جست‌وجوی استاد و پژوهشگر

Francesco Guerraمشاهده پروفایل

استاد

• Scalable Data Management
• Data Mining
• Big Data Analysis
• +۵ مورد دیگر

Francesco Guerra is a Full Professor at the Department of Engineering 'Enzo Ferrari' at the University of Modena and Reggio Emilia. He teaches Software Engineering and Big Data Analysis. His research focuses on scalable data management, machine learning explainability, entity resolution, data mining, text analytics, semantic web, and keyword-based search on structured data. Academic Positions: Rector’s delegate for ICT (since Feb 2024) President of Computer Engineering Degree Councils (since Nov 2022) Curriculum Coordinator for Computer Engineering Doctorate (2021-2023) Visiting Professor at University of Rijeka (2019) Education: PhD in Information Engineering (2004) Master Degree in Information Engineering (2000) Classical Diploma (1992) from Liceo Classico 'L.A. Muratori' Research Interests: Develops explainable machine learning techniques for entity resolution and anomaly detection. Specializes in interpretable time series clustering (Time2Feat), BERT-based entity matching analysis, and pushing ML predictions into DBMS frameworks. Active in semantic keyword search and big data integration projects like KEYSTONE (COST Action 2013-2017) and Re-search Alps (2017-2019). Projects: KEYSTONE COST Action: Semantic keyword search on structured data Re-search Alps: Big data integration for Alpine research labs AutoML4Clust: Automated machine learning for clustering FAIRnessEval: Framework for algorithmic fairness evaluation Awards: No awards explicitly listed in the provided texts. However, his work has been funded through EU and national projects.

Francesco Grossettiمشاهده پروفایل

استادیار

• Financial Disclosure Analysis
• Natural Language Processing
• Blockchain Applications
• +۳ مورد دیگر

Francesco Grossetti is Assistant Professor of Accounting Analytics and Data Science at Bocconi University, with a Ph.D. in Applied and Computational Statistics from Politecnico di Milano. His research develops computational approaches for analyzing corporate disclosures using NLP and computer vision techniques, with additional focus on blockchain implications for accounting practices. His interdisciplinary work bridges financial mathematics, stochastic modeling, and accounting innovation. Research examines volatility modeling calibration puzzles, textual characteristics in financial reports, blockchain accounting applications, and corporate governance language patterns. Methodologies include machine learning, entropy-based calibration, and sports analytics for tournament fairness. Recent projects include EXECML (analyzing executive compensation disclosures via machine learning) and multi-modal corporate communication studies. He serves on editorial boards for Finance & Stochastics, SIAM Journal on Financial Mathematics, and Quantitative Finance, reflecting his cross-disciplinary expertise.

Luca Cosmoمشاهده پروفایل

دانشیار

• Computer Science
• Machine Learning
• Data Analytics
• +۴ مورد دیگر

Luca Cosmo is an Associate Professor at the Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice. His research focuses on machine learning, graph theory, and their applications in pattern recognition, computer vision, and 3D shape analysis. He has contributed to advancements in graph neural networks, diffusion models for music generation, and spectral methods for shape analysis. Teaching Activities: Machine Learning (Master's and PhD programs) Computer Programming and Data Management Lab of Information Systems and Analytics Data Analytics for Business and Society Research Interests: Graph Neural Networks and their interpretability Music generation via diffusion models 3D shape analysis using spectral and quantum-inspired methods Applications in healthcare and molecular design Recent Work Trends: Recent publications emphasize graph-based learning, including novel architectures like GraFix and LESI-GNN, and interdisciplinary applications in music decoding from EEG signals and molecular design. His work bridges theoretical foundations (e.g., spectral geometry) with practical tools for analyzing complex data structures. Labs/Teams: Active in the D.A.I.S. department's research groups focused on informatics and statistical methodologies.

• Software Engineering
• Reverse Engineering
• Web Applications
• +۳ مورد دیگر

Giuseppe Antonio Di Lucca is an Associate Professor at the University of Sannio, Department of Engineering. His research focuses on software engineering, reverse engineering, web applications, and aspect-oriented programming. He teaches courses including Databases and Elements of Computer Science for engineering students. Research Interests: His work spans model-driven engineering, design pattern detection, aspect mining, software maintenance, and web application testing. Key areas include: Reverse engineering methods for aspect mining in object-oriented systems Model-driven approaches for rapid prototyping of web applications Queue theory applications in software maintenance staffing Aspect-oriented frameworks for measurement systems Publication Trends: Di Lucca's 136+ publications emphasize software engineering innovations, with recent works focusing on model-driven design pattern detection (2013), aspect-oriented fault detection (2010), and web application testing techniques (2006). His research demonstrates consistent contributions to improving software quality through reverse engineering and aspect-oriented paradigms.

Silvia Liberata Ulloمشاهده پروفایل

پژوهشگر

• Remote Sensing
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Silvia Liberata Ullo is a Researcher at the Department of Engineering (DING) of Università del Sannio . Her work spans Remote Sensing , Machine Learning , and Telecommunications , with a focus on Environmental Monitoring and Public Health Applications . Key research areas: Deep Learning for Land Cover Mapping Speckle Noise Reduction in SAR Data Machine Learning for Pandemic Response Mixed Reality in Military Systems Her recent publications highlight advancements in 2025 : Deep Learning for Land Cover Classification using Sentinel-1 2024 : Ensemble ML models for Dengue Outbreak Forecasting in Brazil/Peru 2023 : SAR Image Enhancement via Residual Neural Networks She collaborates with international institutions on climate-health services and geospatial AI projects.

Giulio Ermanno PIBIRIمشاهده پروفایل

دانشیار

• Computer Science
• Bioinformatics
• Algorithms
• +۵ مورد دیگر

Giulio Ermanno PIBIRI is an Associate Professor at the Department of Environmental Sciences, Computer Science and Statistics of Ca' Foscari University of Venice. His primary research focuses on algorithms, data structures, and their applications in bioinformatics, genomics, and information retrieval. He specializes in developing efficient methods for handling large-scale genomic data and optimizing storage and querying of massive datasets. His work includes advancements in minimal perfect hashing, k-mer indexing, and compression techniques for de Bruijn graphs. Key research areas include: Algorithm design for bioinformatics (e.g., sequence analysis, genome assembly) Data compression and indexing (inverted indexes, variable-byte codes) Efficient storage and retrieval of genomic and textual data Secure machine learning (verifiable tree ensembles) Recent publications highlight contributions to: Meta-colored de Bruijn graphs for genomic data representation Efficient k-mer hashing and sampling algorithms Secure and robust machine learning models Teaching includes courses on computer programming and foundational computer science topics for engineering students.

Andrea Facchinettiمشاهده پروفایل

استادیار

• Bioengineering
• Continuous Glucose Monitoring
• Diabetes Technology
• +۳ مورد دیگر

Andrea Facchinetti is an Assistant Professor in Bioengineering at the Department of Information Engineering, University of Padova, Italy. His research focuses on developing advanced algorithms for continuous glucose monitoring systems and diabetes management technologies, with significant contributions to both academic research and commercial applications through technology transfer to Dexcom Inc. Dr. Facchinetti received his Laurea Quinquennale (summa cum laude) in Information Engineering from the University of Padova in 2005, followed by a Ph.D. in Bioengineering from the same institution in 2009. After serving as a Postdoctoral Fellow from 2009 to 2014, he was appointed Assistant Professor in 2014. His primary research interests center around processing data from continuous glucose monitoring (CGM) sensors, with specific focus on: Algorithms for signal denoising using Kalman filters and Bayesian smoothing techniques Strategies to reduce or eliminate calibrations in CGM sensors Real-time detection of hypo/hyperglycemic events using neural networks and statistical approaches Modeling of CGM data error and transient faults Development of type 1 diabetes simulators for testing decision-making systems Retrospective retrofitting methods to improve glucose concentration profiles Analysis of Dr. Facchinetti's recent publications (2023-2025) reveals an expanding research scope beyond traditional diabetes management. His work now encompasses post-bariatric hypoglycemia, neonatal glucose monitoring, integration of AI in clinical decision support systems, and applications of continuous glucose monitoring in novel populations including multiple sclerosis and amyotrophic lateral sclerosis patients. His research demonstrates a clear trajectory from foundational algorithm development toward practical clinical implementation and user-centered design of diabetes technologies. Dr. Facchinetti has been involved in significant technology transfer activities, with multiple patents optioned by and transferred to Dexcom Inc., a leading continuous glucose monitoring company. His work has contributed to commercial CGM systems including the Dexcom G4. He has co-authored over 70 international conference papers and numerous journal publications in high-impact diabetes and biomedical engineering journals. His research group maintains active collaborations with clinical partners and industry stakeholders to translate algorithmic innovations into practical diabetes management solutions. Current projects include developing decision support systems for pediatric type 1 diabetes, addressing post-bariatric hypoglycemia through forecasting algorithms, and creating modular platforms for real-world data acquisition in clinical trials.

• Colorectal Surgery
• Inflammatory Bowel Disease
• Minimally Invasive Surgery
• +۳ مورد دیگر

Andrea Vignali serves as Associate Professor of General Surgery at Vita-Salute San Raffaele University and Head of the Inflammatory Bowel Diseases Unit at Milan's San Raffaele Hospital since 2014. With dual clinical-academic leadership, he directs the Onco-Team for colorectal cancer guidelines and coordinates minimally invasive surgery initiatives for the Italian Society of Colorectal Surgery. Educational background includes: MD, Medicine and Surgery (110/110 cum laude, 1989) Specialization in Emergency Surgery (70/70 cum laude, 1994) CNR-NATO fellowship (1995-1997) Research fellowship at Cleveland Clinic Colorectal Surgery Department (1994-1996) His research pioneers perioperative protocols through 68 indexed publications and 11 book chapters. Key focus areas include: Enhanced Recovery After Surgery (ERAS) implementation Minimally invasive approaches for rectal tumors International consensus guidelines via the Rectal Cancer Study Group Multicenter trials on Crohn's disease surgical management Recent work (2023-2024) establishes global standards for anastomotic techniques, risk prediction models, and elderly cancer care through Lancet and British Journal of Surgery publications. Scientific recognition includes: Karl Storz Award for Best Oral Presentation (2009) Two ASCRS Best Paper Awards (1998, 2002) Three competitive research grants (1992-1995) Southern California Society Best Poster Award (2009) As active reviewer for major surgical journals and editorial board member of Annals of Laparoscopic and Endoscopic Surgery, he mentors through the Italian Society of Colorectal Surgery's School of Surgery. Current projects focus on the SPICY and HAND2END international trials for Crohn's disease recurrence prevention. He leads the San Raffaele Hospital's Inflammatory Bowel Diseases Unit and collaborates with Case Western University's International Rectal Cancer Study Group, driving evidence-based protocols across 64 countries via the EAGLE trial infrastructure.

• Automata theory
• Formal language theory
• Combinatorics on words
• +۲ مورد دیگر

Giuseppa Castiglione is a Researcher (INFO-01/A) in the Department of Mathematics and Computer Science at the University of Palermo's School of Basic and Applied Sciences. She maintains regular office hours on Tuesdays and Thursdays from 2:00 PM to 3:00 PM in Room 209, second floor, and teaches Theoretical Computer Science for both Computer Science and Mathematics degree programs. Her primary research interests span several interconnected areas within theoretical computer science and discrete mathematics. These include automata theory, formal language theory, combinatorics on words, enumerative combinatorics, and discrete two-dimensional structures. Her work demonstrates a consistent focus on the mathematical foundations of computer science, particularly in the analysis of string algorithms, word combinatorics, and discrete geometric structures. Analysis of her publication record from 2017-2025 reveals a strong concentration on combinatorial aspects of words and automata, with recent work expanding into isometric words, Fibonacci cubes, and Wheeler automata. Her research shows a progression from foundational work on polyominoes and automata minimization toward more complex combinatorial structures and their applications in string processing. Castiglione has participated in numerous significant research projects including the Prin 2010-11 project on mathematical aspects of automata and formal languages, the PO FESR 2007/2013 Neverlost project, PON Research and Competitiveness 2007/2013 SMART HEALTH initiative, and GNCS 2016 on combinatorial methods for handling large data sets. These projects demonstrate her ability to secure competitive research funding across multiple domains. She collaborates with researchers including Marinella Sciortino, Gabriele Fici, Chiara Epifanio, Sabrina Mantaci, and Antonio Restivio, forming a productive research network focused on theoretical computer science and combinatorics. Her extensive publication record in high-quality venues demonstrates her sustained contribution to the field over more than two decades.

• Human Activity Recognition
• Wireless Sensor Networks
• Internet of Things (IoT)
• +۱ مورد دیگر

Emanuele Lattanzi is an Associate Professor in the Department of Pure and Applied Sciences (DiSPeA) at the University of Urbino Carlo Bo, specializing in Information Processing Systems (IINF-05/A). His research focuses on human activity recognition, wireless sensor networks, IoT systems, and artificial intelligence applications. He obtained his Laurea degree in 2001 and PhD in 2003, followed by a visiting scholar position at Pennsylvania State University's Department of Computer Science and Engineering. His educational background forms the foundation for his current research in sensor-based AI systems. Lattanzi's research interests center on developing energy-efficient solutions for human activity recognition using wearable sensors, with particular emphasis on optimizing machine learning models for resource-constrained IoT devices. His work bridges theoretical AI advancements with practical implementation challenges in real-world sensor deployments. Analysis of his 2025 publications reveals a strong thematic focus on human activity recognition, with recurring exploration of energy-accuracy tradeoffs, novel neural network architectures (including transformers and Siamese networks), and self-supervised learning techniques specifically adapted for inertial sensor data. His research demonstrates consistent collaboration with multiple co-authors across international conferences. As an educator, Lattanzi teaches core computer science courses including Operating Systems (12 credits) and IoT Programming (6 credits) across multiple Informatics degree programs. His teaching spans from 2021/2022 through the current 2025/2026 academic year, demonstrating sustained contribution to the department's curriculum in both foundational systems programming and emerging IoT technologies.

Giovanni Trappoliniمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Geometric Deep Learning
• +۹ مورد دیگر

Giovanni Trappolini is an Assistant Professor in the Department of Computer, Control, and Management Engineering at Sapienza University of Rome, where he conducts research in the RSTLess Lab under the supervision of Prof. Fabrizio Silvestri. He earned his PhD in Machine Learning from Sapienza in 2022, focusing on Geometric Deep Learning under Prof. Emanuele Rodolà. Trappolini has collaborated with leading institutions such as Stanford, Technion, Meta, Amazon, TII, and the University of Pisa, contributing to cutting-edge research in AI and information retrieval. His research interests include machine learning, deep learning, geometric deep learning, multimodal AI, information retrieval, and large language models. He has pioneered work on Retrieval-Augmented Generation (RAG), Neural Databases, and the development of Fauno, the leading Italian LLM. His teaching experience includes courses on Python for Data Science, Advanced Data Mining, and Statistical Learning at both Sapienza and Luiss Guido Carli. Trappolini's recent publications span top-tier venues like SIGIR, NeurIPS, ECCV, and IEEE Transactions, with a focus on graph neural networks, federated learning, anomaly detection in 5G networks, and multimodal systems. His work shows a strong trend toward robust, privacy-preserving, and multimodal AI systems with real-world applications in language, vision, and network security. Sapienza honor graduate IELTS 8.0 He has advised several teaching roles and delivered courses on Python, data science, and statistical learning. His research is supported through collaborations with industry leaders like Meta and Amazon, and he leads innovative projects such as Fauno and Neural Databases. Trappolini is actively involved in the AI research community and continues to push the boundaries of language and retrieval systems. He is a core member of the RSTLess Lab and has been instrumental in advancing geometric and multimodal deep learning at Sapienza University. His work bridges theoretical innovation with practical implementation, particularly in the domains of Italian language modeling and secure, efficient information retrieval systems.

Sascha Krausمشاهده پروفایل

استاد

• Strategy
• Entrepreneurship
• Family Business
• +۳ مورد دیگر

Prof. Sascha Kraus is a Full Professor of Management and Chairholder at the University of Siegen (Germany), concurrently serving as a teaching and research staff member at the Free University of Bozen-Bolzano's Faculty of Economics and Management since 2020. He holds a doctorate from Klagenfurt University, a Ph.D. from Helsinki University of Technology, and a Habilitation from Lappeenranta University of Technology. His academic journey includes Full Professorships at Utrecht University (Netherlands), the University of Liechtenstein, École Supérieure du Commerce Extérieur Paris (France), Durham University (UK), and part-time roles at other institutions. His research focuses on Strategy, Entrepreneurship, Family Business, and Innovation, with over 100 publications in top journals such as Academy of Management Perspectives and Global Strategy Journal . He is a Clarivate Highly Cited Researcher (2022-2023) and ranked 3rd in Germany/Austria/Switzerland for 'Current Research Performance' (WirtschaftsWoche 2024). His work bridges theoretical frameworks with practical applications, emphasizing digital transformation and sustainability in entrepreneurial contexts. Education: Doctorate in Social and Economic Sciences, Klagenfurt University (AT) Ph.D. in Industrial Engineering and Management, Helsinki University of Technology (FI) Habilitation (Venia Docendi), Lappeenranta University of Technology (FIN) His research trends emphasize AI-driven business models, digital innovation in SMEs, and family firm resilience. He advises on strategic pragmatism for crisis navigation and explores ethical dimensions in family business operations. Recent work includes studies on metaverse entrepreneurship, generative AI applications, and post-pandemic talent management strategies. Awards: WirtschaftsWoche 2024: 3rd in Current Research Performance, 9th in Lifetime Achievement International Council for Small Business: Top 10 Most Influential Researcher Clarivate Highly Cited Researcher 2022-2023 Prof. Kraus leads initiatives at Bozen-Bolzano on innovation contests, strategic agility, and digital entrepreneurship education. His labs focus on cross-cultural management and trilingual academic collaboration, leveraging South Tyrol's unique multicultural environment to foster global research networks.

Flavio De Paoliمشاهده پروفایل

دانشیار

• Semantic Web Technologies
• Data Enrichment
• Services-Oriented Computing
• +۳ مورد دیگر

Flavio De Paoli is an Associate Professor affiliated with the Insid&s LAB. His research focuses on semantic web technologies, data enrichment, and interactive tools for data management. Key projects include the enrichMyData initiative and the development of frameworks like SemT and Protobject . His work spans geospatial data analysis, services-oriented computing, and scalable data solutions. Notable contributions include tools for semantic table interpretation (e.g., MantisTable ), API-based data enrichment, and event-driven analytics. He has also contributed to cloud computing frameworks and semantic wiki integration for knowledge management. His research emphasizes human-in-the-loop systems, automated data processing, and tool development for both academic and industrial applications. Projects like EW-shopp and BEEO demonstrate his focus on applying semantic technologies to real-world challenges in marketing, urban planning, and smart systems.

Marco Schaerfمشاهده پروفایل

استاد

• Artificial Intelligence
• Nonmonotonic Reasoning
• Computational Complexity
• +۶ مورد دیگر

Marco Schaerf is a Full Professor at Sapienza University of Rome, Department of Computer, Control, and Management Engineering. He previously held roles as Associate Professor at the University of Cagliari (1992–1994) and Sapienza (1994–2000). He chairs the Engineering Information Area in Latina and serves on the editorial boards of Artificial Intelligence Journal and Journal of Artificial Intelligence Research . His research focuses on artificial intelligence, nonmonotonic reasoning, computational complexity, digital libraries, and bibliometrics. Notably, he received the 1997 AI*IA Award for Best Young Researcher in AI. Key projects include coordinating the European MAGICSTER project, FIRB 2001 ASTRO, and COFIN grants (2002/2004). He led Sapienza’s participation in Italy’s VQR research assessment program and contributed to digital library systems like the Sapienza Digital Library. His work bridges AI with cultural heritage, including AI-driven visitor experience analysis in museums. Research interests span AI applications, knowledge representation, and computational methods in cultural heritage. He has published over 60 papers in top journals, with recent work on scientometrics, anomaly detection in infrastructure, and weakly supervised semantic segmentation. His contributions to AI theory, robotics, and bibliometric analysis underscore his interdisciplinary impact.

Roberto Grossiمشاهده پروفایل

استاد

• Algorithms and Data Structures
• Dynamic and External Memory Algorithms
• Algorithm Engineering
• +۴ مورد دیگر

Roberto Grossi is a Professor of Algorithms and Data Structures at the University of Pisa's Dipartimento di Informatica. He is actively involved in editorial roles for journals like Theory of Computing Systems and RAIRO-Theoretical Informatics, and has served on program committees for conferences such as SWAT, IWOCA, ESA, FOCS, and SPIRE. His research focuses on algorithms, data structures, graph theory, and bioinformatics, with contributions to string processing, compressed data structures, and algorithm engineering. He advises Ph.D. students and collaborates on projects involving large-scale networks and community detection. Grossi has authored numerous publications, including works on clique enumeration, graphlet algorithms, and phylogenetic reconstruction. His teaching includes courses on Algorithm Design and Data Structures, and he maintains an active presence in academic networks through his research and editorial activities. Education: Not explicitly stated, but inferred from his role as a professor at a top Italian university. Research Interests: Dynamic algorithms, external memory methods, experimental algorithmics, pattern matching in strings and graphs. Grants/Awards: While no specific prizes are listed, his extensive publication record and editorial roles reflect sustained recognition in the field. Lab/Teams: Leads research groups focused on algorithmic foundations and applications, collaborating with international teams on projects like Zuckerli (compressed graph representations) and motif trie indexing. His work bridges theoretical computer science with practical applications in bioinformatics and network analysis.