جست‌وجوی استاد و پژوهشگر

• Livestock Systems
• Animal Breeding
• Genetics
• +۳ مورد دیگر

Matteo Bolner is a Post-Doctoral Research Fellow at the University of Bologna's Department of Agricultural and Food Sciences, specializing in livestock genomics and metabolomics. He holds a PhD in Agricultural and Food Sciences (defended March 2025) and an International Master in Bioinformatics from the University of Bologna. His research integrates genomic and metabolomic data to improve livestock sustainability, particularly in pig production systems. Key focuses include identifying metabolic pathways influencing production traits, analyzing pig viromes for disease outbreaks, and leveraging big data for One Health applications. His educational background includes a Biological Sciences degree (2018) and a bioinformatics master's (2021). He interned at CINECA's SCAI department, focusing on HPC software containerization. Current affiliations include membership in the Animal and Food Genomics group, where he explores genomic solutions for breed conservation and sustainable production. Research trends in his articles emphasize multi-omics integration to understand pig metabolism, stress responses, and breed-specific adaptations. He also applies genomics to authenticate food products and enhance conservation strategies for endangered livestock breeds like the Mora Romagnola pig. His work bridges animal science, computational biology, and agricultural sustainability. Notable contributions include developing genomic tools for honey bee population analysis and creating a catalog of mitochondrial insertions in pig genomes. Future directions involve advancing metabolomics-based precision livestock farming and applying big data analytics to livestock One Health challenges.

Luca Sebastianiمشاهده پروفایل

استاد

• Plant Physiology
• Molecular Plant Stress Responses
• Nutraceuticals in Plants
• +۳ مورد دیگر

Luca Sebastiani is a Full Professor in Horticultural Sciences (AGR/03) at Scuola Superiore Sant'Anna in Pisa, Italy, since 2014. He currently coordinates the PhD Course in AgroBioSciences and has previously served as Director of the Institute of Life Sciences (2016-2021). His academic career includes roles as Associate Professor (2002-2014) and Assistant Professor (1998-2002) at the same institution. PhD in Plant Biology from Scuola Superiore Sant'Anna (1996) MSc in Agricultural Sciences from University of Pisa (cum laude, 1991) Postdoctoral research in agricultural biotechnology (1996-1998) Research Interests: Focus on plant-environment interactions, particularly abiotic and biotic stress responses in crops. Key areas include: Physiological and molecular responses to climate change stressors Nutraceutical enhancement of food crops Plant germplasm conservation using molecular markers Agriculture 4.0 integrating AI, IoT, and robotics Phytoremediation using poplar and Brassica species Scientific Contributions: His work bridges molecular mechanisms (aquaporin function, heavy metal transport) with ecosystem-level applications (precision irrigation, contaminant phytoremediation). Recent publications emphasize genome sequencing, stress tolerance modeling, and nutraceutical food development. ISHS Medal for SapFlow Workshop organization (2011) Giovanni Spitali Foundation Award for PhD dissertation (1998) Collaborations: Extensive international collaborations with institutions like Beijing Forestry University, Comenius University, and Purdue University. Currently supervises projects in plant phenotyping, omics technologies, and sustainable crop management.

Giuseppe Santucciمشاهده پروفایل

دانشیار

• Visual Analytics
• Information Visualization
• Human-Computer Interaction
• +۳ مورد دیگر

Giuseppe Santucci is an Associate Professor at the Department of Computer, Control and Management Engineering Antonio Ruberti at Sapienza University of Rome. He teaches courses on Fundamentals of Computer Science, Software Engineering, and Visual Analytics. His office is located in Room B218 at Via Ariosto 25, Rome, and his contact email is santucci@diag.uniroma1.it. Dr. Santucci's research focuses on Visual Analytics, Information Visualization, Human-Computer Interaction, and Information Retrieval. His work spans theoretical aspects of visual query languages for semantic models to practical applications in visual analytics for cybersecurity, cryptocurrencies, and deep learning explainability. He has published over 130 articles in international journals and conferences, demonstrating his significant contributions to these fields. His recent publications show a strong trend toward applying visual analytics to increasingly complex domains including cybersecurity, cryptocurrencies, and explainable AI. The work demonstrates an evolution from theoretical foundations of visual query systems to practical applications that help users understand complex data and systems. His research bridges the gap between theoretical computer science and practical user-centered solutions. Dr. Santucci has received notable recognition including: IEEE VizSec 2018 Best Paper Award Human-Computer Interaction Cybersecurity Awards 2018 He actively mentors students through thesis projects focused on information visualization and visual analytics. His PROMISE project provides a framework for students to engage in cutting-edge research in information retrieval and visual analytics. He has supervised work on topics including visual evaluation techniques, visual mappings optimization, and user studies for Infovis systems. Dr. Santucci leads the A.WA.RE (Advanced Visualization & Visual Analytics REsearch) group at Sapienza University. This group conducts research on visual analytics tools for information retrieval evaluation, cybersecurity analysis, and deep learning explainability. Their work includes developing frameworks like CryptoComparator for cryptocurrency analysis and BUCEPHALUS for cybersecurity platform analysis.

Damiano Piovesanمشاهده پروفایل

دانشیار

• Protein function prediction
• Intrinsically disordered proteins (IDPs)
• Machine learning in bioinformatics
• +۵ مورد دیگر

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 Casalinoمشاهده پروفایل

استادیار

• Computational Intelligence
• eHealth
• Data Stream Mining
• +۵ مورد دیگر

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.

Giacomo Parigiمشاهده پروفایل

دانشیار

• Nuclear Magnetic Resonance (NMR)
• Paramagnetism-Based Effects
• MRI Contrast Agents
• +۴ مورد دیگر

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.

• Mathematical oncology
• Cancer evolution
• Multiscale modeling
• +۴ مورد دیگر

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.

• Algorithms
• Data Structures
• Pattern Matching
• +۳ مورد دیگر

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
• +۵ مورد دیگر

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
• +۳ مورد دیگر

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.

Paola Paciمشاهده پروفایل

دانشیار

• Computational Biology
• Bioinformatics
• Network Medicine
• +۳ مورد دیگر

Paola Paci is an Associate Professor at the Department of Computer, Control, and Management Engineering "Antonio Ruberti" of Sapienza University of Rome and an Affiliate Professor at the Department of Laboratory Medicine, Cardio-Metabolic Unit of Karolinska Institutet, Sweden. She has held visiting positions at Harvard Medical School and research roles at CNR's Institute for Systems Analysis and Computer Science. Her work bridges computational biology and network medicine. 2010: Second Level Master in Bioinformatics (Sapienza University of Rome) 2003: PhD in Physics (University of Pavia) 1999: MSc in Physics (Sapienza University of Rome) Paci develops algorithms for biomarker discovery and drug repurposing in network medicine. Her tools include SWIM for glioblastoma biomarkers, SPINNAKER for ceRNA interactions, SAveRUNNER for drug repurposing, and MIENTURNET for miRNA-target analysis. Recent work focuses on single-cell multi-omics, adverse drug effects, and pathogen spillover mechanisms. Her research spans 2025-2024 publications covering Alzheimer’s disease drug prioritization, COPD pathway analysis, obesity therapeutics avoiding hepatic steatosis, and neuroblastoma immunotherapy resistance. These works integrate network medicine with pharmacology, genomics, and systems biology. 2018: BITS Best Poster Award 2017: IEEE TCCLS Best Poster (700 USD) 2016: IEEE TCCLS Best Poster (500 USD) 2014: SYSBIO Award (10,000 EUR) 2010: Master Award (1,500 EUR) She teaches bioinformatics at Sapienza University and Campus Biomedico University, organized key IEEE workshops, directs training courses, and serves on editorial boards (Scientific Reports, Biotechnology Reports). Her affiliations include the Network Medicine Institute and GNB bioengineering society.

• Genomics
• Transcriptomics
• Network Biology
• +۷ مورد دیگر

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.

Francesco Iorioمشاهده پروفایل

پژوهشگر

• Computational Biology
• Pharmacogenomics
• Cancer Biology
• +۳ مورد دیگر

Francesco Iorio is a Research Group Leader in Computational Biology at Human Technopole (Milan, Italy), leading research in computational cancer pharmacogenomics and therapeutic target discovery. He holds a PhD from the University of Salerno and TIGEM (Naples), focusing on computational methods for drug discovery. Previously, he was a Senior Bioinformatician at EBI, leading CRISPR-Cas9 screen analyses across cancer cell lines to identify synthetic lethalities and therapeutic targets. He led the Cancer Dependency Map Analytics team at Wellcome Sanger Institute, developing algorithms for integrating large-scale cancer datasets. Since 2019, he serves as a Scientific Advisor to the Cancer Research Horizon–AstraZeneca Functional Genomics Centre. His research integrates computational methods with experimental data to uncover cancer dependencies, drug mechanisms, and biomarkers. Key contributions include tools like CRISPRcleanR for bias correction in CRISPR screens, and CELLector for selecting cancer cell line models. His work spans pharmacogenomics, functional genomics, and CRISPR technologies, with a focus on translating findings into clinical applications. Publications highlight advancements in CRISPR screen analysis, cancer dependency mapping, and drug repositioning. Current projects aim to advance precision medicine through computational frameworks for understanding tumor heterogeneity and therapeutic vulnerabilities.

Diego di Bernardoمشاهده پروفایل

استاد

• Genomic Medicine
• Systems and Synthetic Biology
• Biomedical Engineering
• +۴ مورد دیگر

Diego di Bernardo is a Full Professor of Biomedical Engineering at the University of Naples Federico II and Principal Investigator at TIGEM, serving as Coordinator of the Genomic Medicine Program and Head of the Bioinformatics Core. His work bridges engineering and biological sciences to advance disease understanding and therapeutic development. His educational background includes a Laurea cum laude in Electronic Engineering from the University of Naples Federico II (1997) and a PhD in Medical Physics from the University of Newcastle School of Medicine (2001), funded by a European Commission Marie Curie Fellowship. Postdoctoral training followed at the Wellcome Trust Sanger Center and Boston University. Di Bernardo's research integrates Biomedical Engineering, Control Engineering, and Molecular Biology to pioneer Biomolecular Control. His lab develops microfluidics platforms for real-time cellular analysis and computational approaches for gene network reverse engineering and drug repositioning. Current work focuses on single-cell transcriptomics to combat drug resistance in cancer and engineer stress-response pathways for bioproduction optimization. His publication record demonstrates consistent innovation in computational biology, with recent work emphasizing pan-cancer transcriptomics, microfluidics-based cell control, and bioinformatics tool development for pathway analysis. The research trajectory shows increasing integration of engineering principles with genomic medicine. Scientific recognition includes: Marie Curie Fellowship He advises PhD students including Clarissa Poles and Virginia Fusco, leading a multidisciplinary team of postdocs and bioinformaticians. Major funding sources include Telethon, AIRC, Italian Ministries, HFSP, and EU programs such as Re-MEND (2023-2027) for mental health resilience and iPC (2019-2023) for pediatric cures. His TIGEM laboratory operates cutting-edge microfluidics and bioinformatics facilities to engineer living systems, with current projects targeting super-producer cell lines for biological drugs and viral vectors while reducing production costs through stress-response re-engineering.

Salvatore Ramponeمشاهده پروفایل

دانشیار

• Machine Learning
• Cybersecurity
• Internet of Things (IoT)
• +۷ مورد دیگر

Salvatore Rampone is an Associate Professor at the Department of Law, Economics, Management and Quantitative Methods (DEMM) of Università degli Studi del Sannio (Unisannio). His research focuses on Machine Learning , Internet of Things (IoT) , and Cybersecurity , with applications in intrusion detection, bioinformatics, and financial systems. Research Interests : Privacy-preserving Machine Learning Hybrid Cloud-IoT Architectures Genetic Programming Neural Network Applications Data Processing in Distributed Environments Recent Publication Trends : His work spans federated learning for IoT security ( 2025 ), DNA splicing site prediction ( 2020 ), meningitis classification ( 2019 ), RFID-based traceability systems ( 2015 ), and geological data analysis using neural networks ( 1995-1996 ). Key technologies include Apache Spark , MLlib , and Wavelet Transform . Teaching Activities : He teaches courses like Applied Machine Learning and Principles of Computer Sciences across multiple degree programs including Statistical and Actuarial Sciences and Economics and Management .