Find Academic Supervisors

• Social Network Analysis
• Network Science
• Data Science
• +7 more

Giacomo Fiumara is an Associate Professor at the University of Messina, Department of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences. He holds academic rank since October 2021. Previously, he served as a Permanent Researcher (2008–2021) and secondary school teacher (1997–2008). He earned a Doctorate in Physics (1993) and a Degree in Physics (1989), both from the University of Messina. He is an associate member of the Accademia Peloritana dei Pericolanti and qualified as an associate professor in INF/01 and ING-INF/05 sectors. His research focuses on social network analysis, network science, data science, criminal networks, knowledge representation, bioinformatics, and computational modeling. He has supervised over 170 theses and advised PhD students in Mathematics and Computational Sciences. Key collaborations include work with Prof. Pasquale De Meo on criminal networks and complex systems, and international projects with institutions in the US, UK, China, and Australia. Teaching includes courses on Algorithms, Data Structures, Bioinformatics, and Machine Learning across Computer Science, Engineering, and Medical programs since 2000. He also contributed to international programs at Lviv Polytechnic, Birzeit University, Cluj-Napoca, and Murcia. His editorial roles include Associate Editor of IEEE Access and Academic Editor of Complexity. He holds a patent for predictive analysis of criminal organizations' social structures and has received FFABR research funding. Key awards include FFABR funding (2017) and recognition in the FFABR Unime 2020 II edition. He organized conferences like Crimenet 2014 and participated in high-profile events such as the 2022 Complex Networks conference in Palermo, presenting on quantum walks for criminal network analysis.

• Algebra
• Representation Theory
• Category Theory
• +4 more

Enrico Gregorio serves as an Associate Professor in the Department of Computer Science at the University of Verona, Italy. His academic specialization is in Algebra (MATH-02/A), with research focused on representation theory of algebras. Based at Ca' Vignal 2, Floor 2, Room 16, he teaches across multiple degree programs including Mathematics, Computer Science, Bioinformatics, and Primary Teacher Education. His office hours are Tuesdays from 2:00 PM to 3:00 PM. Dr. Gregorio's scholarly work centers on advanced algebraic structures and their applications: Representation theory of algebras Tilting and cotilting theory Module categories and derived categories Category theory applications Finite and infinite dimensional algebras Cluster categories His research trajectory shows consistent focus on algebraic structures over two decades, from early work on module category equivalences to recent projects involving LaTeX formalism for academic writing. Many of his research initiatives have been supported through national funding mechanisms including PRIN grants. Dr. Gregorio actively contributes to academic governance as a member of the Computer Science Teaching Committee, Mathematics and Data Science Teaching Committee, and the Computer Science Department Council. He also participates in third mission activities including public engagement events and continuing education programs.

Damiano PiovesanView profile

Associate Professor

• Protein function prediction
• Intrinsically disordered proteins (IDPs)
• Machine learning in bioinformatics
• +5 more

Damiano Piovesan is Associate Professor in Bioinformatics (SSD BIO/10) at the Department of Biomedical Sciences , University of Padua , Italy. Since March 2022 he has held this rank, having previously served as Assistant Professor (2022) and PostDoc researcher (2019) in the same department. Education 2013 – PhD in Biotechnology, Pharmacology and Toxicology, University of Bologna 2009 – MSc in Bioinformatics, University of Bologna 2007 – BSc in Biotechnology, University of Bologna Research Focus Piovesan’s research integrates machine-learning approaches with structural bioinformatics to advance understanding of intrinsically disordered proteins (IDPs) and protein function prediction . He develops widely used resources such as MobiDB for disorder annotation, DisProt for functional curation of disordered regions, and RING for residue interaction networks. Additional interests include tandem repeat proteins , cancer-related IDP targets , and community benchmarking initiatives (CAFA, CAID, CAGI). Publication Trends His 2024–2025 output is dominated by updates to flagship databases ( InterPro , DisProt , MobiDB ), next-generation disorder predictors leveraging deep learning ( PredIDR , MobiDB-lite 4.0 ), and large-scale genomics challenges ( CAGI6 ). Across the decade, recurring themes include methodological advances in disorder prediction, creation of interoperable bioinformatics platforms, and rigorous benchmarking to ensure community-wide reliability. Scientific Awards No specific awards are listed in the provided materials. Advising & Grants No individual students or grant details are explicitly supplied; however, his leadership in multi-institutional consortia (e.g., InterPro, DisProt, CAFA) implies substantial supervisory and funding coordination roles. Labs & Teams Piovesan is affiliated with the BioComputingUP Lab ( https://biocomputingup.it/ ) at the University of Padua, a hub for computational biology and bioinformatics tool development.

• Machine Learning
• Data Mining
• Bioinformatics
• +11 more

Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.

• Statistical Mechanics
• Condensed Matter Physics
• Soft Matter Physics
• +4 more

Davide Cassi serves as Associate Professor of Physics of Matter at the University of Parma's Department of Mathematical, Physical and Computer Sciences since 2001, following his appointment as Researcher in Theoretical Physics (1995-2001). With over 30 years of academic service, he teaches Condensed Matter Physics, Soft Matter Physics, and Physics Applied to Gastronomy across undergraduate and graduate programs in Physics and Gastronomic Science. His educational background includes: Ph.D. in Physics, University of Parma (1988-1992) Master’s degree in Materials Science and Technology, University of Parma (1986-1988) Degree in Physics, University of Parma (1982-1986) Cassi's research integrates statistical mechanics with real-world applications through two primary lenses: complex network theory for ecological and social systems, and soft matter physics applied to culinary processes. His work on biodiversity loss prediction in agricultural networks and food preservation technologies demonstrates exceptional interdisciplinary reach. Recent publications reveal a strategic pivot toward AI-driven biodiversity conservation and network robustness modeling. Analysis of his 15 most recent publications (2023-2025) shows dominant themes in network vulnerability analysis (68% of works) and food-physics applications (27%), with emerging focus on machine learning integration for ecological modeling. His research bridges theoretical physics with practical solutions in food safety and ecosystem management. Key recognitions include: Grand Prix de la Science de l'Alimentation from Académie Internationale de la Gastronomie (2012-2013) Dual National Scientific Qualifications for Full Professorship (2022) in Theoretical Physics of Fundamental Interactions and Matter Cassi's academic contributions extend beyond publications to two international patents in food preservation technology and editorial leadership since 2007 for World Scientific's Series on Advances in Statistical Mechanics . His research program demonstrates consistent translation of theoretical physics into practical applications across gastronomy and ecology, with growing emphasis on AI-enhanced network analysis for sustainability challenges.

Giacomo ParigiView profile

Associate Professor

• Nuclear Magnetic Resonance (NMR)
• Paramagnetism-Based Effects
• MRI Contrast Agents
• +4 more

Giacomo Parigi is an Associate Professor of Chemistry at the University of Florence, affiliated with the Magnetic Resonance Center and the Department of Chemistry. His work focuses on paramagnetic effects in NMR, MRI contrast agents, and protein dynamics. He holds a Physics degree (1992) and a Chemistry PhD from the University of Florence, with postdoctoral and research roles at CERM since 1999. Research Interests: Parigi’s research explores paramagnetic effects in biological molecules, including protein structure determination, MRI contrast agent design, and relaxometry. He co-authored seminal books on NMR of paramagnetic molecules and pioneered methods for studying protein dynamics via field-cycling NMR. His lab integrates computational biology, bioinformatics, and experimental NMR to address biomedical challenges. Publications & Trends: Recent work emphasizes machine learning-enhanced NMR analysis, novel MRI probes (e.g., Mn-based nanogels), and structural studies of metalloproteins. His articles highlight innovations in paramagnetic NMR restraints, dynamic aggregation imaging, and low-field MRI applications. Awards & Grants: No explicit awards mentioned, but his extensive publications reflect significant contributions to NMR methodology. Active in securing grants for structural biology and biomedical imaging projects. Labs & Teams: Leads the Magnetic Resonance Center’s NMR group, collaborating on interdisciplinary projects involving biomaterials, drug design, and protein engineering. His team develops cutting-edge tools for in-cell NMR and metabolomics analysis.

• Algorithms
• Data Structures
• Pattern Matching
• +3 more

Simon J. Puglisi is a Professor in the Department of Computer Science at the University of Helsinki. His research focuses on algorithms, data structures, pattern matching, and data compression, with applications in bioinformatics and genomics. He has collaborated extensively with researchers in the field, including Travis Gagie, Juha Kärkkäinen, and Andrew Turpin. His work spans theoretical computer science and practical applications in genomic data processing and efficient indexing techniques. Key research interests include genome sequencing algorithms, efficient compression methods (e.g., Lempel-Ziv and relative Lempel-Ziv), and the development of succinct data structures for handling large genomic datasets. His contributions to suffix arrays, wavelet trees, and de Bruijn graphs have advanced computational methods in bioinformatics and information retrieval. Puglisi's articles frequently address challenges in text indexing, error correction in short-read sequencing, and optimizing algorithms for scalability. He is known for his work on self-indexing techniques and the SHREC error correction method for genomic data. His research bridges theoretical algorithm design with real-world applications in high-throughput sequencing and large-scale data management.

• New Product Development
• Knowledge Management
• Collaborative Work Environments
• +5 more

Angelo Corallo is an Associate Professor at the Department of Experimental Medicine, University of Salento, specializing in technologies and methodologies for collaborative processes in industrial systems. His research spans Digital Business Ecosystems , Cybersecurity , and Collaborative Product Design , focusing on the interplay between technology and organizational dynamics. He leads interdisciplinary research divisions in Open Networked Business Management , Learning and Innovation , and Collaborative Product Design . Research Interests : Corallo's work integrates Information and Communication Technologies (ICT) with Business Management, particularly in Digital Twins for healthcare and manufacturing Knowledge Modeling and Ontology Engineering Industry 4.0 and Smart Manufacturing Agri-Food Sustainability through digitalization Scientific Contributions : His recent articles explore trends in Cybersecurity for Industrial IoT Metaverse Applications in business models Traceability Systems in food supply chains Collagen-Based Biomaterials from aquaponics

• Structural Bioinformatics
• Protein Stability Prediction
• Machine Learning in Biology
• +3 more

Emidio Capriotti is an Associate Professor at the Department of Pharmacy and Biotechnology, University of Bologna. He leads the Second Cycle Degree in Bioinformatics and holds a PhD in Physical Sciences. His research focuses on predicting protein stability changes due to genetic variations using machine learning, with applications in personalized medicine. He develops tools like DDGun and PhD-SNPg for variant interpretation. Teaching includes courses on Bioinformatics, Biophysics, and Lab Techniques for Bioinformatics. His work bridges computational methods and biological problems, particularly in cancer genomics and protein structural analysis. Education: PhD in Physical Sciences from the University of Bologna. Research interests include structural bioinformatics, protein folding, and the interplay between genomic variations and disease. He collaborates with initiatives like CAGI challenges and ELIXIR-IT to advance genomic data infrastructure. Publications emphasize protein stability prediction, federated learning in clinical settings, and benchmarking computational methods. His lab contributes to open-source tools and standards for reproducible machine learning in biology.

• Genomics
• Transcriptomics
• Network Biology
• +7 more

Pasquale Luca Curci is a Researcher at the Institute of Biosciences and BioResources (IBBR), part of the National Research Council of Italy (CNR) in Bari. His work focuses on integrating large-scale genomic and transcriptomic datasets to uncover biological mechanisms in plants, particularly in crops like durum wheat and artichoke. His research interests span genomics, transcriptomics, and network biology, with a strong emphasis on nitrogen use efficiency, gene regulatory networks, and plant stress responses. He employs systems biology approaches to identify key genes and regulatory elements involved in plant development and adaptation. The analysis of his recent publications reveals a consistent focus on plant genomics, particularly in durum wheat and Cynara species. His work integrates omics data to understand gene function, stress responses, and evolutionary relationships. Key themes include nitrogen metabolism, microRNA regulation, chloroplast genomics, and cross-species network analysis. He has led and coordinated multiple national and international research collaborations with institutions in Belgium, Sweden, Spain, France, and Italy, focusing on gene function discovery, nitrogen efficiency, and germplasm characterization. Integrative network analysis for gene function discovery in wheat (with BASF Agricultural Solutions, Belgium) Cross-species network biology in plants (with SweTree Technologies, Sweden) Transcriptomics and regulatory networks in durum wheat (with Sequentia Biotech SL, Spain) Phenotypization and genomic analysis of durum wheat (with Alma Mater Studiorum, Bologna) Asparagine synthetase genes in durum wheat (with INRA-CNRGV, France) He has contributed to the development of genomic resources for Cynara and has published extensively on chloroplast genomics, transcriptomics under nitrogen stress, and genetic diversity in vegetable crops. His work supports crop improvement and sustainable agriculture through advanced molecular tools.

Giorgio VinciguerraView profile

Research Fellow

• Data Compression
• Algorithm Engineering
• Learned Data Structures
• +3 more

Giorgio Vinciguerra is a Research Fellow (RTD-A) at the Department of Computer Science of the University of Pisa since January 2023, and a member of the A³ Lab. His research focuses on compact data structures, data compression, and algorithm engineering, with a specialization in learned data structures that leverage machine learning to improve space-time trade-offs. He holds a PhD from the University of Pisa (2022), awarded the Best PhD thesis in Theoretical Computer Science by the Italian Chapter of EATCS. His academic journey includes postdoc research (2022), a visiting researcher role at KTH Royal Institute of Technology (2024), and Harvard University (2020). He has contributed to EU-funded projects like SoBigData.it and owns patents for innovations in data structure design. Key research interests include: Learned compression techniques for time series and string dictionaries Space-efficient indexing for massive datasets Algorithmic integration of machine learning into traditional data structures His work has been published in top venues including ICDE, Inf. Syst., and ACM Trans. Algorithms. Awards include the 2025 WSDM Outstanding Reviewer Award. He has co-supervised multiple theses on topics like adversarial query optimization and compressed indexing. Teaching roles include courses on programming, algorithms, and information retrieval at the University of Pisa. His software libraries (e.g., LeMonHash, PGM-index) are widely used in database systems and bioinformatics.

Gabriele Andrea LugliView profile

Researcher

• Microbiology
• Genomics
• Microbiome Research
• +3 more

Gabriele Andrea Lugli is a Fixed-term researcher at the University of Parma's Department of Chemical, Life and Environmental Sustainability Sciences, teaching Genomics of Microbial Ecosystems, Microbiology Applied to Materials Science, and Applied Microbiology and Probiogenomics for Biotechnology and Materials Science undergraduate and graduate programs. His research centers on microbial genomics with emphasis on human gut microbiome dynamics and food microbiology. Key interests include bifidobacteria-host molecular interactions, bacteriophage ecology in dairy fermentation, probiotic mechanisms, and microbial contributions to environmental sustainability. He employs metagenomic, genomic, and machine learning approaches to investigate microbial biodiversity in cheese production and gut health contexts. Analysis of his recent publications reveals a cohesive focus on genomic characterization of microbial communities across food and human ecosystems. His work consistently explores bifidobacterial adaptation, vitamin biosynthesis pathways, and phage-host dynamics, demonstrating translational applications in probiotics, nutrition, and food safety. The integration of computational methods with experimental microbiology defines his methodological signature.

Claudia AndreiniView profile

Associate Professor

• Metalloproteins
• Bioinformatics
• Biological Databases
• +3 more

Claudia Andreini is an Associate Professor at the University of Florence, affiliated with the Magnetic Resonance Center and the Department of Chemistry. Her research focuses on computational biology, particularly metalloproteins and bioinformatics. She pioneered the use of bioinformatics to study metalloproteins and developed the MetalPDB database. She collaborates internationally and has presented at major conferences. Education: PhD in Chemistry (2006), University of Florence; Laurea in Chemistry (2002, cum laude), University of Florence. Research interests include metalloprotein structure-function relationships, metal-coordination databases, and predictive computational tools. Her work integrates structural bioinformatics, machine learning, and database mining to analyze metal sites in proteins. Recent publications highlight advancements in zinc-binding site prediction, metal-induced structural variability, and applications of deep learning in metalloprotein analysis. She has contributed to drug design studies involving ruthenium complexes and antibacterial agents. No scientific awards or grants are explicitly listed in the provided texts. She leads the MetalPDB project and collaborates on structural biology initiatives.

• Combinatorics on Words
• String Algorithms
• Data Mining
• +3 more

Gabriele Fici is an Associate Professor at the Department of Mathematics and Computer Science, University of Palermo (Unipa). His research focuses on combinatorics on words, string algorithms, and algorithmic pattern matching. He teaches courses in text sequence analysis, data mining, and software development. Research Interests : Combinatorial pattern matching, Abelian repetitions, digital convexity, and theoretical foundations of string processing. Key Contributions : Pioneering work on Lyndon words, de Bruijn sequences, and jumbled pattern matching algorithms. Publications : Extensive publications in top-tier conferences like CPM and journals, with a focus on 2013-2025 trends in algorithmic complexity and computational linguistics.

• Computational Logic
• Answer Set Programming
• Bioinformatics
• +6 more

Alessandro Dal Palu' is an Associate Professor at the Department of Mathematical, Physical, and Computer Sciences at University of Parma. He holds a PhD in Computer Science from University of Udine and has been with University of Parma since 2005, transitioning from Researcher to Associate Professor in 2014. His teaching portfolio includes courses on Computer Architecture, Constraint Programming, and Algorithms & Data Structures. His research spans computational logic, bioinformatics, and GPU computing. Notable achievements include the 2007 GULP award for his Ph.D. thesis and the ICLP 2010 best paper award. He has led Italian INdAM-GNCS research projects on GPU applications (2011) and Logic Programming in cancer genomics (2016). Recent publications focus on explainable AI frameworks, bioinformatics applications, and sustainable logistics solutions. His work integrates Answer Set Programming with biomedical challenges like protein structure analysis and cancer genome evolution. He chairs the International Conference on Logic Programming (ICLP 2018) and has organized multiple international workshops on constraint programming. 2007 GULP Award ICLP 2010 Best Paper PI for INdAM-GNCS projects (2011, 2016) Program Committee member for international conferences