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.
Vito Puliafito is a Full Professor at the Politecnico di Bari, affiliated with the Department of Electromagnetic Fields (ING-IND/31). His research focuses on spintronics, nanotechnology, and unconventional computing, with applications in magnetic solitons, skyrmionics, and neuromorphic systems. His work explores magnetic soliton dynamics , Dzyaloshinskii-Moriya interaction , and micromagnetic modeling of ferromagnetic, ferrimagnetic, and antiferromagnetic materials. Key areas include skyrmion stabilization , spin-transfer torque , and nonlinear oscillator networks for computing. Recent publications highlight trends in skyrmion-based devices , Ising machine architectures , and magnetic tunnel junction sensors . His research bridges fundamental physics and translational applications in nanoscale magnetic systems. For contact, email: vito.puliafito@poliba.it
Wojciech Borkowski is a researcher affiliated with the University of Warsaw , focusing on social simulation and agent-based modeling. His work explores computational tools for simulating political decisions, societal dynamics, and agent intelligence within resource-constrained systems. He advocates for standardizing social simulation frameworks and optimizing programming languages like Processing (for educational purposes) C++ (for serious computational tasks) CUDA (for GPU-accelerated simulations) to enhance interdisciplinary collaboration. His research intersects with the Humane AI Network and the AI4Europe initiative, supported by the European Union's Horizon 2020 program (grant agreement No 952026).
Antonietta Strada serves as a Research Fellow in the Department of Management and Production Engineering (DIGEP) at the Polytechnic University of Turin, focusing on interdisciplinary research at the intersection of technology, law, and management systems. Her research examines blockchain applications across diverse sectors including cultural heritage institutions and judicial systems, with specific emphasis on legal frameworks for digital assets and process optimization. She investigates technical-managerial integration challenges in emerging technologies while addressing regulatory complexities in digitized environments. Publication trends reveal consistent interdisciplinary collaboration, merging computer science with legal scholarship to solve real-world problems in museum technology and court administration. Her work demonstrates how technical solutions like BPMN and blockchain require parallel development of legal and managerial frameworks to achieve practical implementation.
Fabio Guido Mario Salassa serves as an Associate Professor in the Department of Management and Production Engineering (DIGEP) at the Polytechnic University of Turin. He is a member of the Interdepartmental Center Ec-L - Energy Center Lab and actively participates in the College of Management and Production Engineering as well as the College of Computer, Film, and Mechatronics Engineering as an invited member. His research focuses on algorithm design, combinatorial optimization, exact algorithms, metaheuristics, and scheduling problems. His work bridges theoretical computer science with industrial applications, particularly in operations research and mathematical optimization. He specializes in developing optimization models for real-world industrial challenges including production scheduling, transportation logistics, and resource allocation. His recent publications demonstrate a strong focus on scheduling theory with applications to manufacturing systems, parallel machine environments, and workforce management. The research shows consistent contributions to solving complex optimization problems through innovative algorithmic approaches and mathematical formulations. Professor Salassa has supervised PhD student Elena Rener, whose thesis focused on Single Machine Rescheduling for New Orders. He has secured multiple commercial research contracts with companies including Protea S.r.l. and Sandeza S.r.l., focusing on optimization algorithms for waste calculation, production scheduling, and transportation problems. He teaches Operations Research and Optimization for Problem Solving courses across multiple academic programs including Management Engineering and Computer Science Engineering. His teaching spans from Bachelor to PhD levels, with recent courses scheduled through the 2025/2026 academic year.
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.
Alessandro Agnetis is a Full Professor at the Department of Information Engineering and Mathematical Sciences, University of Siena, with expertise in combinatorial optimization and scheduling theory. His research spans operations research, multi-agent systems, and lean management applications, with recent publications focusing on adversarial bilevel scheduling, drone emergency logistics, and fairness-utility trade-offs in two-agent systems. Current Courses: Optimization Methods, Combinatorial Optimization, Production Planning (Master's in Engineering Management) Contact: alessandro.agnetis@unisi.it Research Trends: His work addresses complex scheduling problems across manufacturing, healthcare, and logistics, particularly emphasizing: Multi-agent coordination and Nash equilibrium models Parallel machine scheduling with unreliable jobs Emergency delivery optimization using drones Time-critical testing and cross-docking systems Lean management integration in healthcare operations
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.
Paolo Detti is a Full Professor of Operations Research at the Department of Information Engineering and Mathematical Sciences at the University of Siena. He currently teaches Operations Research and Production and Supply Chain Management - Logistics courses for both Bachelor's and Master's degree programs in Engineering Management. Professor Detti chairs the Management Engineering Degree Committee and maintains an active research program focused on complex optimization problems across multiple domains. Professor Detti's research spans several key areas of operations research with a strong emphasis on combinatorial optimization and scheduling. His work addresses real-world challenges in resource allocation for mobile telecommunications systems, electrical load scheduling for energy consumption minimization, healthcare planning and transportation problems, and sustainable crop planning in agriculture. His approach combines theoretical mathematical modeling with practical applications, often developing novel optimization algorithms and metaheuristics to solve complex problems. Analysis of Professor Detti's recent publications reveals a clear research trajectory focused on applying operations research methodologies to sustainability challenges. His work in agricultural optimization has grown significantly, with multiple 2025 publications on sustainable crop planning and rotation. The healthcare logistics domain remains consistently strong in his portfolio, particularly in biological sample transportation. Parallel machine scheduling with unreliable elements forms another persistent research thread, with multiple publications across different years addressing variations of this fundamental problem. Professor Detti maintains active teaching responsibilities across multiple academic levels. He currently teaches Operations Research to second-year Bachelor's students in Management Engineering and Production and Supply Chain Management - Logistics to first-year Master's students in Engineering Management for the 2025/2026 academic year. As chair of the Management Engineering Degree Committee, he plays a significant administrative role in academic governance. His educational background includes a PhD in Operations Research from Sapienza University of Rome, establishing his strong theoretical foundation in the field.
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
