جست‌وجوی استاد و پژوهشگر

Laurent Poirrierمشاهده پروفایل

استاد مدعو

• Discrete Optimization
• Computational Optimization
• Mathematical Programming
• +۳ مورد دیگر

Laurent Poirrier is an Adjunct Professor in the Department of Computing Sciences at Bocconi University in Milan, Italy. Previously, he was affiliated with the Combinatorics and Optimization department at the University of Waterloo. His academic work spans both theoretical and applied aspects of optimization, with a focus on computational methods. Dr. Poirrier's research interests center on the computational aspects of discrete optimization, with particular emphasis on the implementation of fast optimization algorithms. His work encompasses linear algebra, combinatorics, and numerical analysis as they relate to optimization problems. He has made significant contributions to mathematical programming, particularly in areas such as cutting planes, integer programming, and polyhedral combinatorics. His publication record demonstrates consistent contributions to the field of optimization over the past two decades, with a focus on theoretical foundations of optimization algorithms and practical implementations. His work spans pure optimization theory, computational methods, and applications in areas like vehicle routing. The progression of his research shows increasing sophistication in handling multi-dimensional optimization problems and developing numerically stable algorithms. At Bocconi University, Dr. Poirrier teaches Software Engineering (course 20875) and has previously taught courses in computer science. His teaching reflects his research expertise, covering topics from low-level computing concepts to software engineering practices.

Giuseppe Carlo Calafioreمشاهده پروفایل

استاد

• Convex Optimization
• Control Systems
• Randomized Algorithms
• +۶ مورد دیگر

Giuseppe Carlo Calafiore is a Full Professor at the Department of Electronics and Telecommunications (DET) of the Polytechnic University of Turin , where he has been since 1998. He has held visiting positions at institutions like Stanford University , University of California at Berkeley , and Vietnam National University . His research focuses on convex optimization , control systems , and randomized algorithms , with applications in finance , robotics , and machine learning . Education : Laurea in Electrical Engineering (1993) and Ph.D in Information and System Theory (1997) from Politecnico di Torino. Research Interests : Span large-scale optimization , robust portfolio optimization , and distributed algorithms , with emphasis on sparsity-based methods and data-driven control for autonomous systems. Recent Publications include works on nonlinear model predictive control , financial network robustness , and adaptive gain control in population games. His scientific awards include the IEEE CSS George S. Axelby Outstanding Paper Award (2008) and Fellow of the IEEE (2018) . He has advised students in artificial intelligence and communications engineering programs. His projects include research on smart energy allocation and financial risk modeling .

• Control Systems
• System Identification
• Machine Learning
• +۷ مورد دیگر

Fabrizio Dabbene is a Researcher at the Consiglio Nazionale delle Ricerche - Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni (CNR-IEIIT) and an external lecturer at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino. His academic roles include teaching and research in control systems, machine learning, and physics-based system identification. Research Interests : His work focuses on integrating physics-based knowledge with data-driven machine learning techniques for system identification, particularly in aerospace applications like spacecraft inertia estimation and Earth observation missions. Key areas include nonlinear dynamics, multi-step prediction, filtering, optimization, and reliability in safety-critical systems. Control Systems System Identification Machine Learning Aerospace Engineering Mechatronics Optimization Recent Publications highlight trends in physics-informed modeling, conformal prediction, and nonlinear system identification. These works emphasize applications in space economy, epidemiological models, and automotive engineering, validated through complex case studies. Advising and Research includes supervision of PhD students Cesare Donati (Ingegneria Elettrica, Elettronica e delle Comunicazioni), Gabriel Oliveira Ferreira (Ingegneria Elettrica, Elettronica e delle Comunicazioni), and Sara Narteni (Intelligenza Artificiale). His collaborations span institutions like Politecnico di Torino, Politecnico di Bari, and CNR-IEIIT.

• 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.

• Bioengineering
• Artificial Intelligence
• Explainable AI
• +۷ مورد دیگر

Gloria Menegaz is a Full Professor of Bioengineering at the Department of Engineering for Innovation Medicine, University of Verona, where she directs the BraiNAVLab. She has been serving in this position since 2021, following her appointment as Full Professor in Computer Science in 2017. Previously, she held Associate Professor and Assistant Professor positions at the University of Verona, University of Siena, and University of Fribourg (Switzerland). Her research spans multiple interdisciplinary fields including bioengineering, artificial intelligence, machine learning, and neuroimaging. She specializes in explainable AI methods applied to biomedical data, with particular focus on brain connectivity analysis using multi-modal neuroimaging data (fMRI, EEG) integrated with genetic information. Her work has resulted in over 160 publications in prestigious journals including IEEE Transactions on Image Processing, IEEE Transactions on Medical Imaging, and Frontiers in Neuroscience. Her recent research trends emphasize the development of explainable AI frameworks for clinical applications, particularly in neurological disorder diagnosis and treatment planning. She has pioneered approaches for integrating structural and functional brain connectivity data with genetic markers to identify numerical biomarkers for various neurological conditions. Senior Member of IEEE (2017) Chair of Steering Committee of IEEE Transactions on Medical Imaging Member of IEEE-SPS Challenges and Data Collection Technical Committee Vice-Chair of IEEE Bioimaging and Signal Processing Committee Recipient of Rita Levi Montalcini grant Throughout her career, Professor Menegaz has secured over 1 million euros in research funding from international institutions including the European Commission (H2020, FP7), European Science Foundation, MIUR (PRIN, SIR, FIRB), and ANR (France). She maintains extensive international collaborations with Georgia Tech University, INRIA (France), EPFL (Switzerland), Queen Mary University, and University College London. Her laboratory, BraiNAVLab, focuses on developing advanced computational methods for brain imaging analysis and navigation.

• 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.

• Operations Research
• Optimization
• Mathematical Programming
• +۵ مورد دیگر

Marianna De Santis is an Associate Professor in the Department of Information Engineering (Dipartimento di Ingegneria dell’Informazione) at the University of Florence, Italy. She previously held positions at Sapienza University of Rome and other institutions. Her research focuses on optimization, particularly in multiobjective programming, mixed-integer programming, and algorithm design. She has authored numerous articles in top journals and developed software tools like BBMOQIP and MOMIX for solving complex optimization problems. Education : PhD in Operations Research, Sapienza University of Rome (2012) MSc in Applied Mathematics (2007), BSc in Mathematics (2005) Research Interests : Dr. De Santis specializes in optimization theory and applications, including multiobjective optimization, integer programming, and algorithm design. Her work bridges theoretical advancements with practical implementations, addressing challenges in machine learning, bioinformatics, and operations research. Key areas include semidefinite programming, robust optimization, and exact methods for complex combinatorial problems. Articles Trends : Her recent work emphasizes multiobjective optimization, with contributions to hyperparameter optimization, robust combinatorial methods, and sustainable modeling. Publications span journals like Journal of Global Optimization and Mathematical Programming , reflecting her expertise in algorithmic innovation and mathematical rigor. Grants & Teaching : She leads projects on augmented Lagrangian methods and multiobjective integer optimization. Teaching includes courses on operations research, optimization, and data science at the undergraduate and graduate levels. She has also organized international workshops and served on conference committees. Labs/Teams : Her research group develops optimization software (e.g., BBMOQIP) and collaborates internationally on projects involving algorithm design and real-world applications.

Marco Stefanucciمشاهده پروفایل

استادیار

• Statistical Methodology
• Functional Data Analysis
• Spectroscopy
• +۳ مورد دیگر

Marco Stefanucci is an Assistant Professor in Statistics at the Department of Economics and Finance, University of Rome Tor Vergata. Previously, he held positions at the University of Rome La Sapienza, University of Trieste, and served as a postdoctoral researcher at the University of Padova. He earned his PhD from the University of Rome La Sapienza under Professor Pierpaolo Brutti. His research focuses on statistical methodology, functional data analysis, and applications in spectroscopy, mortality trends, and neuroscience. Collaborations include work with Professors Mauro Bernardi, Pierpaolo Brutti, and Laura Sangalli, among others. Research interests include advanced statistical techniques for functional data, compositional data analysis, and their applications in diverse fields such as demography, chemistry, and medical imaging. His recent work emphasizes adaptive regression frameworks, sparse modeling, and classification of complex datasets. He has published extensively on topics like mortality trend analysis, spectroscopic data interpretation, and multimodal imaging methodologies. While no awards or grants are explicitly mentioned, his contributions to statistical theory and applied research are evident through his academic trajectory and collaborative projects. Teaching roles include lecturing on Quantitative Methods and Statistical Learning at undergraduate levels.

Marcello Carioniمشاهده پروفایل

استادیار

• Optimal Transport
• Inverse Problems
• Machine Learning
• +۴ مورد دیگر

Marcello Carioni is an Assistant Professor in Mathematics at the University of Twente, affiliated with the Mathematics of Imaging & AI (MIA) group and the SACS group within the Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on optimal transport theory, inverse problems, calculus of variations, and their applications in machine learning and imaging. He holds a PhD in Mathematics from the Max-Planck Institute for the Mathematics in the Sciences (Leipzig), preceded by a M.S. and B.S. in Mathematics from the University of Parma. Education: B.S. (2009, University of Parma), M.S. (2012, University of Parma), PhD (2017, Max-Planck Institute) Research interests include: Dynamic optimal transport and preferential path analysis Sparsity in variational regularization Applications to medical imaging and neural networks Generalized conditional gradient methods Recent work emphasizes sparsity optimization for dynamic inverse problems, Lipschitz spaces in neural networks, and end-to-end reconstruction techniques. His 2025 preprints explore Wasserstein curves and distributionally robust optimization. He has advised PhD students including Christian Amend (2024) and Floor van Maarschalkerwaart (2024).

• Hardware Design for Network Devices
• Fault Tolerance
• Error Correction Codes
• +۳ مورد دیگر

Salvatore Pontarelli is a Researcher at CNIT (Italian National Inter-University Consortium for Telecommunications), affiliated with the University of Rome Tor Vergata. He holds a Master's in Electronic Engineering from the University of Bologna (2000) and a PhD in Microelectronics and Telecommunications from the University of Rome Tor Vergata (2003). His work focuses on hardware design for network devices, fault tolerance, error correction codes, and high-speed packet processing. He contributed to EU-funded projects and received a CISCO Research Award (2011) for work on Bloom filters and Ternary CAM. Research interests include hash-based structures (e.g., Bloom filters, hash tables), stateful programmable data planes, and FPGA-based network intrusion detection systems. He led the Defect and Fault Tolerance Group (DFTGroup) under Prof. Adelio Salsano and has extensive experience in collaborative projects with academic and industry partners. Publications highlight innovations in flow monitoring, data plane optimization, and efficient memory management. His work often bridges theoretical algorithms (e.g., cuckoo hashing) with practical hardware implementations, emphasizing real-world applications in networking and telecommunications.

Filippo Maggioliمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Geometry Processing
• Numerical Simulations
• +۳ مورد دیگر

Filippo Maggioli is a Postdoctoral Researcher at the University of Milano-Bicocca and an Adjunct Professor at Pegaso University. He completed his Ph.D. in Computer Science at Sapienza University of Rome, where he also served as a teaching assistant and postdoctoral researcher. His research focuses on interdisciplinary topics in Computer Graphics, Geometry Processing, and Numerical Simulations, with a strong emphasis on scalable algorithms and real-world applications. He has contributed to projects involving plant wilting simulation, shape correspondence, and procedural texturing. Maggioli advocates for open-source software and has published extensively in top-tier conferences such as ECCV. His teaching roles include courses on Computer Architecture and Networking at Pegaso University. Education: Ph.D. in Computer Science, Sapienza University of Rome (2019–2023) M.Sc. in Computer Science, Sapienza University of Rome (2018–2019) B.Sc. in Computer Science, Sapienza University of Rome (2014–2017) Research Interests: Maggioli’s work bridges computational geometry, numerical methods, and interdisciplinary applications. He explores topics such as functional maps for shape correspondence, physically-based simulation of natural phenomena, and GPU-optimized algorithms. His projects often integrate machine learning and procedural content generation to address challenges in 3D modeling and real-time applications. Awards & Contributions: Maggioli is a proponent of open-source software and has contributed to projects like SBML2Modelica. His research has led to impactful publications in areas like scalable geometry processing and fluid dynamics simulation.

• High Performance Computing
• Sparse Matrix Computations
• Parallel Computing
• +۳ مورد دیگر

Michele Martone is a Researcher at the High Performance Systems Division of the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. His work focuses on High Performance Computing (HPC) , sparse matrix computations , and code restructuring techniques , with a strong emphasis on OpenMP/MPI parallelization and semantic patching using the Coccinelle tool. Research Interests : HPC, automated code restructuring, sparse matrix optimization, semantic patching, and free/open source software (FLOSS) development. Key Tools : Author of the librsb library for sparse matrix operations, SparseRSB for Octave, PyRSB for Python integration, and the FIM image viewer. Publications : His recent work includes semantic patching for HPC refactorings, Coccinelle-based tooling, and performance optimization of sparse linear algebra libraries. Teaching : Delivers trainings and talks on Coccinelle, OpenMP/MPI, and FLOSS at events like HIPS25 , FOSDEM , and deRSE conferences. Advocacy : Strongly promotes free software, transparency in science, and public access to code developed with taxpayer funding.

• Mathematical Analysis
• Applied Mathematics
• Partial Differential Equations
• +۵ مورد دیگر

Pierluigi Colli is a Professor in the Department of Mathematics at the University of Pavia. His research focuses on mathematical analysis of phase transition models, optimal control theory, and applications to tumor growth modeling. He leads the Mathematical Analysis and Applications research group and has contributed extensively to the study of Cahn-Hilliard equations, viscoelastic systems, and nonlinear PDEs. His work integrates advanced analytical techniques with computational approaches, addressing problems in material science, biological systems, and thermodynamics. Colli has published over 100 peer-reviewed articles, with recent emphasis on tumor growth models, fractional operators, and dynamic boundary conditions. His research interests include: Phase field systems and their applications Optimal control of PDEs Nonlinear parabolic equations Mathematical models in oncology Material deformation and viscoelasticity Nonlocal and fractional differential equations Recent studies analyze tumor growth dynamics with chemotaxis effects, sparsity-constrained control strategies, and the interplay between thermal memory and phase transitions. Colli’s contributions to well-posedness, asymptotic analysis, and numerical methods have advanced understanding of complex systems in applied mathematics.

• Optimal Control
• Partial Differential Equations
• Mean Field Games
• +۶ مورد دیگر

Associate Professor at the University of Pavia's Department of Economics and Management, specializing in optimal control, partial differential equations, and mean field games with applications to economics and finance. Holds an Italian qualification (ASN) for associate professorship and a French qualification (maître de conférences). Research focuses on dynamic optimization, stochastic systems, and economic modeling of human capital, environmental quality, and pandemic impacts. Active in teaching mathematics for economics and optimization at undergraduate and graduate levels. Education: PhD in Mathematics from University of Padua (2015), postdocs at University of Graz (Austria) and University of Padua. Extensive visiting collaborations in France, Chile, and Brazil. Research Highlights: Pioneering work on mean field games modeling spatial interactions in human capital dynamics and environmental quality regulation. Contributions to sparse optimization, inverse problems in fracture mechanics, and large deviations in stochastic volatility models. Recent Publications Trends: Focus on infinite-dimensional mean field games (e.g., Hilbert spaces), pandemic economics, and environmental policy modeling. Over 15 peer-reviewed articles spanning optimal control, PDEs, and economic applications. Grants & Leadership: Coordinator of Italian GNAMPA projects on MFGs and environmental models. Organized conferences including AMASES 2022 and special sessions at international optimal control conferences. Referee for top journals like SIAM J. Control Optim. and J. Mathematical Economics. Teaching: Courses include 'Mathematics for Management,' 'General Mathematics,' and PhD-level 'Static and Dynamic Optimization.' Oversees tutoring programs and international collaborations via ERASMUS+.

Lea Petrellaمشاهده پروفایل

استاد

• Quantile regression
• Graphical models
• Random Forest
• +۳ مورد دیگر

Lea Petrella is a Full Professor at the Department of Methods and Models for Economics, Territory, and Finance, Sapienza University of Rome. She teaches courses in Time Series Analysis and Advanced Statistical Methods , focusing on practical applications using R software. Research Interests: Quantile regression, Graphical models, Hidden Markov Models, Risk measures, and Time Series analysis Key Projects: Generalized Dynamic Graphical Models for pandemic impacts, Penalized quantile regression for risk assessment, Multivariate quantile regression frameworks Her recent publications include: 2025: Mid-quantile mixed graphical models for public shootings 2025: Spatial quantile random forests for economic mobility 2024: Expectile hidden Markov models for cryptocurrency returns 2024: Mixed-frequency quantile regressions for risk forecasting She supervises postdocs and PhD students including Maria Saiz, Beatrice Foroni, and Valentina Raponi. Her work spans financial risk modeling, environmental statistics, and biomedical applications. Email: Lea.Petrella@uniroma1.it or lea.petrella@uniroma1.it