جستجوی استاد راهنمای دانشگاهی

• 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.

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.

• Combinatorics on Words
• String Algorithms
• Data Mining
• +۳ مورد دیگر

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.

• Algorithms on strings
• Formal language theory
• Bioinformatics
• +۳ مورد دیگر

Chiara Epifanio is a researcher (INFO-01/A) at the University of Palermo , based in the Department of Mathematics and Computer Science . She holds regular office hours on Tuesdays 14:30–17:00 in Room 104, first floor, Via Archirafi 34, and can be reached at chiara.epifanio@unipa.it . Over the past fifteen years she has designed and taught a diverse portfolio of courses spanning Programming , Bioinformatics , Advanced Programming , Teaching Methodologies and Techniques for Computer Science and Pattern Discovery for Life Sciences . These offerings serve degree programmes in Mathematics, Computer Science, Statistics & Data Science, and the recently launched curricula in Data, Algorithms & Machine Intelligence and Computer Science & Artificial Intelligence . Her research lies at the intersection of combinatorics on words , string algorithms , and bioinformatics . She has made sustained contributions to the design of alignment-free distances for biological data, the theory of Sturmian words and their associated graphs, suffix automata tolerant to mismatches, and compact data structures such as linear-size suffix tries. The work repeatedly draws on deep results from formal language theory, automata theory, and discrete mathematics to solve practical problems in sequence analysis. Publications trend: From 2004 onward she has produced a steady stream of peer-reviewed articles that move from foundational combinatorial results on Sturmian words and critical factorization theorems toward application-driven studies on approximate string matching and genomic data mining. A marked acceleration is visible in the 2023 publications addressing k-Hamming and k-edit distances, reflecting current demands in large-scale biological data analytics. Scientific awards: None explicitly mentioned in the provided material. Advising & grants: No PhD or Master’s students are listed in the supplied pages, and no funded project descriptions are available. Laboratories & teams: While no specific lab is named, her teaching and research are embedded within the Department of Mathematics and Computer Science, which hosts groups in algorithms, discrete mathematics, and bioinformatics, and provides access to the university’s ATeN Center and other research infrastructures.

• Computational Biology
• Bioinformatics
• Data Compression
• +۷ مورد دیگر

Raffaele Giancarlo is a Full Professor in the Department of Mathematics and Computer Science at the University of Palermo. His office hours are held on Mondays and Thursdays from 2:30 PM to 5:30 PM in Room 106 of the DMI (Dipartimento di Matematica e Informatica) or via TEAMS. He can be reached at raffaele.giancarlo@unipa.it. Research Focus: Computational Biology, Bioinformatics, Data Compression, and Algorithm Design. Key Contributions: Development of agile platforms for genomic data analysis, optimization of Bloom filters, and innovative text indexing techniques.

Michael Shekelyanمشاهده پروفایل

مدرس

• Data Management
• Database Systems
• Differential Privacy
• +۵ مورد دیگر

Dr. Michael Shekelyan is a Lecturer (Assistant Professor) in Computer Science at Queen Mary University of London (2023–present), part of the School of Electronic Engineering and Computer Science. He holds a PhD in Computer Science from the Free University of Bozen-Bolzano (2018) and a Diploma in Media Informatics from Ludwig Maximilian University of Munich (2014). Previously, he worked as a Research Associate at King's College London (2021–2023) and a Research Fellow at the University of Warwick (2018–2021). His academic journey includes roles as a Meta-Reviewer for NeurIPS and ICDT Proceedings Chair (2024), with extensive service as a reviewer for top conferences like ICML, NeurIPS, ICLR, and journals such as TKDE and IEEE T-IFS. Research Interests: Focuses on algorithms and data structures for managing large/sensitive datasets, including differential privacy, random sampling (e.g., Hidden Shuffle Method), and multidimensional data summarization (e.g., DigitHist). His work bridges theoretical guarantees with practical applications in data management and machine learning. Notable contributions include privacy-preserving top-k selection, efficient sampling over joins, and error-bounded data summaries. Publications: Over 15 peer-reviewed articles in top venues including SIGMOD, ICDE, EDBT, AISTATS, and PVLDB. Key works include 'Streaming Weighted Sampling over Join Queries' (EDBT'23), 'Sequential Random Sampling Revisited' (AISTATS'21), and 'Sparse Prefix Sums' (Information Systems'19, which won an ADBIS award). Grants/Awards: Received the ADBIS'17 Award for Sparse Prefix Sums. Active in organizing conferences and mentoring early-career researchers through reviewing roles. Current projects include a PhD studentship on privacy-preserving algorithms for medical data sharing. Labs/Teams: Leads the Privacy-Preserving Algorithms initiative at Queen Mary, collaborating with international research networks in database systems and data privacy.

• Combinatorics on Words
• Algorithms on Strings
• Automata Theory
• +۴ مورد دیگر

Marinella Sciortino is a Professor at the University of Palermo , affiliated with the School of Basic and Applied Sciences and the Department of Mathematics and Computer Science . She serves as Director of the CINI Research Unit and is a member of the Academic Senate (2022-2024). Sciortino contributes to editorial and scientific committees for journals and conferences, including Theoretical Computer Science and the GRIN Committee . Research focuses on: Combinatorics on Words String Algorithms Automata Theory Symbolic Dynamics Data Compression Biological Sequence Analysis Her work includes groundbreaking studies on the Burrows-Wheeler Transform (BWT) , extending its application to multiple sequences and improving compression efficiency. She has supervised PhD student Giuseppe Romana and participated in numerous conference program committees (e.g., SPIRE , ICTCS , CiE ). Recent scientific contributions (2022-2020): BWT teaching methodologies String attractors Variant discovery in genomics Algorithmic perspectives on alternating BWT Positional clustering for SNP detection Mental rotation effects on computational learning Teaching roles include Laboratorio di Algoritmi (Computer Science) and Teoria dell'Informazione e Compressione Dati (Master's in Computer Science). Contact: marinella.sciortino@unipa.it , +39-091-238-91080.

Nicola PREZZAمشاهده پروفایل

دانشیار

• Algorithms for Massive Data
• Bioinformatics
• Information Systems
• +۴ مورد دیگر

Nicola Prezza is an Associate Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Informatics and Statistics. His research focuses on algorithms for massive data, bioinformatics, and information systems, with a strong emphasis on compressed data structures and computational pan-genomics. He has led the ERC-funded project REGINDEX, exploring compressed indexes for regular languages. Prezza is also a guest editor for multiple scientific journals and has contributed to international symposiums like SPIRE and CPM. In teaching, he instructs courses on algorithms for massive data, advanced computer science, and programming at both undergraduate and graduate levels. His work bridges theoretical computer science with practical applications in compression, streaming algorithms, and graph indexing. Recent projects involve Wheeler automata, suffix arrays, and efficient data structure design. Prezza's research outputs include over 50 indexed publications, with recent highlights in string processing, finite automata indexing, and parameterized-space compression. He collaborates with institutions globally and actively participates in editorial roles for Information and Computation , Information Systems , and Algorithms .

• Bioinformatics
• Algorithms
• Genomics
• +۳ مورد دیگر

Roles & Affiliations: Professor at the Department of Computer Science, University of Pisa since at least 2005. Active in teaching and research in Bioinformatics and Algorithms. Collaborative roles at University of Siena for Bioinformatics master programs. Affiliations: University of Pisa (primary), University of Siena (visiting roles). Education Background: PhD in Computer Science (implied via academic rank) Advanced training in Computational Biology and Algorithm Design Research Interests: Focuses on string algorithms with applications in bioinformatics , including genomic sequence analysis, variant discovery, and algorithmic foundations. Specializes in elastic-degenerate string processing, pangenome graphs, and efficient data structures for large-scale genomic data. Develops tools like WhatsHap for haplotype assembly and RettDb for disease genomics. Teaching Contributions: Core courses in Algorithms, Bioinformatics, and Programming for undergrad/graduate students Master's courses in Bioinformatics and Data Science (University of Pisa/Siena) PhD-level workshops on genomic algorithms Recent Trends in Articles: Recent work emphasizes deep learning applications in genomics , efficient string indexing methods , and pangenome analysis . Explores new paradigms for comparing elastic-degenerate strings and managing genomic variation data at scale. Active in algorithmic foundations and interdisciplinary applications. Grants & Labs: Leads research teams in bioinformatics algorithm development. Involved in EU-funded projects on computational genomics and algorithmic methods. Collaborates with medical institutions on translational research (e.g., Rett syndrome genomic database).

Paolo Ferraginaمشاهده پروفایل

استاد

• Data Compression
• Learned Data Structures
• Search Engines
• +۴ مورد دیگر

Prof. Paolo Ferragina is a Full Professor at the Department of Computer Science, University of Pisa. He leads the Advanced Algorithms and Application Lab (Acube), focusing on algorithms for Big Data compression, indexing, and analysis. His work includes collaborations with MIT, Harvard, Google, and others. He has received prestigious awards like the ACM Paris Kanellakis Award (2022) and the ESA Test of Time Award (2023). Education: Laurea (summa cum laude, 1992) and PhD (1996) from the University of Pisa, Postdoc at Max-Planck Institute for Informatics (1997–1998). He has held roles such as Vice-Rector for ICT (2019–2022) and President of the PhD in Computer Science program (2017–2020). Research interests span compressed data structures, search engines, sports analytics, and DNA-based data storage. His work on the FM-index and TAGME system has had significant impact. He co-founded PlayeRank, a startup for soccer performance analysis. Grants and collaborations include Google Cloud Research Innovator (2022), Yahoo! Research Award (2007–2010), and projects with European Broadcasting Union and ST Microelectronics. He has authored over 180 papers and books like Computational Thinking and Pearls of Algorithm Engineering . Labs/Teams: Acube Lab at University of Pisa, collaborations with MIT’s Senseable City Lab, Harvard’s Pinello Lab, and industry partners like Bloomberg and ENEL.

Francesco Tosoniمشاهده پروفایل

پژوهشگر ارشد

• Lossless Data Compression
• String Indexing
• Big Data Analytics
• +۴ مورد دیگر

Francesco Tosoni is a Research Fellow at the University of Pisa's Department of Computer Science, affiliated with the Acube Laboratory (A³), directed by Prof. P. Ferragina. He holds a PhD in Computer Science from the University of Pisa, completed in 2024, and an MSc in Computer Science and Networking from the University of Pisa and Sant’Anna School of Advanced Studies (2020). His research focuses on lossless data compression, string indexing, big data analytics, and energy-efficient algorithms. He has conducted collaborative work with the Software Heritage team and was a visiting researcher at the University of Chile in 2022. Awards include the Con.Scienze 2020 Best Thesis Award and recognition as the top graduate in his MSc program. Recent projects include developing compressed formats for matrices and trie structures, with applications in green computing and efficient storage systems. Education: PhD in Computer Science, University of Pisa (2024), advisors: P. Ferragina and G. Manzini MSc in Computer Science and Networking, University of Pisa & Sant’Anna School (2020) BSc in Computer and Electronic Engineering, University of Perugia Research Interests: Lossless data compression techniques, energy-efficient algorithms, matrix and trie compression, stringology, big data analytics, and urban mobility platforms. Recent work emphasizes environmental sustainability in computing through optimized compression formats. Key Contributions: Developed the CoCo-trie compression method for efficient string storage Proposed novel compressed formats enabling direct matrix operations without decompression Recipient of grants for urban mobility algorithms and software heritage projects Awards: Con.Scienze 2020 Best Thesis Award (MSc thesis) Best Graduate in MSc Computer Science and Networking (2018/19) Advising & Grants: Supervised Federico Ramacciotti's MSc thesis on compression techniques in key-value stores Recipient of a 2020 research grant on algorithms for urban mobility platforms Labs & Teams: Core member of the Acube Laboratory, collaborating with global teams such as the Software Heritage initiative and Prof. Gonzalo Navarro's group in Chile.

Simone Faroمشاهده پروفایل

دانشیار

• Algorithmic solutions for text-processing
• Natural language processing
• Algorithmics in quantum domain
• +۱۲ مورد دیگر

Simone Faro is an Associate Professor at the University of Catania , affiliated with the Department of Mathematics and Computer Science . He has held this position since 2015, after serving as a University Researcher from 2006 to 2015. Teaches Algorithms and Laboratory (Bachelor's L31) and Quantum Computer Programming (Master's LM18) Also teaches Computer Science (Bachelor's L20) and previously held Computational Linguistics (Master's LM49) at the Department of Humanities Research Focus : Algorithmic solutions for text-processing and natural language processing Combinatorial algorithms on graphs and networks Privacy-preserving text processing Bit-parallel automata simulations Quantum domain algorithm design Research Projects : Co-Principal Investigator for STORAGE (2020), University of Catania Responsible for Scientific Degrees Plan (PLS 2019/21) Component in Godscapes project (2020) and COMPACT project (2015) Principal Investigator for multiple string matching and secure text processing projects Editorial Roles : Guest Editor for Algorithms journal (MDPI) special issues Editor for String Processing and Combinatorial Algorithms (Springer Nature) Member of Le Matematiche editorial team Contact: faro@dmi.unict.it | simone.faro@unict.it

• 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.

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.

• Data Compression
• Indexing Data Structures
• Algorithms
• +۷ مورد دیگر

Giovanni Manzini is a Professor of Computer Science at the University of Pisa's Department of Computer Science. He holds a PhD in Mathematics from the Scuola Normale Superiore di Pisa and has held visiting roles at MIT, Johns Hopkins University, and the University of Melbourne. Previously, he served as Assistant Professor at the University of Torino and Associate Professor at the University of Eastern Piedmont. His research focuses on designing algorithms and data structures for data compression, indexing massive datasets, and computational biology. Notable contributions include advancements in the Burrows-Wheeler Transform (BWT), grammar-based compression, and matrix operations optimization. He received the ACM Paris Kanellakis Theory and Practice Award (2022) and the 2023 Test-of-Time Award from the European Symposium on Algorithms. His recent work explores two-dimensional string repetitiveness, BWT-based indexing, and green computing techniques for matrix operations. Manzini has contributed to over 100 conference program committees since 2020, including DCC, ALENEX, and SPIRE. His teaching spans courses on algorithms, programming languages, and numerical analysis. Current courses include Algorithms and Data Structures for Data-intensive Applications and an undergraduate laboratory course on programming fundamentals. He leads software projects such as MMRepair (grammar-compressed matrix multiplication), BigBWT (BWT construction via prefix-free parsing), and EZcount (microRNA quantification tools). He also mentors the Scuola Ortogonale under the Elicsir Foundation, promoting STEM education.