Find Academic Supervisors

Alessio MicheliView profile

Professor

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +5 more

Alessio Micheli is a Full Professor in the Department of Computer Science at the University of Pisa. His research focuses on Machine Learning, Artificial Intelligence, and Deep Learning, with emphasis on structured data like graphs and sequences. He leads the Computational Intelligence and Machine Learning (CIML) group, which develops innovative models for graph data analysis and reservoir computing. **Research Interests:** Machine Learning, Graph Neural Networks, Deep Learning, Reservoir Computing, Bioinformatics, Cheminformatics, and applications in healthcare and robotics. His work includes pioneering contributions to neural networks for graphs and dynamic graph analysis. **Teaching:** He teaches courses on Machine Learning, Computational Neuroscience, and Artificial Intelligence at both undergraduate and graduate levels. Notable courses include 'Machine Learning Fundamentals' and 'Advanced Neural Networks.' **Key Projects:** Includes EU-funded initiatives like TAILOR (Trustworthy AI) and BrAID (AI for Cardiac Diagnosis). He coordinates national and international research groups, including the MLDM.IT working group and the IEEE Task Force on Reservoir Computing. **Grants and Labs:** Active in projects on AI for healthcare, wastewater quality, and robotics. His team collaborates with industries like FOSBER and MAIOR on applied machine learning solutions.

Paolo MilazzoView profile

Associate Professor

• Complex Systems Modelling and Simulation
• Computational Systems Biology
• Biomedical Informatics
• +3 more

Paolo Milazzo is an Associate Professor at the Department of Computer Science, University of Pisa. His research focuses on Complex Systems Modelling , Computational Systems Biology , and Biomedical Informatics . He leads the BioMedical & Health Informatics Lab and the Research Group on Biosystems Modelling. He holds roles in institutions like the Toscana Life Sciences Foundation and the Centro 3R. Education: Master's from University of Bologna (2003), PhD from University of Pisa (2007) Teaching: Courses on Programming, Computational Models, and Systems Biology Research Projects include PAN-HUB (combating pandemics and antibiotic resistance), THE (innovation ecosystems), and HaloFarMs (precision agriculture). He co-chairs conferences like CMSB 2024 and IntSys 2024. Publications span over 130 Scopus-indexed works in journals and conferences, with a focus on computational biology and formal methods. His tools like RettDB and ProMI advance biomedical research.

Franco TuriniView profile

Professor

• Programming Languages
• Logic Programming
• Functional Programming
• +7 more

Franco Turini is a Full Professor in the Department of Computer Science at the University of Pisa, where he has been a central academic figure for decades. He has held major leadership roles including Director of the Department of Computer Science (1996–2002, 2010–2016), President of GRIN, and Academic Senator since 2011. He has also served on national research commissions such as the FIRB Commission of the Italian Ministry of University and Research. Research Interests: His work spans programming languages, formal semantics, logic and functional programming, and more recently, explainable AI (XAI), fairness in AI, and data mining with constraints. He has led national and international research initiatives and co-edited influential works such as Meta-logics and Logic Programming with Krzysztof Apt. The recent publications (2014–2022) show a strong focus on XAI, with a trend toward developing interpretable, rule-based, and counterfactual explanation methods for black-box AI systems. His work frequently appears in top venues in AI and data mining, emphasizing transparency, fairness, and actionable insights. Scientific Awards: Ordine del Cherubino, University of Pisa (2007) Advising and Grants: While specific students are not listed, Turini has been a principal investigator in major research projects such as XAI (ERC Advanced Grant), NoBIAS, ICON, and ANONIMO. He has secured significant national and European funding, including leadership in national programs sponsored by the Italian National Research Council. Labs and Teams: He is affiliated with the KDD Lab at the University of Pisa, a leading research group in knowledge discovery and data mining, collaborating closely with Fosca Giannotti, Dino Pedreschi, and others in advancing ethical and explainable AI.

Giovanna Maria DimitriView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +7 more

Giovanna Maria Dimitri is a tenure-track Assistant Professor in Artificial Intelligence at the Università degli Studi di Milano (Statale), with strong affiliations at the University of Siena (DIISM) and the University of Cambridge. She conducts research in deep learning, machine learning, and AI applications across domains such as computer vision, bioinformatics, climate modeling, and healthcare. She also lectures in Business Intelligence at the University of Siena and serves as a Guest Lecturer in Data Science at the University of Cambridge's Institute of Continuing Education. PhD in Artificial Intelligence, University of Cambridge MPhil in Advanced Computer Science (Distinction), University of Cambridge Master's and Bachelor's in Computer and Automation Engineering (110/110 cum laude), University of Siena Her research spans foundational AI models and their applications in diverse fields including neuroscience, environmental science, and medical diagnostics. She develops methodologies in deep learning, graph neural networks, and multimodal data analysis, with a growing interest in sustainable AI and ethical implications of machine learning systems. Her recent publications reflect a strong trend in applying AI to real-world challenges: from detecting synthetic images and analyzing brain signals to modeling climate impacts and assessing sustainable development goals. Her work bridges theoretical innovation with practical implementation across healthcare, environmental monitoring, and digital humanities. Scientific Awards and Editorial Roles: Ai-Net Fellows Scholarship (DAAD, 2023) Associate Editor, Neurocomputing (Elsevier) Associate Editor, IEEE Transactions on Technology and Society (since May 2024) Giovanna has extensive experience in teaching, supervising, and science communication. She has delivered seminars at top institutions like the University of Cambridge and has been featured in Italian media for her AI expertise. She mentors students and collaborates internationally, including with Prof. Gemma Roig at the DAAD-sponsored Ai-Net program. She is actively involved in academic service, conference organizing, and public engagement, contributing to both technical and societal aspects of AI advancement. She is a life member of Clare Hall College, University of Cambridge, and maintains active research collaborations across Europe. Her lab and research group focus on developing robust, interpretable, and sustainable AI systems, with ongoing projects in medical AI, climate informatics, and ethical machine learning.

Francesco CauteruccioView profile

Assistant Professor

• Social and Complex Network Analysis
• Data Mining and Data Science
• Internet of Things (IoT)
• +3 more

Francesco Cauteruccio is a Tenure-Track Assistant Professor at the Department of Information Engineering, Electrical Engineering and Applied Mathematics (DIEM) at the University of Salerno, Italy. He is affiliated with the CORE and S 3 labs, focusing on interdisciplinary research at the intersection of social networks, IoT, and hybrid AI models. Ph.D. in Mathematics and Computer Science, University of Calabria (thesis: "Generalizing Identity-Based String Similarity Metrics: Theory and Applications") Italian National Scientific Qualification (ASN) for Associate Professor in Computer Science and Computer Engineering Visiting researcher at University of Klagenfurt (AI & Cybersecurity) and University of Lyon 1 (CERMEP/CREATIS) His research spans: Social & Complex Networks: Modeling cross-blockchain ecosystems, Reddit community dynamics, and sentiment propagation IoT & Hybrid Intelligence: Scope formalization, anomaly detection in MIoT networks, and air quality prediction systems Logic Programming: Integrating Answer Set Programming with machine learning for medical informatics and pattern mining Algorithm Design: Multi-Parameterized Edit Distance (MPED) for bioinformatics and network analysis Recent publications address network theory applications, hybrid reasoning frameworks, and high-utility pattern mining with formal constraint specification. He serves as guest editor for journals like Big Data and Cognitive Computing and Frontiers in Medical Engineering , and organizes international workshops on hybrid intelligence (HYDRA, VIPERC). Academic distinctions: Italian National Scientific Qualification for Associate Professor positions (2024) Guest Editor for 12+ special issues in data science and IoT journals Program/General Chair for 8 international workshops (2022-2024) His teaching portfolio includes courses on algorithms, distributed systems, and data analytics at University of Salerno, Polytechnic University of Marche, and University of Calabria.

• Theoretical Physics
• Statistical Physics
• Computational Biology
• +3 more

Pierre Barrat-Charlaix serves as a Fixed-term Assistant Professor in the Department of Applied Science and Technology (DISAT) at the Polytechnic University of Turin, where he is affiliated with the Institute of Condensed Matter Physics and Complex Systems. He holds invited committee positions in Chemical and Materials Engineering and Mechanical, Aerospace, and Automotive Engineering programs, and collaborates on Physics I instruction for Automotive Engineering across multiple academic years (2023/24–2025/26). His research integrates Theoretical Physics, Statistical Physics, and Computational Biology to address complex biological systems. Key interests include viral evolution modeling, protein-sequence analysis using machine learning, and statistical inference methods for multiscale biological data, with strong emphasis on interdisciplinary approaches bridging physics and life sciences. Recent publications reveal a cohesive trajectory in computational biophysics: developing TreeKnit for influenza reassortment graph inference, analyzing predictability limits in viral mutations, and advancing sparse Boltzmann machine applications for protein families. This work consistently applies statistical physics frameworks to biological sequence data, demonstrating methodological innovation in evolutionary modeling and generative AI for biomedicine. He actively contributes to the SIMBAD project (2023–2027), funded by Horizon Europe's Marie Skłodowska-Curie Actions, which focuses on statistical inference from multiscale biological data. Teaching responsibilities include Physics I course collaboration within Automotive Engineering, reflecting his commitment to interdisciplinary technical education. Research operations are centered within DISAT's Institute of Condensed Matter Physics and Complex Systems, where his work leverages institutional strengths in theoretical modeling and data science for biological applications.

Raffaella BurioniView profile

Professor

• Statistical Physics
• Complex Networks
• Random Walks
• +5 more

Raffaella Burioni is a Full Professor of Theoretical Physics in the Department of Mathematics, Physics and Computer Science at the University of Parma. She serves as Chair of the Non-Linear and Statistical Physics Division of the European Physical Society (EPS) and co-founded the Italian Society of Statistical Physics (SIFS), where she holds the position of Vice President. Her academic leadership includes directing the Ph.D. School in Physics and previously managing quality assurance for the Master's program in Physics (2017-2023). Her educational background includes an MS in Physics with distinction from the 2nd University of Rome and a PhD in Theoretical Physics from the University of Rome 'La Sapienza'. Postdoctoral experience spans the Theoretical Physics Laboratory of the École Normale Supérieure in Paris, the University of Milan, and the National Institute for the Physics of Matter (INFM). Prof. Burioni's research centers on equilibrium and non-equilibrium statistical physics, with deep expertise in graph theory, complex networks, random walks, and stochastic processes. She pioneers interdisciplinary applications to biological systems, neuroscience, and machine learning. Her work on rare events and anomalous diffusion has established fundamental principles like the 'single big jump' mechanism in transport phenomena. Recent investigations bridge statistical physics with neural network theory, examining kernel renormalization and feature learning in deep architectures. Analysis of her 15 most recent publications (2022-2025) reveals three dominant trends: (1) Theoretical advances in rare event statistics for jump processes and extreme value theory, (2) Network-based epidemic modeling incorporating adaptive temporal dynamics and simplicial structures, and (3) Machine learning physics connecting neural network theory to statistical mechanics through Bayesian effective actions and kernel methods. Her work consistently demonstrates how statistical physics principles solve complex problems across disciplines. Her scientific accolades include: Two-time recipient of the Enrico Persico Prize from the Accademia Nazionale dei Lincei Fellow of the Institute for Scientific Interchange (ISI) since 2014 American Physical Society Outstanding Referee (2018) Fellow of the European Centre for Living Technology (2020) As Director of the Ph.D. School in Physics, she mentors doctoral candidates while serving on editorial boards for Physical Review E, JSTAT, and Journal of Physics A. Her research is supported by international collaborations with institutions including the Kavli Institute for Theoretical Physics, Max Planck Institute, and Ben-Gurion University, evidenced by frequent keynote invitations at major conferences like StatPhys, ECCS, and APS March Meetings. Prof. Burioni leads collaborative research networks through her EPS division chairmanship and SIFS leadership, fostering cross-institutional projects on statistical physics applications. Her campus-based studies leverage Wi-Fi data from Parma University to model pedestrian dynamics and epidemic spreading in real-world constrained environments.

Maksim KitsakView profile

Associate Professor

• Network Science
• Complex Networks
• Hyperbolic Geometry
• +6 more

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.

Frank TakesView profile

Associate Professor

• Network Science
• Computational Social Science
• Graph Algorithms
• +5 more

Frank Takes is an Associate Professor at the Leiden Institute of Advanced Computer Science (LIACS) , part of the Faculty of Science at Leiden University. He leads the Computational Network Science research group and serves as academic co-director of the PLANET-NL platform for population-scale social network analysis. He is also a board member of the Dutch Network Science Society and director of education for Leiden's Informatics and Data Science & Artificial Intelligence bachelor programs since 2022. His research focuses on algorithms for large-scale social network analysis , with applications in computational social science, economics, and science studies. He co-ordinates the development of disclosure risk-aware methods for combining social network datasets (e.g., microdata registers, social media) as part of the NWO SSHOC-NL research infrastructure project (2024-2028) . He also co-leads the ANO-NET project (2021-2025) , studying anonymity in social networks via structural position and attributes. Contact information: Leiden : Gorlaeus room BE 3.07, Phone: +31 (0)71 527 7143 Amsterdam : takes@uva.nl

• Combinatorial Optimization
• Meta-heuristics
• Mixed Integer Programming
• +7 more

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.

Adam Teodor PolakView profile

Assistant Professor

• Algorithms
• Fine-Grained Complexity
• Learning-Augmented Algorithms
• +3 more

Adam Teodor Polak serves as an Assistant Professor in the Department of Computing Sciences at Bocconi University, where his research centers on theoretical algorithms with dual emphases on fine-grained complexity and learning-augmented algorithms. His work investigates fundamental questions about computational hardness while developing prediction-enhanced algorithms that maintain worst-case guarantees. Polak earned his PhD from Jagiellonian University in 2019 under Paweł Idziak, including a research visit at MIT with Virginia Vassilevska Williams. He subsequently held postdoctoral positions at the Max Planck Institute for Informatics and EPFL before joining Bocconi. His research program addresses why computational problems resist efficient solutions and how imperfect predictions can robustly improve algorithmic performance. This manifests in two interconnected streams: establishing conditional lower bounds for problems like 3SUM and Orthogonal Vectors, and designing learning-augmented frameworks for dynamic graph problems, caching, and optimization that blend theoretical rigor with practical machine learning insights. Recent publications reveal accelerating momentum in algorithms with predictions, with over half of his 2023-2025 output appearing in top ML venues (ICML, NeurIPS, ICLR) alongside traditional theory conferences (STOC, SODA). This cross-pollination demonstrates how worst-case theoretical guarantees can coexist with data-driven performance gains across graph algorithms, scheduling, and combinatorial optimization. Scientific recognition includes: Best Paper Award at ESA 2024 for knapsack algorithm breakthroughs Bronze Medal at ACM ICPC World Finals (2011) 2nd Place in PACE 2018 Challenge for Steiner tree algorithms Polak actively shapes the field through program committee service (ESA, ICALP, SOSA) and community building, notably co-organizing the 2022 Workshop on Algorithms with Predictions (ALPS) and decade-long high-school algorithmics workshops. His industry collaborations with Teroplan and Google demonstrate real-world impact in route planning and distributed systems. Current teaching includes graduate Algorithms courses at Bocconi, while his experimental work on GPU-accelerated graph algorithms and medical computer vision continues to bridge theoretical insights with practical implementation challenges.

• Computational Logic
• Decidability Theory
• Algorithm Design
• +4 more

Gianluca Cincotti serves as an Assistant Professor of Informatics [INFO-01/A] at the Department of Mathematics and Computer Science, University of Catania. He maintains regular office hours on Tuesdays and Thursdays from 10:00 to 11:00 in Block III, Room 53 of the Mathematics and Computer Science Department building. His primary research focuses on two major areas: decidability theory and algorithms and data structures . In decidability theory, his work centers on defining and proving decidable theories with applications to automatic demonstration systems, particularly examining fragments of real analysis and graph theory. His algorithmic research includes developing in-place sorting algorithms and approximation algorithms . He has authored scientific publications in international journals within these specialized fields. Dr. Cincotti teaches Programming I and laboratory courses at the University of Catania, with teaching activities documented from the 2022/2023 academic year to present. His educational background includes a graduate degree in Computer Science, and he has also taught in humanistic sectors and professional training courses, demonstrating interdisciplinary teaching capabilities.

Michela QuadriniView profile

Assistant Professor

• Bioinformatics
• Computational Biology
• RNA Secondary Structures
• +3 more

Michela Quadrini is an Assistant Professor at the University of Camerino with expertise spanning Bioinformatics , Computational Biology , and Graph Neural Networks . Her academic work focuses on RNA structure analysis, human activity recognition, and formal methods for collective adaptive systems. Research Interests include: RNA pseudoknots comparison and classification Machine learning applications in biomedical signal processing Spatial logics for complex system modeling Protein-protein interaction site prediction Ontology-based frameworks for activity recognition Publications demonstrate methodological innovation across: RNA structure alignment and translation tools (TARNAS) Graph neural network programming languages (μG) Stress detection from wearable sensor data Formal verification of collective systems Immunoinformatics feature engineering Technical Expertise combines computational biology with advanced machine learning techniques, evidenced by contributions to: Topological data analysis for RNA structures Convolutional neural network architectures Semantic ontologies in mechatronic systems Integral equation numerical methods

Paolo BaldanView profile

Professor

• Formal Methods for Software Reliability
• Programming Languages
• Concurrency Theory
• +6 more

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.

• Artificial Intelligence
• Machine Learning
• Neural-Symbolic AI
• +5 more

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.