جست‌وجوی استاد و پژوهشگر

Giulia Giordanoمشاهده پروفایل

استاد

• Automatic control
• Complex networks
• Control systems
• +۱۲ مورد دیگر

Giulia Giordano is a Full Professor in the Department of Industrial Engineering at the University of Trento, Italy, where she leads the Dynamical Networks and Systems Biology research group. She also holds a dual appointment as Visiting Professor and Delft Technology Fellow at the Delft Center for Systems and Control, Delft University of Technology, The Netherlands. Her career includes previous positions as Assistant Professor at Delft University of Technology (2017-2019), Postdoctoral Research Fellow at Lund University, Sweden (2016-2017), and Research Fellow at the University of Udine, Italy (2016). Giulia earned her Ph.D. in Industrial and Information Engineering: Automation (Excellent) from the University of Udine with a thesis titled "Structural Analysis and Control of Dynamical Networks." She completed her M.Sc. and B.Sc. in Electrical Engineering (both Summa cum laude) at the same institution. She also undertook research visits at Caltech (2012) as a SURF Fellow and at the University of Stuttgart (2015) as a DAAD Research Scholar. Her primary research focuses on the analysis and control of dynamical networks with applications in systems biology, mathematical ecology, and mathematical epidemiology. She develops mathematical frameworks that bridge control theory, network theory, and dynamical systems to address complex problems in biological systems. Her recent work spans epidemic modeling, opinion dynamics, biochemical networks, and neurological disorders, with a particular emphasis on structural analysis of networked systems. She employs both theoretical and computational approaches to understand system behavior under uncertainty. Giulia's publications reveal a strong interdisciplinary focus, spanning from theoretical control systems to practical applications in epidemiology and biology. Her recent work shows increasing emphasis on epidemic modeling (particularly related to mpox and SARS-CoV-2), network synchronization, and the application of control theory to biological phenomena like fibromyalgia pathogenesis and opinion formation. Many of her papers appear in top-tier control journals including Automatica and IEEE Transactions on Automatic Control. 2024: Outstanding Service as Associate Editor of IEEE Control Systems Letters 2021: SIAM Activity Group on Control and Systems Theory Prize 2020: Outstanding Reviewer, Annals of Internal Medicine 2017: NAHS Best Paper Prize and EECI PhD Award 2016: Outstanding TAC Reviewer, IEEE Transactions on Automatic Control Giulia actively mentors students and postdoctoral researchers, currently supervising five postdoctoral researchers and two Ph.D. students at the University of Trento. She has advised numerous M.Sc. and B.Sc. students on topics ranging from bio-inspired modeling to optimal control of epidemic systems. Her research is supported by competitive grants including the ERC Starting Grant INSPIRE (Integrated Structural and Probabilistic Approaches for Biological and Epidemiological Systems). She serves as Associate Editor for IEEE Control Systems Letters and Automatica, and is a Senior Member of IEEE and the Control Systems Society. Giulia leads the Dynamical Networks and Systems Biology research group at the University of Trento, which maintains strong international collaborations across Europe and North America. The group's work combines theoretical advances in control theory with practical applications to pressing problems in public health and biological systems, demonstrating the power of mathematical approaches to understanding complex phenomena in the life sciences.

Gabriele Pulciniمشاهده پروفایل

استادیار

• Proof Theory
• Philosophy of Mathematics
• Hilbert's 24th Problem
• +۶ مورد دیگر

Gabriele Pulcini is an Assistant Professor in Logic and Philosophy of Science at the Philosophy Department, University of Rome 'Tor Vergata'. He holds dual PhDs in Philosophy and Theory of Human Sciences from the University of Rome 3 and in Discrete Mathematics from the University of Aix-Marseille II. Since 2020, he has served as a council member of the Italian Society for Logic and Philosophy of Science (SILFS). Previously, he was a postdoctoral researcher at the Institute for Logic, Language and Computation (ILLC) in Amsterdam, and has taught master courses in proof theory and set theory at institutions including the Sorbonne, University of Campinas, and the University of Lisbon. His research focuses on proof theory, Hilbert's 24th problem, and the philosophy of mathematics, with contributions to linear/nonmonotonic logics, refutation calculi, and Gödel's incompleteness theorems. His work bridges formal logic with interdisciplinary applications, such as integrating logical frameworks into molecular biology. Key themes include deductive saturation, context-sensitiveness in logical systems, and foundational questions in mathematical logic. Pulcini's publications explore advanced topics like cut-elimination, complementary proof nets, and fractional semantics, often addressing the interplay between classical and non-classical logics. His recent work emphasizes innovations in refutation systems and their philosophical implications.

• Algorithms on strings
• Formal language theory
• Bioinformatics
• +۳ مورد دیگر

Chiara Epifanio is a researcher (INFO-01/A) at the University of Palermo , based in the Department of Mathematics and Computer Science . She holds regular office hours on Tuesdays 14:30–17:00 in Room 104, first floor, Via Archirafi 34, and can be reached at chiara.epifanio@unipa.it . Over the past fifteen years she has designed and taught a diverse portfolio of courses spanning Programming , Bioinformatics , Advanced Programming , Teaching Methodologies and Techniques for Computer Science and Pattern Discovery for Life Sciences . These offerings serve degree programmes in Mathematics, Computer Science, Statistics & Data Science, and the recently launched curricula in Data, Algorithms & Machine Intelligence and Computer Science & Artificial Intelligence . Her research lies at the intersection of combinatorics on words , string algorithms , and bioinformatics . She has made sustained contributions to the design of alignment-free distances for biological data, the theory of Sturmian words and their associated graphs, suffix automata tolerant to mismatches, and compact data structures such as linear-size suffix tries. The work repeatedly draws on deep results from formal language theory, automata theory, and discrete mathematics to solve practical problems in sequence analysis. Publications trend: From 2004 onward she has produced a steady stream of peer-reviewed articles that move from foundational combinatorial results on Sturmian words and critical factorization theorems toward application-driven studies on approximate string matching and genomic data mining. A marked acceleration is visible in the 2023 publications addressing k-Hamming and k-edit distances, reflecting current demands in large-scale biological data analytics. Scientific awards: None explicitly mentioned in the provided material. Advising & grants: No PhD or Master’s students are listed in the supplied pages, and no funded project descriptions are available. Laboratories & teams: While no specific lab is named, her teaching and research are embedded within the Department of Mathematics and Computer Science, which hosts groups in algorithms, discrete mathematics, and bioinformatics, and provides access to the university’s ATeN Center and other research infrastructures.

• Graph Algorithms
• Graph Problems from Computational Biology
• Graph Problems from Sensor and Ad-Hoc Wireless Networks
• +۳ مورد دیگر

Tiziana Calamoneri serves as a Full Professor within the Department of Computer Science at Sapienza University of Rome. Her academic appointment is active, with office hours held at Viale Regina Elena, 295 (Building E), 2nd floor, room 207. She can be reached via email at calamo@di.uniroma1.it and by telephone at +39.06.49255.424 (extension 36424). Professor Calamoneri's research is centered on graph theory and its diverse applications. Her primary interests include: Graph Algorithms Graph Problems arising in Computational Biology Graph Problems in Sensor and Ad-Hoc Wireless Networks Applicable discrete mathematics Interconnection Topologies Graph Drawing She maintains a specialized online resource for L(h,k)-labeling problems. While specific details on graduate student advising and research grants are not provided in the source material, her work represents significant contributions to theoretical computer science and applied discrete mathematics. Additionally, she serves as the Faculty Contact for Students with Disabilities and Specific Learning Disorders at her institution.

Jessica Gliozzoمشاهده پروفایل

پژوهشگر ارشد

• Bioinformatics
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Jessica Gliozzo is a research fellow in bioinformatics at the IRCCS Ca' Granda – Ospedale Maggiore Policlinico of Milan and collaborates with the AnacletoLab (Bioinformatics and Computational Biology laboratory). She holds a BSc in Medical Biotechnology from the University of Milan (2014) and an MSc in Molecular Biotechnology and Bioinformatics from the same institution (2016). Her primary research interests include: Bioinformatics Machine Learning Deep Learning applications in genomics Analysis of Next Generation Sequencing data Dr. Gliozzo's current research focuses on the analysis of Next Generation Sequencing (NGS) data from patients affected by primitive hematopoietic proliferative disorders of the skin. She is actively developing deep learning neural networks for the prediction and prioritization of regulatory variants in human diseases and creating network-based semi-supervised models to predict patients' clinical outcomes by leveraging heterogeneous biological data. Her recent publications demonstrate a strong trend toward integrating multi-omics data, developing knowledge graphs for RNA interactions, and applying advanced machine learning techniques to clinical problems, particularly in cancer research and genomic analysis. Her work spans computational biology, medical informatics, and artificial intelligence applications in healthcare, with a particular emphasis on dimensionality reduction techniques, network-based approaches, and resource-efficient computational methods. Network-based patient similarity analysis RNA knowledge graph construction Resource-limited medical image processing Large language models for protein science Multi-omics data integration Dr. Gliozzo has maintained extensive collaborations with researchers including Giorgio Valentini, Elena Casiraghi, Marco Mesiti, and Alessandro Petrini across numerous publications in reputable journals such as BMC Bioinformatics, Scientific Reports, and Frontiers in Bioinformatics, demonstrating consistent research productivity from 2017 through 2025.

Rosario Culmoneمشاهده پروفایل

استادیار

• Internet of Things
• Blockchain Technology
• Sensor Networks
• +۷ مورد دیگر

Rosario Culmone is an Assistant Professor at the University of Camerino, with a strong research focus in Internet of Things (IoT), sensor networks, and related technologies. His academic work spans multiple domains including smart city applications, blockchain implementations, and educational technology. Dr. Culmone's research interests center around IoT interoperability , sensor network optimization , and formal verification methods for intelligent environments. His work demonstrates a consistent focus on practical applications of distributed systems, with significant contributions to energy-efficient routing algorithms, blockchain-based certification systems, and ontology-driven frameworks for ambient assisted living. An analysis of his publication record reveals a clear trajectory from foundational work in wireless sensor networks and ECA rules verification toward more applied research in smart city technologies, food traceability using blockchain, and AI-enhanced educational systems. His most recent publications (2023-2025) show increased focus on practical implementations addressing real-world problems in security, education, and supply chain management. Dr. Culmone has developed several notable frameworks including the Share Pattern for IoT interoperability, NARUN for noise-adaptive routing, and IRON , a domain-specific language for programming IoT devices. His research bridges theoretical computer science with practical implementations across multiple application domains.

Alessandro De Parisمشاهده پروفایل

دانشیار

• Mathematics
• Theoretical Computer Science
• Differential Geometry
• +۴ مورد دیگر

Alessandro De Paris is an Associate Professor in the scientific-disciplinary field MAT/03 - Geometry at Link Campus University in Rome. He transitioned from a 24-year permanent research career to his current role, focusing on tensor rank and combinatorial aspects with applications in theoretical computer science and mathematics. His research intersects algebraic geometry, differential geometry, and computational mathematics. His recent publications highlight expertise in Waring decompositions, tensor analysis, and polynomial complexity. Key contributions include determining sharp bounds for ternary algebraic forms and developing mathematical models for olfactory neuron responses. Notable works address Monge-Ampère equations and geometric invariants. Current research trends emphasize algebraic geometry, computational mathematics, and interdisciplinary neuroscience applications. Collaborative efforts include co-authoring a monograph on linear connections in differentiable vector bundles.

Giuliano Grossiمشاهده پروفایل

دانشیار

• Sparse representation models
• Signal processing
• Face recognition
• +۵ مورد دیگر

Giuliano Grossi is an Associate Professor at the Department of Computer Science, University of Milan. His research focuses on sparse representation models, signal processing, and GPU algorithms with applications in biomedical engineering and affective computing. He leads projects such as 'Stairway to Elders' for elderly wellbeing and contributes to tools like pyVHR for remote health monitoring. Grossi has supervised PhD students in areas including high-performance computing and stochastic models. Notable awards include Best Paper recognitions in 2017 and 2020. His work spans interdisciplinary fields from bioinformatics networks to facial recognition systems. Education: PhD in Computer Science (1999), University of Milan Positions: Assistant Professor (2001-2022), Associate Professor (2022–present) Research Interests: Sparse recovery methods, parallel algorithms, emotion recognition via multimodal analysis, and graph-based models for learning. Projects include EU T-NOVA for network virtualization and emotion interpretation frameworks funded by MIUR. Scientific Contributions: Over 100 publications in journals like IEEE Transactions and conference proceedings. Key contributions include GPU-accelerated algorithms, network biology analysis tools (UNIPred-Web), and frameworks for photoplethysmography (pyVHR).

Riccardo Zecchinaمشاهده پروفایل

استاد

• Machine Learning
• Statistical Physics
• Computer Science
• +۹ مورد دیگر

Riccardo Zecchina is a Full Professor in the Department of Computing Sciences at Bocconi University, Milan, holding this position since 2017 after serving as Full Professor of Theoretical Physics at the Polytechnic University of Turin (2007-2017) and as research scientist and head of the Statistical Physics Group at the International Centre for Theoretical Physics (ICTP) in Trieste (1997-2007). He maintains long-term visiting affiliations with Microsoft Research (Redmond and Cambridge MA) and the Laboratory of Theoretical Physics and Statistical Models (LPTMS) at the University of Paris-Sud. His educational background includes a PhD in Theoretical Physics from the University of Turin under the supervision of Tullio Regge. Professor Zecchina's research spans the intersection of statistical physics, computer science, and machine learning, with core expertise in learning algorithms, combinatorial optimization, probabilistic message-passing, computational neuroscience, and interdisciplinary applications of statistical physics. His work emphasizes both theoretical foundations and practical implementations in modern AI and biologically constrained models, addressing fundamental questions in neural network training and information processing. His publication trajectory reveals a clear evolution from foundational work on phase transitions in satisfiability problems (1990s-2000s) to contemporary focus on deep learning optimization, loss landscape analysis, and computational biology applications (2010s-present), demonstrating consistent innovation at disciplinary boundaries. His major scientific recognitions include: ERC Advanced Grant (2011-2015) Lars Onsager Prize in Theoretical Statistical Physics (2016, awarded jointly with M. Mezard and G. Parisi by the American Physical Society) Professor Zecchina leads significant research initiatives including his ERC-funded work and co-founded Bocconi University's Department of Computing Sciences as an interdisciplinary hub for information and computation research. While specific student mentorship details are not publicly enumerated, his extensive collaborative publication record indicates active supervision of researchers across multiple institutions. The department operates under principles of openness and collegiality, fostering cross-disciplinary teams tackling fundamental modeling problems in computation.

Georgia FARGETTAمشاهده پروفایل

پژوهشگر ارشد

• Optimization
• Machine Learning
• Game Theory
• +۹ مورد دیگر

Georgia Fargetta is a Research Fellow (RTD-A) in Computer Science at the Department of Mathematics and Computer Science, University of Catania, Italy. She holds a PhD in Computer Science (2022) and both Bachelor's (2017) and Master's (2019) degrees in Mathematics from the University of Catania. Current academic affiliation: Department of Mathematics and Computer Science, University of Catania Academic rank: Research Fellow RTD-A (INF/01) Research themes: Optimization, Machine Learning, Game Theory, and applications to supply chain networks and crowd evacuation modeling Research Interests Georgia's research spans multiple domains including: Optimization Game Theory Metaheuristic Algorithms Crowd Simulation Medical Supply Chain Her publications demonstrate expertise in applying these methodologies to diverse problems such as social media content competition, emergency evacuation planning, and medical supply allocation. The scientific awards section highlights: Young Women in Operations Research Award (EURO WISDOM, 2021) Young Women for Operational Research (2022) She actively participates in international conferences including ODS, EUROPT, and MIC. As member of IPLAB (Image Processing Laboratory), she contributes to research in Computer Vision and Multimedia.

Luca Becchettiمشاهده پروفایل

دانشیار

• Algorithms
• Graph Theory
• Network Science
• +۷ مورد دیگر

Luca Becchetti is an Associate Professor in the Department of Computer, Automatic, and Management Engineering at Sapienza University of Rome, where he has been a core faculty member since 2012. He holds a PhD and M.Sc. in Computer Engineering from the same institution and has been actively contributing to research in algorithms and network science. PhD in Computer Engineering, Sapienza University of Rome, 1998 M.Sc. in Computer Engineering, Sapienza University of Rome, 1995 His research focuses on the design and analysis of algorithms for NP-hard and online optimization problems, with a strong emphasis on network design and resource allocation. More recently, his work has evolved to include large-scale information network analysis, such as the Web and Internet. Current research directions involve distributed graph clustering using spectral graph theory, modeling of complex systems, and pioneering applications in Network Medicine through advanced graph mining and spectral techniques. He is a member of research groups in Computer Networks and Pervasive Systems, Algorithms and Data Science, and the Theory of Deep Learning. His recent publications span areas of opinion dynamics, network medicine, and distributed algorithms, demonstrating a trend toward interdisciplinary research combining theoretical computer science with biological and social network applications. Key themes include synchronization in minority dynamics, robustness in opinion models, and multi-omics integration for disease gene prediction. He has been involved in significant research projects including the EU FET-OPEN COSIN, EU IST APPOL II, and the National FIRB WEBMINDS. While no formal list of advisees is provided, he supervises students in algorithms and data science. He teaches courses such as Big Data Computing and Web Information Retrieval. Luca Becchetti is affiliated with the Algorithm Engineering group and the Department of Computer, Automatic, and Management Engineering (DIAG), where he leads research in distributed algorithms and network analysis.

• Distributed Systems
• High-Performance Computing
• Fault Tolerance
• +۵ مورد دیگر

Ozalp Babaoglu is a Full Professor at the Department of Computer Science and Engineering, University of Bologna. He has held this position since 1987, following a PhD in Computer Science from the University of California, Berkeley in 1981. His research focuses on distributed systems, High-Performance Computing (HPC), fault tolerance, self-organization, and machine learning applications in data-driven autonomics. He has led numerous European research projects like BROADCAST, CABERNET, ADAPT, BISON, and DELIS, contributing to foundational work in biology-inspired distributed algorithms and gossip-based systems. He co-founded the Bertinoro International Center for Informatics (BiCi) in 2001 and the IEEE SASO conference series in 2007. Babaoglu is a Fellow of the ACM (2002) and has received prestigious awards, including the Sakrison Memorial Award (1982) and the USENIX Lifetime Achievement Award (1993). His contributions to BSD Unix and open standards are seminal, including virtual memory extensions during his PhD. Babaoglu has served on editorial boards for ACM Transactions on Autonomous and Adaptive Systems, and previously for ACM Transactions on Computer Systems and Distributed Computing. His recent work addresses HPC fault classification, data-driven resource allocation, and energy-efficient computing in hybrid systems. He actively advises on European projects and explores cognitive paradigms in distributed systems.

Maksim Kitsakمشاهده پروفایل

دانشیار

• Network Science
• Complex Networks
• Hyperbolic Geometry
• +۶ مورد دیگر

Maksim Kitsak is an Associate Professor (tenured) at the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. He serves as the M.Sc. Program Coordinator for the Wireless Communication and Sensing track and is an Editorial Board Member for Nature Scientific Reports and IEEE Transactions on Network Science and Engineering. He also co-chairs the Dutch Network Science Society. Dr. Kitsak earned his Ph.D. in Physics from Boston University in 2009, advised by Prof. H.E. Stanley and Prof. S. Havlin. His academic career includes positions at Northeastern University, Dana-Farber Cancer Institute, and University of California San Diego before joining Delft University of Technology in March 2020 as a tenure-track Assistant Professor, achieving tenure in May 2022. His research spans network science with expertise in complex networks, hyperbolic geometry, epidemic spreading, and network resilience. He has pioneered work on using latent geometric spaces to understand network structure and function, with applications in communication networks, pandemic forecasting, and molecular biology. His influential 2010 Nature Physics paper on identifying influential spreaders in networks challenged conventional wisdom and spurred significant follow-up research. His publication record shows a strong trajectory with recent work focusing on hyperbolic geometry applications across multiple dimensions, network resilience in transportation systems, and epidemic modeling. His research has appeared in high-impact journals including Nature Communications, Nature Scientific Reports, and Physical Review journals, demonstrating both theoretical depth and practical applications. NWO VICI research grant for studying shortest paths in large incomplete networks Dutch Research Council (NWO) funding for "Mathematical Description and Inference of Complementarity Mechanisms in Complex Networks" Seed funding from TU Delft Safety and Security Institute for privacy and security theory Dr. Kitsak actively mentors students including Ph.D. candidates Roberto Gheda (Federated Learning, AI, and Privacy), Yongdin Tian (decentralized Machine Learning), and Elizaveta Evmenova (complementarity principles in networks), as well as M.Sc. students. His research group at Delft University of Technology focuses on advancing network science theory while applying insights to real-world problems in communication networks, network medicine, and pandemic response, with two current postdoctoral openings funded by the NWO VICI grant.

• Combinatorial Optimization
• Meta-heuristics
• Mixed Integer Programming
• +۷ مورد دیگر

Luigi De Giovanni is an Associate Professor of Operations Research at the Department of Mathematics, University of Padua, where he has been faculty since 2007 (promoted to Associate Professor in 2018). He holds the Italian academic qualification as Full Professor since 2020. His research focuses on Combinatorial Optimization, Meta-heuristics, Mixed Integer Programming, and their applications in Transportation and Logistics, Air traffic and Airport Optimization, Telecommunication Network Design, and Production Scheduling. His work bridges theoretical optimization methods with practical industrial applications across various sectors including transportation, manufacturing, and telecommunications. Professor De Giovanni has led and participated in several significant research projects including: European Research Project 'OptiFrame - An Optimization Framework for Trajectory Based Operations' (H2020-SESAR-2015-1, 2016-2018) PRIN Project 'Nonlinear and Combinatorial Aspects of Complex Networks' (2017-2020) European Research Project 'AAS - Integrated Airport Apron Safety Fleet Management' (2008-2011) European Research Project 'SCOOP - Sheet Cutting and Process Optimization for Furniture Enterprises' (2006-2008) His extensive publication record spans from 2002 to 2024, with numerous articles in high-impact journals such as Transportation Science, IEEE Access, and European Journal of Operational Research. Recent publications show a strong emphasis on data-driven approaches to air traffic management, vehicle routing and logistics optimization, and graph theory applications, demonstrating continued research productivity and relevance. Professor De Giovanni teaches courses in Operations Research, Methods and Models for Combinatorial Optimization, and Stochastic Optimization at the University of Padua. His office hours are on Thursdays from 10:30 am to 12:30 pm by appointment.

• Combinatorial optimization
• Statistical physics
• Epidemic inference
• +۷ مورد دیگر

Alfredo Braunstein is an Associate Professor at the Department of Applied Science and Technology (DISAT), Politecnico di Torino, and a member of SmartData@PoliTO laboratory. His research spans combinatorial optimization, statistical physics of complex systems, epidemic inference, and computational biology . He leads the Simultaneous Inference from Multiscale Biological Data (SIMBAD) project (2023-2027) and previously led Statistical Inference via Belief Propagation for Epidemics (SIBYL) (2015-2017). He has supervised multiple PhD students in Physics of Complex Systems . Research Interests : Statistical physics, machine learning, network science, epidemic modeling, and optimization. Key Collaborations : Collegio Carlo Alberto, Human Genetics Foundation, and interdisciplinary teams. His recent publications focus on epidemic risk estimation , network dismantling , and message-passing algorithms . He received the La Ricerca dei Talenti award from Fondazione CRT (2015).