جستجوی استاد راهنمای دانشگاهی

• Artificial intelligence
• Data compression
• Deep learning
• +۱۵ مورد دیگر

Enrico Magli is a Full Professor at the Department of Electronics and Telecommunications (DET) at Polytechnic University of Turin, Italy. He serves as Director of the Image Processing and Learning group and Coordinator of the 'ICT for Smart Societies' M.Sc. degree program. Additionally, he is a committee member of the PhD program in Electrical, Electronic and Communications Engineering and a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. Professor Magli's research focuses on applying machine learning and deep learning methods to satellite imaging, with applications to onboard processing and image analysis on the ground. His work spans deep learning for image and video analysis, image and video compression, compressive sensing, satellite imaging, and graph signal processing. He has published over 90 journal papers with 5900+ citations and an h-index of 40 on Google Scholar. His recent publications demonstrate a strong focus on developing deep learning architectures for satellite image processing, particularly for onboard applications. His research addresses challenges in hyperspectral image compression, super-resolution, change detection, and efficient neural network architectures suitable for resource-constrained satellite environments. There's also significant work on secure authentication systems using deep learning techniques and neural network optimization for edge devices. Elevated to IEEE Fellow (2017) 'for contributions to compression and communication of remotely sensed imagery' IEEE Geoscience and Remote Sensing Society 2011 Transactions Prize Paper Award IEEE Multimedia 2019 Best Paper Award Best Paper Awards at IEEE ICIP (2015, 2019) ERC Starting grant (consolidator type) and ERC Proof-of-Concept Grant recipient Multiple Best Paper Awards Francesco Carassa (2011, 2013, 2014) Professor Magli actively supervises numerous PhD students working on cutting-edge topics in deep learning for satellite imaging, image processing, and secure authentication systems. His research is supported by significant grants including ERC projects and multiple commercial contracts with space agencies and technology companies. He leads the Image Processing and Learning (IPL) Group at Politecnico di Torino, which focuses on developing innovative solutions for satellite image analysis and compression.

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.

• Multimedia Security
• Multimedia Forensics
• Adversarial Machine Learning
• +۷ مورد دیگر

Tiziano Bianchi is a Full Professor in the Department of Electronics and Telecommunications at Politecnico di Torino (Polytechnic of Turin), Italy. He serves as the Course Coordinator for the Master's Degree in ICT Engineering for Smart Societies and is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. Previously, he was an Assistant Professor at the same institution starting in 2012, having joined from the University of Florence where he worked as a Research Assistant from 2005 to 2012. Dr. Bianchi's research focuses on multimedia security technologies, with particular expertise in multimedia forensics, signal processing in the encrypted domain, and security aspects of compressed sensing. His work spans adversarial machine learning, image and video forensics, deep learning for video compression, secure authentication based on deep learning, and satellite image compression. He has made significant contributions to privacy-preserving data processing through compressed sensing techniques. His recent publications demonstrate a strong emphasis on applying deep learning to solve complex problems in image processing, security, and data compression. The research shows a clear trajectory toward efficient, secure, and privacy-preserving processing techniques, particularly for resource-constrained environments like wearable devices and satellite systems. Among his notable recognitions are the Journal of Visual Communication and Image Representation Best Editor of 2022 Award, The SIGMM Test of Time Paper Honorable Mention in Multimedia Security and Privacy, and the 2019 Best Paper Award from IEEE MultiMedia. He has also received multiple editorial awards for his work as Senior Area Editor for the Elsevier Journal of Visual Communication and Image Representation and Associate Editor for the IEEE Transactions on Information Forensics and Security. Dr. Bianchi is the co-founder of ToothPic, a startup offering breakthrough technologies for camera identification, and has co-invented four patents related to cryptographic key obfuscation, user authentication, and camera recognition techniques. He has authored over 100 papers in international journals and conference proceedings, demonstrating sustained scholarly productivity throughout his career.

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.

• 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.