جستجوی استاد راهنمای دانشگاهی

Giorgio Vinciguerraمشاهده پروفایل

پژوهشگر ارشد

• Data Compression
• Algorithm Engineering
• Learned Data Structures
• +۳ مورد دیگر

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.

• Neurophysics
• Quantum Biology
• Biomedical Imaging
• +۶ مورد دیگر

Albrecht Haase is an Associate Professor in the Department of Physics at the University of Trento, Italy, and Head of the Neurophysics and Biophotonics Laboratory within the Center of Mind/Brain Sciences (CIMeC). His research focuses on neurophysics, quantum biology, and advanced imaging techniques applied to insect models. He holds habilitations in Experimental Physics (2016) and Applied Physics (2018). Education: PhD in Physics (2005), University of Heidelberg, Germany Diploma in Physics (2000), Freie Universität Berlin, Germany External Diploma Thesis at University of Innsbruck, Austria (1999–2000) Research Interests: Neurophysics, quantum biology, neural network dynamics, multiphoton microscopy, olfaction, magnetoreception, and insect sensory systems. He uses cutting-edge imaging tools to study odor coding, neuroplasticity, and the effects of pesticides on insect brains. Key Projects: Development of bioimaging facilities at CIMeC’s Manifattura campus, optogenetic manipulation of olfactory networks, and investigations into magnetic field perception in insects. His work bridges physics, neuroscience, and ecology. Professional Affiliations: German Physical Society (DPG), Italian Physical Society (SIF), Italian Society of Neuroscience (SINS), and bee research associations EurBee and COLOSS.

• Algorithms and Data Structures
• Big Data
• High-Performance Computing
• +۱۲ مورد دیگر

Francesco Silvestri is an Associate Professor in Computer Engineering at the Department of Information Engineering , University of Padova . He has held previous roles as Assistant Professor (2016-2019), Post-Doc (2015-2016 at IT University of Copenhagen; 2009-2014 at University of Padova), and Part-Time Lecturer (2013-2014 at IT University of Copenhagen). He earned a Ph.D. in Computer Engineering from the University of Padova (2009). Research Focus: Algorithms and data structures for big data processing, high-performance computing, similarity search, graph mining, I/O-efficient algorithms, mobility algorithms, and differential privacy applications. Teaching: Courses include Big Data Computing and Algorithm Design (University of Padova), Advanced Algorithms Seminar (IT University of Copenhagen). Scientific Contributions: ICDT Best Paper Award (2023) for subgraph enumeration ACM SIGMOD Research Highlights (2020) for fair similarity search Developed patented MapReduce methods for triangle enumeration Collaborations and Supervision: Current PhD students: Mariafiore Tognon (2024), Linghan Zeng (2023), Fabrizio Boninsegna (2022). Collaborators include Ninh Pham (University of Auckland), Gianfranco Bilardi, and Rasmus Pagh. Personal Interests: Active in Dottor Clown Padova for hospital clowning, family life with wife Elisa Salvagnin and three children, and cycling advocacy in Padova.

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.