explores operating system innovations for many-core architectures, network function virtualization (NFV) optimization, and kernel-level enhancements for latency control. His work integrates formal methods with practical implementations, such as autonomic QoS control and high-performance container communication frameworks. Recent publications analyze predictive elasticity in cloud infrastructures, real-time DAG optimization on heterogeneous platforms, and AI applications for system-level performance tuning. He actively contributes to open-source tools like ARSim and AQuoSA, while mentoring MSc thesis projects on topics like Kubernetes optimization, fault-tolerant replication logs, and machine unlearning techniques for LLMs. Collaborations with industry leaders (Ericsson, Red Hat, Vodafone) bridge academic research with real-world scalability challenges. Scientific awards include the Best Paper Award at CLOSER 2020 for his work on high-performance inter-container communication frameworks. He participates in program committees of major conferences and contributes to the evolution of Linux real-time scheduling mechanisms through projects like SCHED_DEADLINE enhancements for multimedia applications.
Giovanna Paola Varni is an Associate Professor at the University of Trento , affiliated with the Department of Information Engineering and Computer Science. She actively teaches courses in Human-Computer Interaction and web programming, focusing on multimodal systems and foundational web application development. Research Interests: Design of systems processing heterogeneous (multimodal) data for intuitive human-machine interaction Integration of motoric, emotional, linguistic, and social human communication modalities Web application development paradigms, including REST and asynchronous JavaScript Teaching Activities: Advanced HCI : Covers multimodal interaction frameworks, algorithmic analysis of human behavior, and system implementation. Introduzione alla Programmazione per il web : Teaches HTTP protocols, DOM manipulation, and secure web application design. Programmazione 1 : Focuses on imperative programming fundamentals using C++.
Mario Roberto Casu is an Associate Professor in the Department of Electronics and Telecommunications (DET) at the Polytechnic University of Turin, where he also serves as a contact person for the Degree Course in Electronic Engineering. He is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory and actively contributes to the VLSILAB research group. Dr. Casu received his laurea degree summa cum laude in electronics engineering and his Ph.D. in electronics and communications engineering from the Polytechnic University of Turin in 1998 and 2001, respectively. He has held visiting researcher positions at Columbia University (2010-2011), National University of Singapore (2017), and CEA Grenoble (2001), as well as a visiting professorship at Chongqing Technology and Business University (2016). His research spans several interconnected domains focused on hardware implementation of advanced computing systems. Dr. Casu's work primarily addresses Embedded Machine Learning through heterogeneous embedded systems (ASICs, FPGAs, CPUs, GPUs), System-on-Chip design including latency-insensitive approaches and Network-on-Chip architectures, Microwave Imaging for both biomedical (breast cancer and stroke detection) and industrial applications (food contamination detection), and Ultra-Wide Band technologies for biomedical applications. His research bridges theoretical design methodologies with practical industrial applications across biomedical, automotive, and food sectors. Dr. Casu's recent scholarly output demonstrates a clear trajectory toward optimizing hardware implementations for machine learning workloads, particularly through FPGA-based solutions and precision-scalable multipliers. His work increasingly integrates microwave sensing technologies with machine learning for specialized applications like food contaminant detection, while maintaining strong foundations in traditional VLSI design and system-level optimization techniques. As an academic leader, Dr. Casu serves on the editorial board of IEEE TRANSACTIONS ON AGRIFOOD ELECTRONICS and regularly participates in program committees for major international conferences including DATE, ICCAD, DAC, and VLSI-SoC. He has been involved in 9 national academic research projects (2 as principal investigator), 2 European academic research projects, and 7 national and international industrial projects (2 as principal investigator). Dr. Casu actively mentors the next generation of engineers, currently supervising multiple PhD students including Lorenzo Lagostina, Edward Manca, Teodoro Urso, Fabrizio Ottati, and Luca Urbinati. His teaching portfolio includes courses such as Integrated Systems Technology, Microelectronics Digital Design, and Embedded Electronic Systems for AI/ML across both bachelor's and master's programs in Electronic and Computer Engineering. His laboratory work centers around the VLSILAB Group at DET, where his team develops innovative solutions in hardware acceleration for machine learning, microwave imaging systems, and system-level design methodologies. Current projects include the EU-funded GreenChips-EDU initiative for sustainable microelectronics education and industry collaborations with companies like Infineon Technologies on coarse-grained reconfigurable array architectures for machine learning applications.
Luca Mesin serves as an Associate Professor in the Department of Electronics and Telecommunications (DET) at Polytechnic University of Turin, where he is also a member of the Interdepartmental Center PolitoBIOMed Lab - Biomedical Engineering Lab. His academic appointment falls under the scientific disciplinary sector IBIO-01/A - Bioengineering within Area 0009 - Industrial and Information Engineering. Mesin's research spans multiple domains of biomedical engineering with particular expertise in biomedical signal processing. His primary research interests include brain-computer interfaces, electroencephalography (EEG), electrocardiography (ECG), magnetic resonance imaging (MRI), neuroscience applications, surface electromyography (EMG), and ultrasound imaging. His work focuses on developing innovative signal processing methods for medical diagnostics and human-machine interaction systems, with specific applications in neurological disorders, cardiovascular monitoring, and non-invasive patient assessment. His recent publications demonstrate a strong trend toward integrating machine learning with biomedical signal processing, particularly in EEG analysis for brain-computer interfaces and mental stress assessment. The research shows increasing emphasis on practical clinical applications, security aspects of neural interfaces, and development of non-invasive monitoring techniques for patient volume status and cardiovascular parameters. Best paper award of the 4th IET International Conference on Advances in Medical, Signal and Information Processing (MEDSIP 2008) SIAMOC Best Methodology Paper Award 2007 Featured article of Communications in Theoretical Physics for 2013 'Highlight Paper of the Year' by Computers in Biology and Medicine (2013) High score abstract at EuroEcho 2019 Mesin actively supervises multiple PhD students working on cutting-edge biomedical engineering projects including brain-to-brain communication, venous pulsatility analysis, and smart wearable technologies for stress monitoring. He leads significant research projects including PELVITRACK (2025-2029), funded by the European Innovation Council, and MACIVB (2020-2021), focusing on non-invasive vascular imaging techniques. His research has resulted in multiple patents related to biomedical devices and signal processing algorithms. As a key member of the Mathematical Biology and Physiology research group within DET, Mesin contributes to advancing the field through his leadership in the PolitoBIOMed Lab and through his editorial roles with journals including BIOENGINEERING, JOURNAL OF CLINICAL MEDICINE, and FRONTIERS IN PHYSIOLOGY.
Diego Valsesia is a Fixed-Term Researcher at the Department of Electronics and Telecommunications (DET) at Politecnico di Torino. His work focuses on deep learning and artificial intelligence, particularly in image and signal processing applications. Research Interests: Computer vision, remote sensing, generative models, neural radiance fields, and satellite image super-resolution Teaching: Course instructor for adversarial training of neural networks and collaborator in machine learning courses across Electrical, Electronics, and Communications Engineering programs His research has led to competitive projects like LICAM (AI-powered LiDAR fusion for smartphone cameras) and national PRIN grants. He supervises PhD students in multi-modal image processing and onboard satellite AI. Recent publications highlight trends in neural implicit representations, multi-image super-resolution, and self-supervised learning for satellite systems. His work bridges theoretical advancements with practical applications in remote sensing and 3D vision. Scientific Recognitions: Outstanding Editorial Board Member, IEEE Transactions on Image Processing (2023) Best Paper Awards at IEEE ICIP 2019, IEEE Multimedia (2020) Proba-V Superresolution Challenge (2019), MMSP Workshop (2013) He co-developed multiple international patents for camera identification and image processing, and serves as Associate Editor for IEEE Transactions on Image Processing.
Matteo Cianchetti serves as Associate Professor at the BioRobotics Institute of Sant'Anna School of Advanced Studies, where he leads the Soft Mechatronics for Biorobotics Lab with a 13-member research team. His work centers on developing soft robotics technologies for biorobotic applications spanning healthcare, surgery, and industrial automation. His academic foundation includes: MSc in Biomedical Engineering (cum laude), University of Pisa, 2007 PhD in Biorobotics (cum laude), Scuola Superiore Sant'Anna Dr. Cianchetti's research pioneers Soft Mechatronics for Biorobotics , focusing on soft actuators, compliant sensors, and flexible mechanisms. His work drives innovation in: Assistive robotics (I-SUPPORT bathing assistance system) Surgical robotics (STIFF-FLOP variable-stiffness manipulators) Artificial organs (hybrid heart and larynx development) Wearable monitoring systems for neurorehabilitation His 2024-2025 publications reveal accelerating trends in machine learning integration for soft robot control, biomimetic medical simulators, and agricultural automation. Key advancements include textile-based proprioception, laser-induced graphene sensors, and variable-stiffness architectures enabling real-world deployment in low-resource settings. No scientific awards were documented in the source material. He coordinates major EU initiatives including: SoftGrip: Mushroom harvesting robotics (as coordinator) Hybrid Heart: Artificial heart combining soft robotics and tissue engineering MAESTRI: Next-generation medical robotics platforms His lab actively mentors researchers while securing industry partnerships with oil/gas and sportswear leaders for technology transfer. The Soft Mechatronics for Biorobotics Lab operates as an innovation hub translating fundamental research into commercial applications. Current projects span pipeline inspection robotics, performance-enhancing athletic wear, and educational soft robotics kits, demonstrating strong industry-academia collaboration.
Salvatore Mario Carta is Full Professor at the Department of Mathematics and Computer Science of the University of Cagliari. He founded the Artificial Intelligence and Big Data Laboratory and co-founded 4 hi-tech companies. His research spans multiple AI applications including large language models, financial forecasting, road safety, and health informatics. PhD in Electronics and Computer Science (2003, University of Cagliari) Assistant Professor (2005–2014) Associate Professor (2014–2021) Full Professor (2021–present) His research focuses on Artificial Intelligence, including Large Language Models for knowledge representation, AI Algorithms for smart mobility and financial forecasting, Clustering Algorithms for behavioral pattern analysis, and E-coaching Platforms for health applications. Recent work explores Knowledge Graph Engineering , Monocular 3D Object Detection , and Zero-Shot Learning in urban systems. Scientific output trends include Smart Cities (2024), Knowledge Graphs (2024), Medical Imaging (2024), and Financial AI (2024). His 15 most recent articles (2025–2024) emphasize Deep Learning for security, GAN for biometrics, and Generative Models for language preservation. As lab director and founder of spin-offs like The Cloud Alchemist Srl and Visioscientiae Srl, he bridges academic research and industrial innovation. Current affiliations include ACM membership, with past responsibilities as Local Unit Responsible for CINI.
Sergio Canazza is an Associate Professor at the Department of Information Engineering , University of Padova, Italy. He holds key roles in academic leadership as advisory editor for the Journal of New Music Research and as founder of the Sound and Music Processing Lab . His work bridges music technology , audio restoration , and cultural heritage preservation . Degree in Electronic Engineering, University of Padova CEO, AudioInnova (University spin-off) Research Interests : Expressive information processing in music Auditory displays and cross-modal interaction Preservation of musical cultural heritage Interactive multimedia systems for education AI-driven audio restoration Digital philology for time-based media Scientific Contributions span 20+ years of European/National projects and 200+ publications. His recent work focuses on: Generative AI for IoT sound communication Standardization of audio preservation (ARP technology) Reactivation of historical computer music systems Visual anomaly detection in audio tapes Interactive environments for music education 3D reconstruction of ancient instruments Awards : StartCup Veneto 2010 (Sound and Music Lab) StartCup Veneto 2012 (TechnoTale project) Start Cup 2006 (ARCHIMEDES project) Leadership Roles : Project Manager, EU Culture Program Director, University of Padova's Multimedia Center (2013-2016) Owner of audio preservation patents
Clara Zaccaria is a Research Fellow at the Department of Physics, University of Trento, specializing in biophysics with a focus on neurophotonics and optogenetic technologies. Her work bridges photonics, neuroscience, and machine learning to develop advanced tools for studying neural systems. Research Focus: Her primary interests include: Designing photonic platforms for optogenetic control of neuronal activity Developing AI-integrated systems for neural network analysis Creating in-vitro models for studying synaptic memory formation Advancing optical instrumentation for neuroscience applications Publication Trends (2021-2025): Her recent work demonstrates strong interdisciplinary integration, with publications spanning: Optogenetic device development (5 publications) Computational neuroscience and AI applications (3 publications) Neurophotonics instrumentation (2 publications) Interdisciplinary ethics of neural technologies (1 publication) This reflects a consistent focus on experimental photonics applied to neural systems.
Nawaz Ali is a Lecturer in the Department of Computer Engineering, Modeling, Electronics and Systems at the University of Calabria. His research focuses on edge computing, vehicular networks, and FPGA-based hardware acceleration, with applications in intelligent transportation systems and healthcare telematics. University of Calabria Department of Computer Engineering, Modeling, Electronics and Systems Research Interests: Specializing in edge-cloud continuum computing, mobility-aware routing protocols, and vehicular edge intelligence. His work bridges theoretical modeling with practical implementations in network optimization and hardware development. Recent Contributions: Developed enhanced simulators for urban vehicular edge environments, pioneered proximity-aware federated learning frameworks, and created GUI tools for optical signal compensation. His research also spans cognitive radio spectrum sensing and secure task scheduling in mobile edge clouds.
Silvio C. E. Tosatto is a Full Professor of Bioinformatics at the Department of Biomedical Sciences, University of Padua (Italy), where he heads the BioComputing UP laboratory. He holds significant leadership roles within ELIXIR, the European infrastructure for life science data, serving as deputy Head of Node for ELIXIR Italy, member of the Data Platform Executive Committee, co-lead of the Cellular & Molecular Research priority area, and co-lead of the Machine Learning focus group. His research spans multiple areas of computational biology and bioinformatics with particular focus on protein structure analysis, machine learning applications in biology, cancer research, and personalized medicine. His work encompasses protein aggregation, repeat proteins, residue interaction networks, and infrastructure development for bioinformatics research. Analysis of his recent publications shows a strong emphasis on database development for protein science (Pfam, InterPro, RepeatsDB), integration of AI/ML approaches in biological data analysis, and applications in genetic variant interpretation for medical conditions like intellectual disability. His work demonstrates the convergence of traditional bioinformatics with modern machine learning techniques. Professor Tosatto maintains active research collaborations across Europe as evidenced by his extensive co-authorship networks. His leadership in ELIXIR highlights his significant contribution to European bioinformatics infrastructure development. He earned his PhD (Dr. rer. nat. with Magna cum laude distinction) in bioinformatics from Universität Mannheim in 2002, following a 1998 graduate degree in Computer Science & Business Administration from the same institution. He has been a Full Professor since October 2016. Fluent in English, Spanish, German, and Italian (native), Professor Tosatto operates effectively in international research environments and leads a productive research group focused on advancing computational approaches to biological problems.
Alexander Miguel Monzon is an Associate Professor in the Department of Information Engineering at the University of Padova, Italy, specializing in non-globular proteins including disordered and repetitive proteins. His work has significantly advanced bioinformatics through co-authorship of critical databases like DisProt, RepeatsDB, MobiDB, PED, and FuzDB, which represent the state-of-the-art in structural biology for non-globular proteins. Education: PhD in Basic and Applied Sciences (2018), National University of Quilmes, Argentina MSc in Bioinformatics (2012), National University of Entre Ríos, Argentina His research focuses on protein aggregation, structured tandem repeats, and intrinsic disorder. Recent work explores AI integration in protein research, conformational ensemble modeling, and sustainable computational methods. His publications span structural biology, database development, and machine learning applications. Monzon actively participates in international consortia including COST-action NGP-net, MSCA RISE IDPfun, REFRACT, H2020 Twinning PhasAGE, and serves as main proposer of the COST action ML4NGP. He has contributed to community standards for machine learning reporting in biology via the DOME Registry.
Emilio Cruciani is a Tenure Track Assistant Professor in Computer Science at the European University of Rome, affiliated with the Department of Human Sciences. He holds a PhD in Computer Science from the Gran Sasso Science Institute (2019) and has conducted research at universities in Germany, France, and Austria. His research focuses on algorithms, data mining, and social network dynamics. Current work investigates local network interactions for graph clustering, stochastic processes on networks, and scalable algorithms for large datasets. He has contributed to Green AI through dynamic model selection for energy efficiency. Co-PI on two Facebook Research-funded projects Active in program committees for algorithms and artificial intelligence conferences Over 30 publications in international conferences and journals Publications span network dynamics, election control, software testing, and hypergraph convergence. His work bridges theoretical computer science with applied problems in social influence and biomedical informatics.
Francesco Della Santa is a Fixed-term Assistant Professor at the Department of Mathematical Sciences (DISMA) of Politecnico di Torino, where he contributes to teaching and research in deep learning, numerical analysis, and optimization. Master Degree in Mathematics, University of Florence (2017) Ph.D. in Pure and Applied Mathematics, Politecnico di Torino (2021) His research focuses on Deep Learning and its applications to Numerical Optimization and Physically-Based Simulations , with a particular emphasis on uncertainty quantification and graph-informed neural networks. Recent publications explore discontinuous neural networks for kernel learning, edge-wise graph modeling, and multi-start optimization techniques. Francesco collaborates on courses like Numerical Optimization for Large Scale Problems and Mathematics for Artificial Intelligence across Mathematical Engineering and Data Science programs. He supervises Ph.D. student Filippo Aglietti in the Energetics program (38th cycle, ongoing since 2022). Research groups: Numerical Analysis and Scientific Computing (DISMA)
