Grammar-Based Compression Professors and Supervisors

Danny Huckeمشاهده پروفایل

پژوهشگر

University of SiegenGermany

Danny Hucke is a researcher affiliated with the University of Siegen, Department of Electrical Engineering and Computer Science. His work focuses on advanced data compression techniques, algorithmic complexity, and formal verification of streaming systems. Education: PhD in Grammar-based compression for strings and trees (University of Siegen, 2019) Research Interests: Driven by challenges in grammar-based compression, empirical entropy metrics, and formal language processing in streaming environments, his research bridges theoretical computer science and practical algorithm design. Key areas include: Data Compression for Trees and Strings Sliding-Window Algorithms Circuit Complexity Algorithmic Entropy Analysis Scientific Contributions: His work has been recognized with a Best Paper Award at SPIRE 2016. Publications span top-tier venues like IEEE Transactions on Information Theory , ACM TOCT , and conferences including ICALP, STACS, and LATIN. Contact: Department of Electrical Engineering and Computer Science, University of Siegen, Hölderlinstrasse 3, D-57076 Siegen. Email: hucke@eti.uni-siegen.de . Phone: +49-271-740-3415. Office: Room H-A 7104. Collaborations: Conducted research within the group of Prof. Markus Lohrey, collaborating extensively with Moses Ganardi, Louisa Seelbach, and Eric Nöth.

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Louisa Seelbach Benknerمشاهده پروفایل

پژوهشگر

University of SiegenGermany

Louisa Seelbach Benkner is a postdoctoral researcher at the University of Siegen , affiliated with the Faculty of Electrical Engineering and Computer Science. Her research focuses on data compression, particularly grammar-based compression, succinct data structures, universal source coding, and analytic combinatorics. She has contributed to theoretical advancements in tree compression and entropy analysis. Education: PhD in Computer Science from University of Siegen (2023). Research interests center on optimizing data compression techniques for tree structures, analyzing entropy bounds, and applying combinatorial methods to algorithm design. Her work bridges theoretical computer science and practical data encoding solutions. Selected publications highlight her expertise in hypersuccinct tree compression, empirical entropy comparisons, and fringe subtree analysis. Key collaborations include researchers like Markus Lohrey, Stephan Wagner, and Travis Gagie. Scientific awards: Capocelli Prize at DCC 2019 Best Student Paper Award at SPIRE 2020 Teaching roles include formal languages, complexity theory, logic, and algorithmics courses. She has also contributed to mathematics instruction in analysis and linear algebra.

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Giovanni Manziniمشاهده پروفایل

استاد

University of PisaItaly

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.

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Christopher Whiteمشاهده پروفایل

دانشیار

University of Massachusetts AmherstUnited States

Christopher White is an Associate Professor of Music Theory and Graduate Program Director at the University of Massachusetts Amherst's Department of Music and Dance. His research focuses on applying big data and computational methods to music analysis, as detailed in his book The Music in the Data (2022). He has taught at Yale University and the University of North Carolina at Greensboro, holds a PhD from Yale, and has additional education from Queens College–CUNY and Oberlin Conservatory. His work bridges music theory, AI, and empirical studies, with publications in journals like Music Perception and Music Theory Online . Research interests include corpus analysis, tonal traditions, and the intersection of music with computational modeling. He has written for public outlets like Slate and The Chicago Tribune , addressing topics such as AI in music and music cognition. As an organist, he performs with the Three Penny Chorus and Orchestra, appearing on America's Got Talent . He currently serves as secretary for the New England Conference of Music Theorists. Key awards include the 2023 Emerging Scholar Award from the Society for Music Theory. His recent articles explore AI's impact on musical creativity, meter-perception interactions, and computational rhythm studies. His book The Music in the Data won the 2023 award, arguing for humanist approaches to music corpus analysis.

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Kathleen McKeownمشاهده پروفایل

استاد

Columbia UniversityUnited States

Kathleen McKeown is the Henry and Gertrude Rothschild Professor of Computer Science at Columbia University , where she has been a faculty member since 1982. She served as Founding Director of the Data Science Institute (2012–2017), Department Chair (1998–2003), and Vice Dean for Research (2003–2005) at the School of Engineering and Applied Science . Education : PhD in Computer Science and Information Science (1982) and MS in Computer and Information Science (1979) from the University of Pennsylvania , AB in Comparative Literature (1976) from Brown University Her research focuses on Natural Language Processing , Text Summarization , and Natural Language Generation , with applications in Social Media Analysis , Disaster Response , and Energy Behavior Messaging . She has pioneered neural methods for summarization, sentiment analysis in low-resource languages, and multi-media explanations. Her work includes 15 recent publications (2011–2013) on topics like discourse relation disambiguation , MT error correction , influencer detection , and hierarchical web summarization , reflecting trends in deep learning , cross-lingual NLP , and social media analytics . Honors & Awards : IEEE Innovations in Societal Infrastructure Award (2023) AAAI Fellow (1994) ACM Fellow (2003) Founding ACL Fellow (2012) Anita Borg Women of Vision Award (2010) NSF Presidential Young Investigator (1985) She leads the NLP Group at Columbia, focusing on real-time disaster updates , behavioral NLP , and sentiment analysis for marginalized communities. Her career spans leadership roles , grant-funded research , and foundational contributions to text generation and summarization .

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Nicole Schweikardtمشاهده پروفایل

استاد

Humboldt UniversityGermany

Nicole Schweikardt is a Professor at the Institute of Computer Science within Humboldt University of Berlin . Her research focuses on Theoretical Computer Science , particularly in Database Theory , Formal Logic , and Algorithmic Meta-Theorems . Academic Rank: Professor Contact: schweikn@informatik.hu-berlin.de Research Interests : Nicole investigates logical characterizations of database query languages, algorithmic meta-theorems for sparse graphs, and efficient enumeration techniques. Her work bridges formal logic, computational complexity, and practical database systems. Scientific Awards : 2018 ACM PODS Alberto O. Mendelzon Test-of-Time Award Recent Article Trends : Nicole's recent publications emphasize schema matching , spanner evaluation , first-order logic extensions , and query enumeration . Her work spans theoretical foundations (e.g., counting quantifiers, Hanf normal forms) and practical applications (e.g., event stream analysis, document compression).

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Sanja Fidlerمشاهده پروفایل

دانشیار

University of TorontoCanada

Sanja Fidler is an Associate Professor at the University of Toronto (Department of Computer Science) within the Mathematical and Computational Sciences School. She is also a co-founder and affiliated faculty at the Vector Institute and serves as VP of AI Research at NVIDIA. Her research focuses on the intersection of Computer Vision, Machine Learning, and Graphics, with emphasis on 3D reconstruction, generative models, and interactive methods. She previously held a Research Assistant Professor position at the Toyota Technological Institute at Chicago. Research Interests: Dr. Fidler specializes in Computer Vision and Machine Learning, particularly in areas such as 3D vision, 3D reconstruction/synthesis, object detection, and interactive scene understanding. Her work bridges graphics and vision, addressing challenges in geometric representations, generative models, and real-world applications like medical imaging and autonomous systems. Awards: She has received notable awards including the Canada CIFAR AI Chair (2018), Connaught Innovation Award (2020), NVIDIA Pioneer of AI Award (2016), and multiple outstanding reviewer distinctions at top conferences. Her teaching excellence was recognized with the Professor of the Year award by UofT's CS Student Union in 2014/2015. Collaborations & Labs: Leading the NVIDIA Toronto AI Research Lab and contributing to initiatives at the Vector Institute, her work integrates academic and industrial research. She co-organized workshops on topics like 'Geometry Meets Deep Learning' and '3D Indoor Scene Understanding' at major conferences.

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Tamara Polajnarمشاهده پروفایل

پژوهشگر

University of CambridgeUnited Kingdom

Dr. Tamara Polajnar is a Visiting Researcher at the Department of Computer Science and Technology, University of Cambridge. Her research focuses on computational linguistics and natural language processing, with particular emphasis on distributional semantics and semantic compositionality. Her research interests span multiple areas including computational semantics, distributional models, and semantic composition. She develops tensor-based methods for representing meaning and investigates techniques for semantic analysis of text at different levels from phrases to documents. Her work combines theoretical linguistics with practical machine learning approaches. Her publications primarily focus on compositional distributional semantics, exploring how to effectively combine word vectors to represent larger linguistic units. Recent work examines discourse-level representations and evaluation methods for semantic models. She has contributed datasets and frameworks that advance the understanding of how computational models can capture linguistic phenomena. She actively contributes to the research community through the development of specialized datasets and evaluation resources. Her work on RELPRON provides valuable resources for evaluating compositional distributional models, while her research on short scientific summaries addresses challenges in processing academic text.

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Digalakis Vassilisمشاهده پروفایل

استاد

Technical University of CreteGreece

Prof. Vassilis Digalakis is a Professor at the Technical University of Crete in the Department of Electronic and Computer Engineering, part of the School of Electrical and Computer Engineering. He holds a Ph.D. in Electrical and Systems Engineering from Boston University and has extensive experience in academia, industry, and public service. His roles include former Rector of the Technical University of Crete (2013-2017) and Deputy Minister of Education and Religious Affairs (2020-2021). Education: Ph.D. in Electrical and Systems Engineering, Boston University (1992) M.Sc. in Electrical Engineering, Northeastern University (1988) B.Sc. in Electrical Engineering, National Technical University of Athens (1986) Research Interests: Prof. Digalakis specializes in machine learning, speech recognition, pattern recognition, and dialog systems. His work emphasizes developing advanced algorithms for speech processing, multimodal interfaces, and adaptive systems. Notable contributions include innovations in linear dynamical models for speech synthesis and robust pronunciation evaluation techniques. Awards and Recognition: He has received two IEEE Signal Processing Society Best Paper Awards (1999, 2000) and holds five patents related to speech recognition technologies. His leadership roles include Director of the Telecommunications Systems Institute (2005–2013) and Chair of the ECE Department (2003–2005). Professional Activities: He served as President of the Hellenic Universities Rectors’ Synod (2015) and later as Chairman of the Standing Committee on Cultural and Educational Affairs. His work extends to industry collaborations, including founding contributions to Nuance Communications and Dialogos Speech Communications. Labs and Projects: Associated with the Information and Networks Laboratory at TUC, his research focuses on cutting-edge solutions in telecommunications and AI-driven speech technologies.

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Jakob Grue Simonsenمشاهده پروفایل

استاد

University of CopenhagenDenmark

Jakob Grue Simonsen is a Professor and Department Chair at the Department of Computer Science (DIKU), University of Copenhagen. He holds a Dr. Scient., PhD, and MBA. His research focuses on the mathematics of computation, including computability theory, term rewriting, lambda calculus, complexity hierarchies, and symbolic dynamics, with applications in information retrieval and human-computer interaction. His primary research explores infinite computational processes and their finite representations, alongside practical work in neural hashing, fact-checking systems, and quantum language models. Previously, he contributed to constructive mathematics and biocomputing. An analysis of his 15 most recent publications (2015–2022) reveals strong emphasis on: Theoretical computability and game-theoretic models Neural networks for NLP (BERT, semantic hashing) Fact verification and explainable AI methodologies Efficient retrieval algorithms and recommendation systems Awards: RTA 2004 Best Paper Award CHI '13 Honorable Mention CHI '14 Honorable Mention

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Maciej Woźniakمشاهده پروفایل

استاد

AGH University of KrakowPoland

Maciej Woźniak serves as a university professor at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków, actively contributing to research and teaching staff while participating in the Computer Science Discipline Council and College of the Faculty. His office is located at D-17, ul. Kawiory 21, III, 4.58, with primary contact via macwozni@agh.edu.pl. His research spans computational mathematics and high-performance computing, specializing in parallel algorithms for numerical simulations including isogeometric analysis, finite element methods, and multi-frontal solvers. Key applications address environmental challenges like hail suppression and urban smog reduction, alongside biomedical modeling of tumor growth and airborne pathogen dispersion. Methodological innovations focus on GPU acceleration, shared-memory architectures, and efficient integration techniques for partial differential equations. Analysis of his 2019-2024 publications reveals consistent advancement in parallel computational frameworks for environmental and biomedical applications, demonstrating interdisciplinary impact through experimental validation of hail cannons, smog reduction technologies, and pathogen dispersion modeling during the COVID-19 pandemic. His work bridges theoretical algorithm development with real-world problem solving across physics, engineering, and life sciences. Scientific Awards: No specific awards were documented in the provided sources. Student advising, research grants, laboratory affiliations, and educational background details beyond his PhD, DSc, and Eng. titles remain unspecified in the available materials, though his extensive publication record suggests active mentorship and collaborative research leadership.

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Rita Hartelمشاهده پروفایل

مدرس ارشد

Paderborn UniversityGermany

Dr. Rita Hartel serves as a Senior Lecturer and Research Associate in the Department of Databases and Electronic Commerce at the University of Paderborn's Institute of Computer Science, while also fulfilling the critical role of Academic Advisor for the Computer Science Student Office where she guides undergraduate and graduate students. Her research forms two distinct yet complementary pillars: cutting-edge data compression algorithms for bioinformatics (specializing in Burrows-Wheeler Transform optimizations for DNA sequence data) and graph databases (developing grammar-based compression for knowledge graphs), alongside innovative digital humanities work applying OCR and semantic analysis to comics historiography with a focus on German traditions. This interdisciplinary approach bridges computational rigor with cultural scholarship. Dr. Hartel demonstrates consistent research productivity with five publications from 2021-2025 in premier venues including the Data Compression Conference and specialized workshops. Her work reflects strong international collaboration patterns and likely benefits from active grant funding given the technical resources required for bioinformatics compression research. As an Academic Advisor, she provides essential student support while her dual research focus creates unique opportunities for students interested in either computational methods or digital humanities applications.

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Jan Heegård Petersenمشاهده پروفایل

دانشیار

University of CopenhagenDenmark

Jan Heegård Petersen serves as an Associate Professor in the Department of Nordic Studies and Linguistics at the Faculty of Humanities, University of Copenhagen. His research focuses on Danish language evolution in diaspora communities, phonetic reduction phenomena, and documentation of the endangered Kalasha language of Pakistan. His academic credentials include: PhD in Linguistics (2006), University of Copenhagen MA in General Linguistics (1998), University of Copenhagen BA in General Linguistics (1993), University of Copenhagen Petersen's scholarly work spans heritage language dynamics, examining phonetic, morphological, and syntactic changes in Danish spoken by immigrant communities across the Americas. His investigations into phonetic reduction explore the spectrum between maximally expanded and extremely compressed speech forms, linking sound reduction to grammatical and pragmatic functions. The Kalasha language documentation project represents decades of fieldwork on this Indo-Aryan language's phonology, grammar, and cultural context. Recent publications (2024-2025) reveal concentrated research on heritage Danish varieties, with significant attention to grammatical gender systems, code-switching patterns, and phonetic innovations among diaspora speakers. These studies frequently employ corpus-based statistical methods and reflect international collaboration, particularly with Karoline Kühl of Hamburg. As an educator, Petersen supervises BA/MA projects across Danish linguistics domains. His research has received funding from the Carlsberg Foundation, A.P. Møller Foundation, and University of Copenhagen. Professional service includes editorial roles for NyS journal and treasurer position for the Linguistic Circle of Copenhagen.

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