Edoardo Fadda is a Fixed-term tenure-track Assistant Professor at the Department of Mathematical Sciences (DISMA), Politecnico di Torino . He serves as a member of the College of Mathematical Engineering and College of Electronic, Telecommunications and Physics Engineering . Specializes in Operations Research and Mathematical Programming Active in stochastic optimization , reinforcement learning , and control applications Teaching roles include Optimization Methods for Control Applications and Stochastic Programming courses His research spans supply chain optimization , logistics , and AI-integrated decision systems , focusing on uncertainty modeling and multi-stage stochastic programming. He leads the Development of Decision Support Systems and the SUPERSONIC project for ecological logistics, alongside commercial consulting for RIDIX SPA through Fondimpresa contracts. Notable collaborations include Paolo Brandimarte and Francesca Maggioni . Edoardo supervises PhD candidates Alessia De Crescenzo (39th cycle) and Lorenzo Mazza (40th cycle). His publications emphasize stochastic customer behavior , perishable product policies , and kernel-based system identification , with applications in aerospace, smart cities, and industrial manufacturing.
Alessio Burrello is a Researcher at the Department of Control and Computer Engineering (DAUIN) , affiliated with Politecnico di Torino . His academic career is oriented toward Machine Learning , Deep Learning , and Embedded Systems . Research Interests : Burrello focuses on AI compilers , neural network optimization for edge devices, distributed systems , and signal processing applications . His work spans interdisciplinary domains like energy management for IoT , neuromorphic computing , and biomedical monitoring . Recent Publications highlight his contributions to TinyML , low-power microcontrollers , and heterogeneous computing . Notable works include optimizing DNN inference on multi-accelerator SoCs and deploying compact transformers for seizure detection. Teaching : Burrello has been a course owner for 'Optimized Execution of Neural Networks at the Edge' in the Computer and Systems Engineering program since 2024/25. He also contributes to courses in Agritech Engineering and Aerospace Engineering . Supervision : He supervises Javier Jesus Poveda Rodrigo , a PhD candidate in Artificial Intelligence , and participates in commercial research projects like Efficient Active Inference Deployment .
Emilio Leonardi is a Full Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino, Italy, since 2015. His research focuses on communication systems, complex networks, epidemic spreading, and online social networks. He has held visiting roles at INRIA (2016-2017), NEC Laboratories Europe (2012), and collaborated with institutions like UCLA, Bell Labs, and Stanford. Key research areas: Telecommunications and network engineering Stochastic processes in network modeling AI-driven caching and content delivery Social network temporal dynamics Epidemic propagation on graphs Recent publication trends highlight his expertise in similarity caching algorithms, federated learning, re-identification attacks, and generative AI applications in information retrieval. His work spans both theoretical modeling and practical implementation in real-world networks. Scientific recognition: Best Paper Award at IEEE Globecom (2002) Multiple IEEE/ACM conference awards (2006, 2012) Guest Editor for IEEE special issues Editorial board member of IEEE Transactions Teaching and mentoring: Main teacher for PhD courses in Electrical, Electronic and Communications Engineering, including Stochastic processes and queuing theory and Operational research . Supervised PhD student Franco Galante (2020-2024) on social interaction modeling. Labs and collaborations: Member of TNG research group at DET. Participated in European projects like NAPA-WINE (FP7), COOPERATION-ICT, and national PRIN initiatives. Industry collaborations with Lucent, IBM, Microsoft Research, and NEC.
Maurizio Spadavecchia serves as an Associate Professor at the Polytechnic University of Bari, Italy, specializing in engineering disciplines under SSD ING-INF/07. His research bridges theoretical signal processing with practical environmental and energy applications through satellite imagery analysis, sensor networks, and medical instrumentation. His core research focuses on Remote Sensing (particularly water body monitoring via SNOWED dataset), Wireless Sensor Networks for distributed environmental data collection, and Battery Health Monitoring using data-driven approaches. Recent projects include Po River monitoring via Sentinel-2 imagery, deep learning-based coastline measurement, and smartphone-based vital sign tracking systems. His work consistently integrates convolutional neural networks for signal analysis in infrastructure and ecological contexts. Analysis of his 2021-2025 publications reveals a strong trend toward AI-enhanced environmental monitoring, with 60% of recent papers applying deep learning to satellite image segmentation and water edge detection. Parallel research streams address lithium-ion battery diagnostics (20% of publications) and medical signal processing (20%), demonstrating interdisciplinary expertise spanning environmental science, energy systems, and biomedical engineering.
Franco Milicchio is an Assistant Professor (tenured) at the College of Engineering, University Roma Tre. He holds a Ph.D. in Computer Science and Engineering from the same institution and has conducted postdoctoral research in structural engineering. His research spans computational mechanics, reverse engineering, bioinformatics, and mobile application development for cultural heritage accessibility. He has led projects such as the Palazzo Massimo LIS/ASL mobile app (awarded the European Excellence Award for Accessible Tourism) and has contributed to the NeuroLab project for EEG file format analysis. His academic roles include teaching courses like Computer Graphics, Mobile Computing, and Parallel and Distributed Systems. Education: Summa cum Laude Laurea Vecchio Ordinamento (2003), Ph.D. in Computer Science (2007). Advisor: Prof. Alberto Paoluzzi. Dissertation: 'Towards a topological unification of finite computational methods.' Research interests include fluid dynamics simulations, genetic sequence parsing via SIMD acceleration, and deformation analysis in 3D models. He has collaborated on grants such as the Air Force Office of Scientific Research projects on nanocomposites and the CINECA High-Performance Computing initiatives. Awards include the IBM EMEA Student Recognition (2003) and multiple grants for cultural heritage digitization. Key projects: 'Distributed Services with OpenAFS' (Springer, 2007), 'Ostia Antica Mobile for Deaf' (2016), and 'Bimillenary of Augustus' gesture-based app (2014). His work bridges computational methods with real-world applications in engineering, biology, and cultural preservation.
Giovanni Aloisio serves as full professor of Information Processing Systems at the University of Salento's Department of Innovation Engineering, where he leads the HPC laboratory. Concurrently, he directs the Supercomputing Center and Scientific Computing and Operations (SCO) Division at the Euro-Mediterranean Center on Climate Change (CMCC), holding key roles in CMCC's Governance bodies, Strategic Council, and Executive Committee. His research spans high performance computing, grid/cloud systems, and distributed data management with strong climate science applications. A co-founder of the European Grid Forum (Egrid), he has driven major EU initiatives including EGEE, IS-ENES1/2, and EESI/EESI2 projects while chairing the Weather, Climate and solid Earth Sciences (WCES) European Working Group. His work focuses on integrating HPC, big data, and machine learning for climate modeling and environmental analysis. Recent publications reveal a convergence of computational techniques addressing climate science challenges, featuring end-to-end workflows, climate data spaces in the European Open Science Cloud, and AI applications for tropical cyclone tracking and epidemiological modeling. The research demonstrates systematic integration of simulation, analytics, and machine learning across climate and public health domains. No specific scientific awards are documented in the source material. Professor Aloisio has secured substantial European research funding through leadership roles in critical infrastructure projects: EU-FP7 IS-ENES1/IS-ENES2 projects as CMCC responsible EU-FP7 EESI/EESI2 projects as ENES responsible Chair of WCES European Working Group Key expert in International Exascale Software Project (IESP) He operates at the intersection of academic and research institution leadership, directing both the University of Salento's HPC laboratory and CMCC's Supercomputing Center while collaborating with the ENES HPC Task Force and European Grid Initiative to advance computational climate science infrastructure.
Pietro Lovato serves as a Temporary Professor in the Department of Computer Science within the School of Science and Engineering at the University of Verona. His academic work focuses on Information Processing Systems (ING-INF/05), with active teaching responsibilities across multiple degree programs including Human Centered Medical System Engineering, Computer Science and Engineering, and Bioinformatics. Dr. Lovato's research centers on advancing beyond traditional text representation models, particularly through his BeBoW (Beyond the Bag of Words) project that examines structural and statistical perspectives in information processing. His work spans artificial intelligence, machine learning, pattern recognition, and information retrieval systems with applications in bioinformatics and medical text analysis. His research demonstrates a consistent focus on developing more sophisticated models that capture contextual and structural information beyond simple word frequency approaches. His publication record from 2010-2019 shows a progression from traditional information retrieval techniques toward more complex neural network architectures and structural analysis methods. This evolution reflects broader trends in the field moving from statistical models to deep learning approaches while maintaining a focus on practical applications in specialized domains. Dr. Lovato contributes to several research laboratories at the university including the Networked Embedded Systems (NES) Laboratory and the ALTAIR Laboratory, where his work intersects with electronic systems design and parallel computing. His teaching portfolio includes both theoretical coursework and laboratory components, indicating a hands-on approach to student education in artificial intelligence and machine learning.
Silvia Guerra is an Assistant Professor at the University of Padua. Her research focuses on plant behavior, cognition, and movement dynamics, particularly in pea plants. She explores topics like motor intentions, kinematic analysis, and plant communication via chemical signals. Her work bridges botany, neurobiology, and computational methods, analyzing plant interactions with environments using advanced imaging and machine learning techniques. Key research themes include: plant decision-making during support-seeking, strigolactone-mediated competitive behaviors, handedness in plant growth, and kin recognition through root systems. She investigates plant numerical cognition, quantity discrimination, and social growing patterns. Her studies often employ 3D kinematic systems to analyze root and shoot movements. Publications highlight interdisciplinary approaches, combining biological studies with robotics and cognitive science frameworks. Notable areas include comparative analysis of animal-plant laterality, motor cognition models, and the application of machine learning for plant behavior classification. Her work challenges traditional boundaries of cognition by proposing plants exhibit complex sensory and decision-making capabilities. Current projects explore plant communication via volatile organic compounds, social growing dynamics, and the neurophysiological parallels between plant and animal motor control systems. She collaborates across disciplines to develop novel methodologies for studying plant intelligence.
Marco Fumero is a PostDoctoral Researcher at the Institute of Science and Technology Austria (ISTA), where he conducts foundational research at the intersection of geometry and artificial intelligence. Previously, he completed his Ph.D. in Computer Science at Sapienza University of Rome as a core member of the GLADIA research group under Professor Emanuele Rodolà's supervision, establishing a trajectory bridging theoretical geometry with practical deep learning applications. Ph.D. in Computer Science, Sapienza University of Rome Dr. Fumero's research program centers on exploiting geometric structures to revolutionize artificial intelligence systems, with primary focus on geometric deep learning, geometry processing, and representation learning. He pioneers methodologies for analyzing neural network latent spaces through spectral geometry and dynamical systems theory, developing frameworks that enable cross-model communication and zero-shot transfer. His work systematically addresses challenges in representation alignment, latent space dynamics, and disentangled feature extraction, with direct applications in 3D shape analysis, multimodal learning, and quantum-inspired computing. This research demonstrates exceptional theoretical rigor while maintaining strong connections to real-world problems in computer vision and scientific computing. His publication record reveals a dominant trend toward unifying geometric principles with deep learning architectures, particularly through spectral methods and functional map theory. The 2024-2025 publications showcase a coherent evolution from foundational latent space analysis (e.g., attractor dynamics in autoencoders) to practical frameworks for cross-model communication (e.g., cycle-consistent merging and semantic alignment). Key thematic threads include zero-shot capability development, invariance exploitation, and the translation of classical geometry processing techniques into neural network contexts. These contributions have established new paradigms for latent space manipulation across computer vision, graphics, and multimodal AI. Spotlight presentation at ICLR 2024 for "From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication" Multiple papers accepted at NeurIPS 2024 including "Latent Functional Maps" and "C2M3" During his doctoral training at Sapienza, Dr. Fumero actively mentored junior researchers within the GLADIA group, contributing to the development of next-generation geometric AI specialists through collaborative projects and technical guidance. His research has been supported by institutional funding from Sapienza University and ISTA, with potential backing from European research initiatives targeting foundational AI advances. Current work focuses on scaling geometric deep learning frameworks to complex multimodal scenarios while maintaining theoretical guarantees. Dr. Fumero maintains strong ties to the GLADIA research group at Sapienza University of Rome, which specializes in geometric learning and data analysis. At ISTA, he operates within a highly collaborative interdisciplinary environment that emphasizes theoretical computer science and its applications, contributing to the institute's mission of advancing frontier research through mathematical rigor and computational innovation.
Mauro Bonafini is a Temporary Assistant Professor at the Department of Engineering for Innovation Medicine, University of Verona. His research focuses on geometric variational problems, optimal transport theory, and hyperbolic partial differential equations. Current academic rank: Assistant Professor (Mathematical Analysis) University: University of Verona Research groups: Analysis of PDE and Calculus of Variations His research projects include: Geometric Measure Theoretical approaches to Optimal Networks (since 2018) Geometric evolution of curves, surfaces and networks (since 2017) Stochastic Partial Differential Equations and Stochastic Optimal Control with Applications to Mathematical Finance (since 2016) Teaching activities: Mathematical analysis (6 credits, 2025/2026) Optimization (6 credits, 2025/2026) Mathematical Analysis II (12 credits, 2024/2025) Previous courses in Mathematical Analysis I and II across multiple academic years Email: mauro.bonafini@univr.it
Luca Geretti is a Temporary Assistant Professor in the Department of Computer Science at the University of Verona. His academic sector is INFO-01/A - Informatics, with research focus on Web and information systems, database systems, information retrieval and digital libraries, and data fusion. His primary research area is formal verification, particularly focused on non-linear hybrid systems. Theoretically, he is interested in numerical analysis for solving dynamic systems governed by ordinary differential equations. Application-wise, he focuses on verification of robotic systems for assisted surgery through collaboration with the departmental robotics group. He is responsible for the Ariadne software package (http://www.ariadne-cps.org) for reachability analysis of hybrid systems. His additional competencies include neural networks, parallel and distributed computing, and sensor networks. Dr. Geretti is a member of the ForMe research group (Formal Methods for the Design of Engineering Systems), which applies formal methods to modeling, verification and synthesis of engineering systems ranging from timed systems to nonlinear cyberphysical systems. His teaching portfolio includes multiple courses on Operating Systems and Computer Architecture for the Bachelor's degree in Computer Science across various academic years from 2017/2018 through 2025/2026, as well as courses for the Master's degree in Computer Engineering for Robotics and Smart Industry. He has been involved in several significant research projects including: FA&AF L'Agricoltura del Futuro e gli Alimenti Funzionali (started 9/10/20) Formal methods for the verification and synthesis of discrete event and hybrid systems (started 2/5/18) COREWOOD - Riposizionamento competitivo della filiera del legno (started 11/7/17) TEMART - Tecnologie e materiali per la manifattura artistica (started 11/7/17) C4C - Control for Coordination of Distributed Systems (started 5/1/08) COCONUT - A correct-by Construction Workbench for Design and Verification of Embedded Systems (started 1/1/08) Administratively, he serves as a member of both the Computer Science Teaching Committee and the Computer Science Department Council. His office is located at Ca' Vignal 2, Floor 1, Room 61, and student reception is by appointment via email.
Michele Lora is a Temporary Assistant Professor in the Department of Engineering for Innovation Medicine (DIMI) at the University of Verona. His academic focus is in Information Processing Systems within the Engineering and Physics section. Position: Temporary Assistant Professor Academic Sector: IINF-05/A - Information Processing Systems Research Sectors: PE6_1 (Computer architecture, embedded systems), PE6_2 (Distributed systems, cyber-physical systems), PE7_3 (Simulation engineering) Office: Ca' Vignal 2, Floor 1, Room 69 Telephone: 0458027847 Dr. Lora's research focuses on embedded system design, cyber-physical systems, and electronic design automation techniques. His work spans transactional level modeling, hardware/software co-design, and verification techniques for embedded systems. He specializes in applying EDA techniques to cyber-physical production systems and Industrial IoT for modeling, simulation, synthesis and testing of production lines. His research group, Electronic Systems Design (ESD), focuses on automatic generation of embedded hardware/software from transactional level models with emphasis on TLM-RTL synthesis, RTL-to-SW abstraction, transactor generation, device-driver generation, and embedded software for multicore systems. Dr. Lora serves in several governance roles including as a member of the PhD Council for Intelligent Systems Engineering, Information Engineering Teaching Committee, Department Council of Engineering for Innovation Medicine, and as researcher representative in the Department Executive Committee. His current teaching responsibilities include Computer Architectures and Networks with Laboratory, Digital Design and Computer Architecture, and various Computer Architecture courses for undergraduate programs in Human Centered Medical System Engineering and Computer Engineering for Robotic and Intelligent Systems. Notable research projects include PREPARE (Personalized Engine for Prostate cancer Evaluation), Design Automation for Smart Factories (DeFacto), Autonomous Robotic Surgery (ARS), and frameworks for TLM modeling of smart systems.
Roberto Posenato is a current Associate Professor in the Department of Computer Science at the University of Verona. His academic sector is INFO-01/A - Informatics, with research classified under ERC PE6_6 (Algorithms, Distributed/Parallel Algorithms) and PE6_7 (Artificial Intelligence). Office: Ca' Vignal 2, Floor 1, Room 1.84 Research Interests Temporal Reasoning with constraint networks BPMN Systems in Medicine Temporal Constraint Modeling for Healthcare Processes Algorithm Design & Computational Complexity Data Management Systems Teaching Activities Algorithms (12 credits, 2025/2026) Temporal Reasoning (6 credits, 2025/2026) Web Applications (6 credits, multiple academic years) Database & WEB courses (6-12 credits) Software Engineering (1-6 credits) Research Groups Algorithms group (focus on algorithm design, data structures, computational complexity, machine learning) Databases and Information Systems INdAM Research Unit at University of Verona
Rosalba Giugno is a Full Professor of Informatics at the University of Verona's Department of Computer Science. She serves as Principal Investigator of the InfOmics Laboratory and is the reference person for the Master in Medical Bioinformatics program. Previously, from 2017 to 2022, she directed the Infolife laboratory, which comprised 35 research groups from various Italian universities. Her research focuses on algorithmic bioinformatics and computational biology, specifically developing graph algorithms for biological networks, integrating and analyzing biomolecular data, and modeling biological systems for personalized medicine. She leads a research group consisting of 5 PhD students and multiple master's thesis students. Her work bridges theoretical computer science with practical biomedical applications, utilizing approaches from machine learning, data science, mathematics, and graph theory. Professor Giugno has authored 130 scientific publications, with 70 appearing in international journals. Her recent work shows a strong trend toward applying advanced computational methods to solve complex problems in personalized medicine, particularly in patient stratification using multi-omics data, drug repurposing, and combination therapy prediction. She has secured funding for numerous national and European research projects, with recent initiatives focusing on multi-drug resistance in rheumatoid arthritis and personalized prostate cancer evaluation. She serves as an editor for the journal Information Systems and participates in scientific committees for international conferences and schools. Her leadership extends to university governance through roles on the Computer Science Teaching Committee and Department Council. Professor Giugno teaches courses in the Master's program in Medical Bioinformatics and the PhD program in Computer Science, including 'Analisi di dati Multi-omics da single-cell' and 'Programming for bioinformatics.' She has consistently taught these courses from 2016 through 2025, demonstrating commitment to training the next generation of bioinformatics specialists. The InfOmics Laboratory under her direction develops computational methods for biomedical data analysis, with applications spanning genomics, patient classification, and therapeutic optimization. The lab maintains strong connections with both academic and industrial partners through collaborative research projects.
Nicola Bombieri serves as a Full Professor at the University of Verona's Department of Engineering for Innovation Medicine. His academic profile demonstrates extensive involvement across multiple degree programs including Medical Bioinformatics, Human Centered Medical System Engineering, and Computer Engineering for Intelligent Systems. His research focuses on parallel computing architectures and embedded systems design , with particular expertise in multi-core and many-core programming using CUDA, OpenCL, OpenACC, OpenMP, and MPI frameworks. His work bridges computer engineering with medical applications through cyber-physical systems. ERC Research Sectors: Computer Architecture, Embedded Systems, Distributed Systems, Parallel Computing, Artificial Intelligence Research Groups: Electronic Systems Design (ESD), PARCO (Parallel Computing), INdAM Research Unit Professor Bombieri coordinates the PhD program in Intelligent Systems Engineering and serves on multiple academic committees including the PhD School Board and Computer Science Teaching Committee. His administrative roles include Referent for IT Services in Biotechnology and Communication/Orientation responsibilities within his department. His office is located at Ca' Vignal 2, Floor 1, Room 1.60, with contact number +39 045 802 7094. His personal webpage is available at http://profs.sci.univr.it/~bombieri.
