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.

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.

Gabriella CasalinoView profile

Assistant Professor

• Computational Intelligence
• eHealth
• Data Stream Mining
• +5 more

Gabriella Casalino is an Assistant Professor at the University of Bari Aldo Moro, Department of Computer Science, and a key researcher at CILAB - Computational Intelligence Lab. Her work focuses on Computational Intelligence methods for interpretable data analysis, particularly in eHealth, Data Stream Mining, and eXplainable Artificial Intelligence (XAI) within medical and educational domains. She has contributed to innovative approaches in smartphone-based health monitoring, fuzzy logic applications, and remote vital sign detection via photoplethysmography. Education : Ph.D. in Computer Science, with advanced training at institutions like Universitat de Girona and Université de Mons. Research Trends : Recent publications highlight applications of evolving granular computing, neuro-fuzzy systems, and explainable AI in hypertension prediction, bipolar disorder monitoring, and educational data analysis. Key subfields include remote health monitoring, medical data streams, and hybrid AI models. Grants : Research funded by AIRC (Italian Cancer Research Foundation), focusing on computational methods for healthcare challenges. Labs & Collaborations : Active in CILAB, collaborating on projects involving mHealth solutions, cardiovascular risk assessment, and intelligent educational systems.

• 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.

• Digital Circuits and Systems
• Energy Efficiency
• Smart Cities
• +13 more

Enrico Macii is a Full Professor at the Politecnico di Torino, affiliated with the Interuniversity Department of Regional and Urban Studies and Planning (DIST) and the Department of Control and Computer Engineering (DAUIN). He leads the Electronic Design Automation (EDA) research group and holds key roles as Scientific Advisor for the Politecnico-STMicroelectronics partnership and Scientific Contact for the European Chips Joint Undertaking. Research Interests: His work spans digital circuits and systems, energy efficiency, smart cities, Industry 4.0, and smart manufacturing. He focuses on embedded and cyber-physical systems, low-power design, neuromorphic computing, AIoT, and sustainable urban development. Recent Publications: His recent research demonstrates strong trends in edge AI, neuromorphic computing, and smart energy systems. Articles highlight innovations in low-power hardware acceleration, federated learning, physics-informed AI, and digital twin applications for urban and industrial systems. There is a clear emphasis on deploying AI efficiently on constrained devices and integrating physical models with machine learning. J. William Fullbright Fellowship (1993) Best paper award IEEE European Design Automation Conference (1996) Best paper award ACM/IEEE Great Lakes Symposium on VLSI (2008) DAC Service Award (2014) IEEE Fellow (2006) DATE Fellow (2014) Advising and Grants: He has supervised over 25 PhD students in computer engineering, AI, and urban systems. His research is funded by major EU programs (Horizon 2020, PNRR, KDT JU), national (PRIN, FAR), and regional grants, as well as industrial contracts with STMicroelectronics, Michelin, and Cefriel. He leads numerous high-impact projects in smart manufacturing, energy efficiency, and digital twins. Labs and Teams: He is a core member of the EDA Group, an interdepartmental research team at Politecnico di Torino focusing on VLSI-CAD, bioinformatics, smart cities, and Industry 4.0. He also contributes to IAM@PoliTo (Integrated Additive Manufacturing) and leads multiple EU and national research consortia.

• Mathematical oncology
• Cancer evolution
• Multiscale modeling
• +4 more

Dr. Kathleen Curtius is an Assistant Professor in the Department of Medicine at the University of California San Diego (UCSD), affiliated with the Division of Biomedical Informatics and the UCSD Moores Cancer Center. She leads the Quantitative Cancer Control laboratory, focusing on early cancer detection and prevention through mathematical modeling and multi-scale data analysis. Previously, she held a Medical Research Council Rutherford Fellowship at Barts Cancer Institute (London) and completed postdoctoral training in Prof. Trevor Graham's lab. Her research integrates applied mathematics, computational biology, and clinical oncology to address cancer evolution and screening strategies. Education: PhD in Applied Mathematics, University of Washington (2015) MS in Applied Mathematics, University of Washington (2011) BS in Mathematics, UCLA (2010) Research Interests: Mathematical oncology Cancer evolution modeling Epigenetic drift analysis Screening optimization Multiscale tumor dynamics Her work bridges disciplines to develop predictive tools for early cancer detection and personalized surveillance strategies. Publications: Recent work includes studies on metagenomic data bias (Nature Communications 2025), AI-driven cancer biomarker discovery (JCI Insight 2022), and computational models of Barrett's esophagus progression (Gut 2020). Her research emphasizes translational applications in clinical oncology and public health. Awards: Recognized with the 2024 Leah Edelstein-Keshet Prize, 2022 AGA Research Scholar Award, and 2018 MRC Rutherford Fellowship. Her work has been cited over 1,000 times across key oncology journals. Funding: Principal Investigator on NIH grants (R01CA270235, I01BX005958) supporting projects on Barrett's esophagus modeling and colitis-associated colorectal cancer surveillance optimization. Collaborates with clinicians, geneticists, and computational biologists to advance cancer control efforts. Labs/Teams: Leads the Quantitative Cancer Control Lab at UCSD, part of the Bioinformatics and Systems Biology division. Active in interdisciplinary initiatives like the Moores Cancer Center's Cancer Control Program.

Nicolò Cesa-BianchiView profile

Professor

• Machine Learning
• Online Learning
• Multi-Armed Bandits
• +4 more

Nicolò Cesa-Bianchi is a Professor of Computer Science at the University of Milan, Department of Computer Science (Dipartimento di Informatica), and affiliated with the DEIB Department at Politecnico di Milano. His research focuses on foundational aspects of machine learning, particularly online learning, multi-armed bandits, reinforcement learning, and graph analytics. He is an ELLIS Fellow and a corresponding member of the Accademia Nazionale dei Lincei. Research interests include the design and analysis of algorithms for prediction, clustering, and online decision-making, with applications to digital markets, social networks, and bioinformatics. Notable contributions span cooperative online learning, multitask learning, and bandit algorithms. He co-authored the influential book Prediction, Learning, and Games (2006). Professional roles include Board member of ELLIS, co-director of the Milan ELLIS unit, and involvement in EU initiatives like ELSA (Secure & Safe AI) and ELIAS (AI for Sustainability). He teaches graduate courses on statistical methods, machine learning, and reinforcement learning, with a focus on theoretical foundations. Key awards: ELLIS Fellowship (2020), Corresponding Member of the Accademia Nazionale dei Lincei (Italian National Academy of Sciences). His work bridges theory and practice, addressing challenges in adaptive systems, market design, and algorithmic fairness. Current projects explore distributed learning, regret minimization in adversarial environments, and interpretable models.

• 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.

Andrea SottorivaView profile

Professor

• Cancer Evolution
• Computational Biology
• Genomics
• +4 more

Andrea Sottoriva is the Head of the Computational Biology Research Centre at Human Technopole , Milan, Italy, and holds the title of Professor of Cancer Genomics and Evolution . His work bridges computational biology, evolutionary theory, and clinical oncology to predict cancer progression and design adaptive treatment strategies. University of Bologna – BSc in Computer Science (2006) University of Amsterdam – MSc in Computational Sciences (2008) University of Cambridge – PhD in Computational Biology (2012) Dr. Sottoriva's research focuses on cancer evolution , applying machine learning and population genetics to decode tumor heterogeneity through multi-omics data. His lab integrates patient-derived organoids and spatial genomics to understand how genetic and epigenetic factors drive cancer progression. The 15 most recent publications demonstrate a consistent emphasis on subclonal dynamics (7/15), computational methods (6/15), and evolutionary modeling (5/15). Key themes include adaptive mutability in colorectal cancer , immune editing in post-transplantation relapses, and deep learning applications for tumor heterogeneity. Scientific recognition includes: Cancer Research UK Future Leaders in Cancer Research Prize (2016) His lab currently trains PhD students and postdoctoral researchers in computational oncology, maintaining a living biobank of patient-derived models while pioneering AI-mechanistic hybrid models for clinical translation.

• 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.

• Database Systems
• Healthcare Information Systems
• Artificial Intelligence
• +5 more

Carlo Combi is a Full Professor at the Department of Computer Science, University of Verona. He serves as Department Director and holds roles in various academic committees, including the Faculty Board of PhD in Computer Science and the Computer Science Teaching Committee. His research focuses on databases, healthcare information systems, temporal data mining, and business process management. He leads the STARS laboratory and has contributed to projects like PREPARE (prostate cancer evaluation) and EDIPO (neuroimaging genetics). His teaching spans courses on biomedical databases, software engineering, and Python programming. Combi's work integrates ethical considerations into process models and addresses challenges in healthcare workflows, temporal analytics, and AI applications in medicine. Education: Not explicitly listed in provided texts. Research Projects: Includes 30+ projects since 2008, emphasizing healthcare informatics, temporal systems, and data warehousing. Labs/Teams: STARS (Semistructured Temporal clinical Geographical Systems) and collaborations with CBMC (Computational Biomedicine Center). Grants: Multiple national/international grants, including funding for temporal data mining and healthcare process modeling. Awards: None explicitly mentioned, though his leadership roles and extensive publications highlight academic recognition. His research bridges clinical processes, temporal databases, and AI, with applications in acute kidney injury prediction, BPMN extensions for healthcare, and explainable AI in medical systems. Combi actively contributes to open-source solutions for clinical data analysis and integrates ethical frameworks into process management.

• Information Visualization
• Visual Analytics
• Human-Computer Interaction
• +8 more

Paolo Buono is Associate Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. He holds a PhD in Computer Science with specialization in Visual Data Analysis. His research focuses on Information Visualization, Visual Analytics, Human-Computer Interaction, and Mobile Applications. Co-founder and CEO of LARE (2010), a university spinoff providing real-time surgical support through audio-video telestration Member (since 2002) and computer science coordinator at METEA Research Center for environmental protection Visiting scientist at AVIZ (France), University of Maryland (USA), and Fraunhofer IPSI (Germany) His work spans multiple application domains including: Cultural Heritage through interactive exploration systems Healthcare with smart therapeutic devices Environmental Monitoring via CET system IoT-based Smart Interactive Experiences He has contributed to: Dynamic hypergraph visualization techniques End-User Development frameworks (EUDroid) Usability evaluation methodologies Mobile health applications As project leader, he has coordinated: EU-funded VisMaster Coordination Action (2008-2010) Italian Ministry-funded LOGIN project (2014-2015) Apulia Region environmental projects His professional engagements include: Co-chair roles at INTERACT, AVI, IS-EUD conferences Program committee participation in VIS series and HCI conferences Member of ACM, IEEE, and SIGCHI Italy

Fosca GiannottiView profile

Professor

• Mobility Data Mining
• Social Network Analysis
• Privacy-Preserving Data Mining
• +6 more

Fosca Giannotti is a Full Professor at Scuola Normale Superiore in Pisa, Italy, and leads the Pisa KDD Lab - Knowledge Discovery and Data Mining Laboratory, a joint research initiative of the University of Pisa and ISTI-CNR. Founded in 1994, the Pisa KDD Lab is one of the earliest research labs focused on data mining. Giannotti is a pioneering scientist in mobility data mining, social network analysis, and privacy-preserving data mining. Her educational background includes a Master Degree in Computer Science from the University of Pisa (1982) with 110/100 cum laude. She has held numerous visiting positions including at MCC in Austin, CWI Amsterdam, UCLA, and the Barabasi Lab at Northeastern University. Giannotti's research focuses on social mining from big data, encompassing smart cities, human dynamics, social and economic networks, ethics and trust, and diffusion of innovations. She has authored more than 300 papers and coordinated tens of European projects and industrial collaborations. Her current work increasingly centers on Explainable AI (XAI), as evidenced by her prestigious ERC Advanced Grant for the XAI project focused on "Science and technology for the explanation of AI decision making." Her recent publications reveal a strong emphasis on trustworthy AI, with research spanning privacy-preserving techniques, fairness in machine learning, human-AI collaboration frameworks, and medical applications of explainable AI. The breadth of her work demonstrates how data mining principles are being applied across diverse domains from social sciences to healthcare. ERC Advanced Grant for XAI project Premio Internazionale Tecnovisionarie 2021 Intelligenza Artificiale Giannotti has coordinated numerous significant projects including SoBigData (the European research infrastructure on Big Data Analytics and Social Mining), XAI, TAILOR (Foundations of Trustworthy AI), HumanE-AI-Net, and AI4EU. As former coordinator of SoBigData, she led an ecosystem of ten cutting-edge European research centers providing an open platform for interdisciplinary data science. She leads the Pisa KDD Lab, which serves as a hub for research on knowledge discovery and data mining. The lab has been instrumental in developing techniques for mobility data analysis, social network mining, and privacy-preserving data analytics, with applications ranging from smart cities to pandemic response.