جست‌وجوی استاد و پژوهشگر

• Aeroengine
• Contact mechanics
• Experimental dynamics
• +۷ مورد دیگر

Daniele Botto is a Full Professor in the Department of Mechanical and Aerospace Engineering (DIMEAS) at the Polytechnic University of Turin, where he conducts research in aeroengine dynamics, contact mechanics, and experimental vibration analysis. His work focuses on aerospace engineering applications with particular emphasis on turbine components and structural dynamics. His primary research interests include Aeroengine , Contact mechanics , Experimental dynamics , Nonlinear dynamics , and Tribology . His scientific work spans mechanical design, machine construction, and aerospace engineering with applications in energy systems. His ERC sectors specifically identify him within aerospace engineering, computational engineering, and mechanical manufacturing fields, aligning with UN Sustainable Development Goals 7 (Affordable and clean energy) and 9 (Industry, Innovation, and Infrastructure). Analysis of Professor Botto's recent publications (2023-2025) reveals a strong focus on digital image correlation (DIC) techniques for vibration measurement, turbine blade dynamics, and contact mechanics. His work demonstrates a consistent pattern of applying experimental methods to solve complex nonlinear dynamics problems in aerospace structures, particularly focusing on friction damping, dovetail attachments, and midspan dampers in turbine blades. The integration of smartphone technology for structural testing represents an innovative direction in his recent research. Professor Botto serves on the editorial board of the journal MATERIALS (2021-present) and has been a guest editor for special issues. He has participated in organizing committees for major conferences including the International Congress on Sound and Vibration and ASME Turbo Expo events. As an academic advisor, Professor Botto supervises multiple PhD students working on mechanical engineering topics, with current advisees including Alessandra Cesaretti, Mohammad Hedayatzadeh Roudsari, Enio Colonna, Federica Cuccovillo, and Serena Occhipinti. His research portfolio includes numerous funded projects such as STAGEH2 (Advanced Heat Exchange Applied to Green Economy), FARAWaY (Full-field Analysis of Rotating Structures), and various commercial research contracts with industry partners focusing on turbine components, wear characterization, and structural analysis. Professor Botto leads the "Aer-Mec for turbine and compressor components" research group within DIMEAS and collaborates extensively with industry on aeroengine-related research. His work bridges academic research with practical engineering applications, particularly in the areas of structural dynamics and contact mechanics for aerospace components.

• Battery Control Systems
• Electric Vehicles
• Automotive Aerodynamics
• +۸ مورد دیگر

Massimiliana Carello is an Associate Professor at the Polytechnic University of Turin's Department of Mechanical and Aerospace Engineering (DIMEAS). She serves as a Student Team Contact Person and member of academic commissions, focusing on sustainable mobility and advanced vehicle systems. Scientific Director of the National Technology Transfer Center for Sustainability (2023-2025) Teaches courses on electric/hybrid vehicles, battery systems, and automotive design Her research spans Battery Control Systems , Vehicle Dynamics , Lightweight Design , and Proton Exchange Membrane Fuel Cells , with 15+ recent publications in automotive engineering journals and conferences. She leads projects under PNRR Missione 4 and commercial contracts. Key scientific recognitions include the GWIIN Global Women Inventors Network (2017) and DISTI-TELPRESS Special Award (2011). Her team supervises PhD students in mechanical engineering related to electric mobility and sustainable technologies.

• Fluid Machinery
• Engine Combustion
• Hybrid and Electric Powertrains
• +۵ مورد دیگر

Daniela Anna Misul is a Professor at the Department of Energy (DENERG) of the Polytechnic of Turin . She serves as Deputy Coordinator of the ENERGETICA Doctoral College and contributes to academic governance as a Member of the Joint Committee for Teaching. Research Interests span Fluid Machinery , Engine Combustion , Hybrid Powertrains , Turbine Cooling , and Smart Mobility . Scientific Networks include ETN Global , European Clean Hydrogen Alliance , and ERCOFTAC . Her recent 15 publications (2022–2025) focus on gas turbine optimization , battery management systems , and hydrogen-fueled engines . She has served on editorial boards for Applied Sciences , ENERGIES , and Transportation Engineering .

• High Energy Physics
• Particle Physics
• Nuclear Physics
• +۳ مورد دیگر

Stefania Bufalino is an Associate Professor at the Department of Applied Science and Technology (DISAT), Politecnico di Torino. She serves as Coordinator of the Doctoral School of Physics and a member of the Doctoral School Council. Her research focuses on experimental physics of fundamental interactions, high energy collisions at CERN's LHC, and advanced sensor development. Research interests: High energy physics, particle and nuclear physics, machine learning applications, pixel sensor design, and quark-gluon plasma characterization. Recent publications analyze hypernuclei, elliptic flow in Xe-Xe collisions, strangeness enhancement, and intra-jet dynamics using ALICE detector data. These works emphasize nuclear physics, particle tracking, and high-resolution timing measurements. Scientific awards: 2008 Antonio Garbasso Award (Italian Physical Society). She supervises PhD students including Marcello Di Costanzo, Giulia Gioachin, and Mario Ciacco. Bufalino leads courses in Physics I and laboratory work for Aerospace Engineering and Physics programs, and contributes to doctoral seminars. Her affiliations include DISAT's ALICE (CMOS sensors) Laboratory and Nuclear Sources Laboratory.

Paolo Baldanمشاهده پروفایل

استاد

• Formal Methods for Software Reliability
• Programming Languages
• Concurrency Theory
• +۶ مورد دیگر

Paolo Baldan is a Professor of Computer Science at the Department of Mathematics of the University of Padova, where he maintains office 6DA6. Previously, he served as an assistant professor at the University of Venice's Computer Science Department and as a PostDoc Researcher at the University of Pisa's Department of Computer Science. His research focuses on Formal Methods for Software Reliability and Programming Languages , with particular expertise in Concurrency Theory, Petri Nets, and Graph Transformation Systems. Professor Baldan has led multiple research projects including the current MUR Project RAP (2023-2025) and previous initiatives like ASPRA (2019-2022) and ANCORE (2015-2016). His publication record spans journal articles, conference papers, and technical reports in theoretical computer science. His recent work examines resource awareness in programming, program analysis techniques, and verification of concurrent systems. The publications demonstrate consistent contributions to formal methods with applications to software reliability. Professor Baldan actively participates in the academic community through conference organization and committee work, including involvement with CALCO, Petri nets conferences, and ICGT. His upcoming commitments include CSL 2026. He teaches courses including Computability , Languages for Concurrency and Distribution , and Algorithms and Data Structures at the University of Padova. Office hours are held Wednesdays 11:00-13:00, requiring advance contact for meetings.

Lorenzo Rosascoمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Robotics
• +۵ مورد دیگر

Lorenzo Rosasco is a Full Professor at the Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS) at the University of Genoa. He holds roles including University contact for the thematic area "Artificial Intelligence" and membership on the Department Board. His research focuses on machine learning theory, kernel methods, optimization, robotics, and their applications in control systems, domain adaptation, and interdisciplinary fields like biomedical engineering. His educational background includes advanced expertise in mathematical foundations of machine learning, though specific academic credentials are not detailed here. Teaching responsibilities include courses such as MACHINE LEARNING, SEQUENTIAL PREDICTION AND REINFORCEMENT LEARNING, and INFORMATION THEORY AND INFERENCE at both undergraduate and graduate levels. Research interests revolve around developing efficient algorithms for large-scale machine learning, including iterative regularization, transfer learning, and control systems using Koopman operator theory. Notable contributions include work on fast kernel methods, optimization in neural networks, and applications in robotics for tasks like humanoid locomotion and tactile sensing. Recent publications explore topics such as double descent phenomena, sim-to-real transfer learning, and adaptive control strategies. His work bridges theoretical insights with practical applications, emphasizing computational efficiency and interdisciplinary impact.

Franco Milicchioمشاهده پروفایل

استادیار

• Computer Science
• Computational Mechanics
• Reverse Engineering
• +۷ مورد دیگر

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.

• Theoretical Physics
• Condensed Matter Physics
• Nanomaterials
• +۳ مورد دیگر

Riccardo Ferrando is a Full Professor at the University of Genoa's Department of Physics. He serves as the Director of the Department and is a member of the School Council. His research focuses on theoretical condensed matter physics, computational modeling of nanomaterials, and their applications. Key areas include nanoparticle structure, colloidal interactions, and alloy phase transitions. Ferrando teaches courses like Computational Physics and Materials Physics at both bachelor's and master's levels. His work integrates advanced computational methods with machine learning to predict nanocluster behavior and optimize nanoalloy properties. Research interests span theoretical and computational studies of colloidal nanoparticles, metallic nanoalloys, and their interactions with biological systems. He explores topics such as metastable configurations in nanoscale systems, coalescence dynamics, and surface engineering for catalytic applications. His computational tools include density functional theory, molecular dynamics, and Monte Carlo simulations. Recent publications highlight advancements in understanding nanoparticle packing, solidification pathways, and machine learning-driven structural analysis. Ferrando collaborates internationally, contributing to cutting-edge studies in materials science and nanotechnology. No specific awards or grants are listed in the provided data.

• Artificial Intelligence
• Machine Learning
• Neural-Symbolic AI
• +۵ مورد دیگر

Marco Gori is a Full Professor at the Department of Information Engineering and Mathematical Sciences , University of Siena. His research spans Artificial Intelligence , Machine Learning , and Neural-Symbolic AI , with significant contributions to Educational Technology and Knowledge Graphs . He teaches Algorithms and Data Structures for undergraduate Management Engineering and Fundamentals of Machine Learning for graduate Artificial Intelligence and Automation Engineering . His recent work integrates Graph Neural Networks and State-Space Models for tasks like educational quiz generation and interoperable supply chain traceability. His publications highlight trends in Hybrid AI , focusing on interpretable models, cross-language educational tools, and temporal sequence processing. Articles like Grounding Methods for Neural-Symbolic AI and Interpretable-by-design Link Prediction underscore his emphasis on explainability and knowledge integration in AI systems.

Edmondo Trentinمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Neural Networks
• Pattern Recognition
• +۵ مورد دیگر

Edmondo Trentin is an Associate Professor of Artificial Intelligence at the Department of Information Engineering and Mathematics (DIISM), University of Siena, Italy. He chairs the IAPR Technical Committee 3 (Neural Networks & Computational Intelligence) and serves as an Associate Editor for Neural Processing Letters . His research focuses on neural networks, probabilistic models, and their applications in bioinformatics, speech processing, and pattern recognition. Trentin has taught extensively at both undergraduate and graduate levels, including courses on Artificial Intelligence, Neural Networks, and Pattern Recognition. He has advised numerous PhD students and contributed to international conferences and workshops. His work integrates machine learning with statistical methods, emphasizing hybrid models for complex data analysis. Teaching Highlights: Artificial Intelligence (MSc. courses since 2001) Neural Networks for Statistical Pattern Recognition Machine Learning for Graphs Supervised and Unsupervised Learning Research Interests: Connectionist density estimation Probabilistic graphical models Hybrid ANN/HMM systems Bioinformatics applications Professional Activities: Former Vice-Chair of IAPR-TC3 IEEE Computational Intelligence Society Secretary (2005-2006) Organizer of international workshops on neural networks and pattern recognition

• Machine Learning
• Operations Research
• Optimization
• +۳ مورد دیگر

Silvia Villa is an Associate Professor in the Department of Mathematics (DIMA) at the University of Genoa (UniGe). Her teaching responsibilities include courses such as Game Theory, Machine Learning, Optimization and Operations Research, and Operations Research at both undergraduate and master's levels. Her research focuses on optimization theory and its applications in machine learning, inverse problems, and regularization methods. She has contributed to advancements in iterative regularization techniques, stochastic optimization algorithms, and convex optimization frameworks. Her work often bridges theoretical foundations with practical applications in signal processing and data science. Her research interests emphasize structured optimization problems, including low-complexity regularizers, bilevel optimization, and sparse recovery. Key methodologies in her studies include primal-dual dynamics, proximal algorithms, and variance reduction techniques. Recent trends in her publications highlight advancements in unrolled deep networks for sparse signal restoration, adaptive optimization strategies, and convergence analysis of stochastic methods. Villa's collaborative efforts span interdisciplinary projects involving applied mathematics, computer science, and engineering. While no specific awards are listed, her prolific publication record reflects her active role in the optimization and machine learning communities. She has advised multiple research projects but no formal student names are provided in the available data.

• Optimization
• Robotics
• Machine Learning
• +۳ مورد دیگر

Marco Rando serves as a Research Fellow at the Department of Computer Science, Bioengineering, Robotics, and Systems Engineering (DIBRIS) at the University of Genoa, Italy, focusing on advanced computational methodologies with practical engineering applications. His research expertise spans critical domains in modern computational science: Optimization (specializing in black-box and zeroth-order techniques) Robotics (particularly humanoid locomotion control systems) Machine Learning (kernel methods and stochastic optimization) Systems Engineering (data quality monitoring frameworks) Recent publications (2023-2024) reveal a concentrated research trajectory toward developing efficient structured optimization algorithms for non-smooth problems, with significant applications in robotic control systems and data integrity verification. His work consistently integrates gradient-free approaches to solve complex real-world engineering challenges. Scientific awards: No awards documented in available materials. Advising activities and research funding details remain unspecified in current records, with no student mentorship information provided. Laboratory affiliations and collaborative research teams are not explicitly referenced in the source documentation.

• Bioinformatics
• Algorithmic Biology
• Natural Computing
• +۴ مورد دیگر

Simone Avesani serves as a Researcher in the Department of Computer Science at the University of Verona's School of Science and Engineering. His academic sector is INFO-01/A - Informatics, where he contributes to the university's research infrastructure through specialized computational approaches to biological data. His research spans Bioinformatics, Algorithmic Biology, and Natural Computing with focus on developing graph and string algorithms for systems biology, advanced data structures for sequence analysis, distance measures for biological sequences, and machine learning applications in biomedical contexts. Avesani is actively involved in the Algorithmic Bioinformatics and Natural Computing research group where he applies theoretical methods to model biological information processes, as well as the InfOmics laboratory which specializes in efficient biomedical data analysis. Currently leading the project 'Defining Clinical and Molecular Phenotypes of Multi-Drug Resistance in difficult to treat Rheumatoid Arthritis - MDR-RA' (started January 1, 2025), his work develops novel methods for mining biological networks, integrating heterogeneous data, analyzing omics, reconstructing pangenomes, and patient classification using interdisciplinary approaches from machine learning, data science, mathematics, and graph theory. He is affiliated with the INFOMICS laboratory among several specialized research facilities including IRIS, ISLa, and STARS laboratories within the department.

• Artificial Intelligence
• Machine Learning
• Pattern Recognition
• +۳ مورد دیگر

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.

Giulia De Pasqualeمشاهده پروفایل

استادیار

• Control Systems
• Social Network Analysis
• Optimization Algorithms
• +۳ مورد دیگر

Giulia De Pasquale is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology, specializing in Control Systems. Her research focuses on social network interventions, optimization algorithms, and fairness in AI systems. She leads the Control Systems Group, addressing challenges in distributed control, hypergraph reconstruction, and ethical algorithm design. Her work integrates machine learning, network science, and control theory to develop fair and safe recommendation systems. Recent projects include hypergradient-based optimization for social network interventions and optimal transport approaches to fairness in influence maximization. She actively collaborates with institutions like Santa Fe Institute and UC San Diego. Giulia organizes interdisciplinary workshops on algorithmic fairness and has presented at NeurIPS, CDC, and NetSci. She advises PhD students like Alessandro Casu and hosts visiting scholars such as Camilla Quaresmini. Her research has been published in prestigious venues including Nature Communications and ICML.