Meltem Aksoy is an Associate Professor at the Department of Architecture, Istanbul Technical University. Her academic work spans urbanization processes, social housing practices, and digital design technologies, with a particular focus on user participation and ecological sustainability in the built environment. Current affiliation: Department of Architecture, Istanbul Technical University Research themes: Composite cities, informal settlements, parametric design, ambient intelligence Her research explores: Intersections of digital technologies and spatial perception Methodologies for participatory social housing Computational approaches to architectural decision-making Temporal-spatial dynamics in contemporary urbanization Recent publications demonstrate trends in: AI-assisted design negotiation frameworks Fuzzy logic applications in architectural performance Critical analyses of composite urban structures Evolution of home-street relationships She has supervised 33 graduate theses spanning topics from: Architectural essay films Political space narratives Smart facade technologies Urban data representation
Prof. Dr. Uli Sauerland is Deputy Director at the Leibniz Centre for General Linguistics (ZAS) in Berlin and Adjunct Professor at the University of Potsdam since 2018. He leads Research Area 4 'Semantics & Pragmatics' and oversees key projects including the ERC-funded Realizing Leibniz’s Dream , DFG-ANR Boolean Connectors , and CRC 1412 projects A05 and B06 . PhD in Linguistics (MIT, 1998) Habilitation (Tübingen, 2003) Rehabilitation (Potsdam, 2007) His research focuses on semantic-pragmatic interfaces , register variation , language acquisition , and cognitive modeling of linguistic phenomena . Recent work includes LMBayes (Bayesian language modeling), DUAL (semantic-pragmatic interactions), and studies on quantifier scope in children. Publications span formal grammar , cognitive development , and language processing . Notable scientific awards : CNRS Fellowship (2024) Academia Europea Membership (2019) ESF Community of Experts (2018) He contributes to editorial boards of First Language , Journal of Semantics , and Language Acquisition , and co-founded the CRC 1412 Register research program.
Prof. Jörn Ostermann is a Full Professor and Head of the Institut für Informationsverarbeitung at Leibniz Universität Hannover since 2003, with prior roles at AT&T Bell Labs and AT&T Labs-Research. He served as Dean of the Faculty of Electrical Engineering and Computer Science (2011–2013) and member of the Senat (since 2020). His research spans video coding, computer vision, machine learning, 3D modeling, and computer-human interfaces , with applications in SAR imaging, predictive maintenance, children's speech analysis, and cochlear implants. Key projects include Next Generation Video Coding , Conditional Coding for Learned Compression , and GreenAutoML4FAS . Notable trends in his recent publications (2025–2023) include Neural network-based video compression Uncertainty estimation in speech recognition Zero-delay coding for cochlear implants Domain adaptation for aerial image segmentation 3D mesh compression standards Error concealment in VVC coding Scientific recognitions: AT&T Standards Recognition Award (1998) ISO Award (1998) IEEE Fellow (2005) Distinguished Lecturer, IEEE CAS Society (2002/2003) MPEG Convenor (2020–2023) He co-authored a graduate textbook on Video Communications , holds >30 patents, and has led >20 research projects. His work bridges academic research and industrial standardization, particularly in MPEG and IEEE committees.
Domagoj Ševerdija is an Assistant Professor at the School of Applied Mathematics and Informatics at Josip Juraj Strossmayer University of Osijek, where he leads the Computer Science and Machine Learning Research Group. He holds a PhD in Electrical Engineering (2013) and a dual BS in Mathematics and Computer Science (2007) from the University of Osijek. His research spans computational linguistics, natural language processing, machine learning, and combinatorial optimization, with applications in bioinformatics, robotics, and energy systems. Recent work focuses on neural language models, domain adaptation techniques, and efficient algorithm design. Publications show strong interdisciplinary trends: computational linguistics (Croatian morphology, sentence embeddings), bioinformatics (cell typing, RNA splicing), and algorithm optimization (terrain guarding, matrix operations). Recent papers emphasize knowledge distillation, domain adaptation, and compressed representations. Awards: Best paper award in AIS - Artificial Intelligence Systems track (MIPRO 2023) Research funding includes: Computer-Assisted Corpus Linguistics (UNIOS, 2019-2020) Croatian Identity Network Framework (Adris Foundation, 2020-2021) Croatian Language in Global Cloud (Adris Foundation, 2019-2020) Leads the Computer Science and Machine Learning Research Group, coordinating projects in NLP, computational geometry, and AI applications.
Tomohiro I is an Associate Professor in the Department of Artificial Intelligence at Kyushu Institute of Technology, Japan. He has been in this position since January 2019, following a research associate role at the same institution from 2015 to 2018. Prior to that, he held postdoctoral positions at Kyushu University and TU Dortmund, Germany. His academic foundation includes a Ph.D. in Science from Kyushu University, awarded in 2012. His research primarily centers on string algorithms , with a strong emphasis on compressed data structures , pattern matching , indexing , and algorithmic efficiency . Key interests include Lyndon factorization, Lempel-Ziv compression, palindrome matching, and reverse engineering of string data structures. He frequently collaborates with prominent researchers like Hideo Bannai and Shunsuke Inenaga, producing high-impact work in theoretical computer science. His recent publications demonstrate a consistent focus on improving algorithms for string processing in compressed formats. Work on Re-Pair , RLBWT , and SLP encoding highlights his expertise in space-efficient computation. The 2022 Best Paper Award at IWOCA for work on Lyndon subsequences underscores the quality and recognition of his contributions. His research bridges theoretical analysis with practical algorithm design. Best Paper Award, International Workshop on Combinatorial Algorithms (IWOCA) 2022 Tomohiro I advises graduate students in his laboratory, although he currently notes that the lab is not accepting new research students. His work involves significant algorithmic research, often supported by theoretical grants or institutional funding, leading to numerous publications in peer-reviewed journals and conferences. He has also presented his work in invited talks, such as at CompressedAI2025 and WCTA 2024, indicating active engagement with the research community. He leads a research laboratory at Kyushu Institute of Technology, focused on advanced string processing and compressed data structures. His team collaborates extensively on algorithm design and analysis, contributing to the broader field of combinatorial pattern matching.
Guillaume Rabusseau is an Associate Professor at Mila and the Department of Computer Science and Operations Research (DIRO) at Université de Montréal , holding a Canada CIFAR AI Chair since 2019. His research spans machine learning, theoretical computer science, and multilinear algebra. Education : PhD in Computer Science (2016) from Aix-Marseille Université , MSc in Fundamental Computer Science from AMU, BSc in Computer Science (distance learning) from AMU. Research Interests : Tensor methods for machine learning, spectral learning algorithms, connections between weighted automata, tensor networks, and RNNs, low-rank regression, and nonlinear computational models on structured data. Publication Trends : Recent work focuses on tensor train decomposition, temporal graph benchmarks, quantum-inspired ML, spectral regularization, and formal methods for sequence modeling. Collaborative papers address dynamic graphs, foundational models for molecular learning, and high-order pooling in GNNs. Scientific Awards : Canada CIFAR AI Chair (2019–present, renewed) Advising : Supervises PhD students like Maude Lizaire and Pascal Tikeng Notsawo, and MSc students such as Soroush Omranpour. Past advisees include Andy Huang (now at Oxford) and Tianyu Li (Samsung).
Dr. Weiwei Sun is a University Senior Lecturer in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on Natural Language Processing (NLP), including semantic parsing, dialogue systems, retrieval-augmented generation, and computational linguistics. He teaches courses such as 'Introduction to Computational Semantics' and 'Natural Language Processing' at the undergraduate and postgraduate levels. His work spans theoretical and applied NLP, with recent contributions to generative retrieval systems, semantic dependency analysis, and cross-lingual NLP techniques. Notable projects include developing frameworks like LLM4Ranking for document reranking and exploring phonetic reconstruction of Middle Chinese using optimization methods. Dr. Sun's publications emphasize improving NLP model efficiency, robustness, and contextual understanding through methods like sparse adapters and consistency alignment. His research also intersects with materials science, as seen in studies on memristor failure mechanisms. No academic awards are explicitly mentioned in the provided text.
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 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.
Dr. Anna Paszyńska is a Lecturer at Jagiellonian University, specializing in computational science and graph-based algorithms. Her research focuses on graph grammars, artificial intelligence, and numerical methods for solving complex engineering problems. She has contributed to advanced simulations in environmental modeling, tsunami prediction, and adaptive finite element methods. Her work integrates graph theory with computational techniques to optimize solver performance and address real-world challenges like pollution tracking and disaster simulations. Affiliation: Jagiellonian University Key Research Areas: Graph grammars, AI-driven simulations, computational fluid dynamics, and numerical algorithms. Her recent publications highlight innovations in physics-informed neural networks (PINNs) for solving PDEs and optimizing tsunami and pollution models. She actively collaborates on interdisciplinary projects involving environmental science, engineering, and computer science.
Prof. Roni Katzir is an Associate Professor in the Department of Linguistics and a member of the Sagol School of Neuroscience at Tel Aviv University. His research focuses on the intersection of mathematical/computational methods and linguistic cognition, investigating how humans represent, learn, and use language knowledge for inference and discourse. He holds a BSc in Mathematics from Tel Aviv University and a PhD in Linguistics from MIT. Education: BSc in Mathematics, Tel Aviv University PhD in Linguistics, Massachusetts Institute of Technology (MIT) His key research areas include computational linguistics, cognitive science, and formal semantics. Notable contributions address the computational modeling of grammar induction, the evaluation of large language models against human linguistic cognition, and the application of Minimum Description Length principles in neural networks. He leads the Computational Linguistics Lab and undergraduate program at TAU and teaches in the Adi Lautman Interdisciplinary Program for Outstanding Students. Recent work critically examines Large Language Models (LLMs), arguing they fail to replicate core aspects of human linguistic competence. His research also explores neural network generalization for grammar induction and the typological stability of logical operators. Prof. Katzir has advised numerous collaborative projects and leads initiatives in computational phonology and semantics. His labs focus on explanatory adequacy in language learning and neurosymbolic AI integration.
Dr. Ratna Saha is a Senior Lecturer at Torrens University Australia, affiliated with the Business and Hospitality Centre for Artificial Intelligence Research and Optimisation (AIRO). She holds a PhD in Computer Science from Flinders University and a BSc in Computer Science and Engineering from Khulna University, Bangladesh. With over 17 years of experience in academia and industry, she focuses on machine learning, medical image analysis, AI-driven healthcare solutions, and generative AI in education. Her research bridges technical innovation with societal impact, particularly in underdeveloped regions. Education: PhD in Computer Science (2020), Flinders University, Australia (Focus: Image Segmentation with Prior Guidance in Cervical Cytology) BSc in Computer Science and Engineering (2007), Khulna University, Bangladesh (Focus: Component-Based Face Detection via SVM) Research Interests: Dr. Saha’s work spans machine learning applications in healthcare diagnostics (e.g., cervical cell segmentation), AI-driven education innovation, and industry 4.0 transformations. She emphasizes interdisciplinary approaches, integrating image processing techniques with medical pathology and leveraging generative AI to enhance learning experiences. Her recent studies explore assembly line optimization in manufacturing and social impacts of business incubation centers in developing economies. Collaborations and Impact: Her research networks span Australia, Bangladesh, and Pakistan, with projects addressing global challenges such as cervical cancer detection and skill development in IT sectors. She actively mentors HDR students in machine learning-based disease prediction and data-driven decision-making frameworks. Current projects include optimizing labor costs in assembly lines using time-study models and fostering entrepreneurial ecosystems through smart educational tools. Professional Experience: Senior Lecturer (Torrens University, 2020–present) Casual Academic (Flinders University, 2016–2020) Lecturer (Khulna University, 2012–present) Senior Software Engineer (Dohatec New Media, 2010–2012) Research Themes: Her publications reflect expertise in medical image segmentation (e.g., cervical nucleus detection), optimization algorithms (e.g., evolutionary harmony search), and socio-economic transformations via technology. Recent trends highlight her shift toward applied AI in education and industry 4.0, complementing her foundational work in computer vision and medical diagnostics. Advising and Grants: Dr. Saha supervises PhD students in machine learning and data-driven healthcare. She has secured research grants focusing on AI applications in education and healthcare, and her work is cited across interdisciplinary fields (h-index: 6, 102 citations).
Christoph Koch is an Associate Professor at Technische Universität Wien specializing in databases and artificial intelligence. His research spans database theory, complexity theory, and logic in computer science, with a focus on XML processing, query optimization, and parallel data science techniques. Lead projects: KnowledgeGraph (2020–2028), HINT (2012–2017), Weblearn (2005–2008) Developed the Lixto visual extraction system and contributed to DLV knowledge representation framework Supervised T. Lukasser's diploma thesis on XPath query processing
Sebastian B Thrun is an Adjunct Professor in the Department of Computer Science at Stanford University. His research focuses on artificial intelligence, robotics, autonomous systems, and medical imaging. He holds positions at Stanford and has affiliations with Udacity, Inc., contributing to innovations in autonomous driving systems, drone technology, and AI-driven healthcare solutions. His work often bridges theoretical advancements with practical applications in transportation and medicine. **Education**: Details not explicitly provided in the text. Research Interests include developing algorithms for autonomous vehicles, deep learning applications in healthcare (e.g., skin cancer detection), and improving medical imaging techniques. He explores Bayesian methods, decision trees, and clustering algorithms to enhance AI systems' efficiency and adaptability. His articles reflect a focus on autonomous systems (e.g., multicopter design, vehicle navigation), healthcare AI (skin cancer classification), and algorithm optimization. Notable trends include leveraging AI for real-world challenges in transportation and medical diagnostics. Scientific Awards**: None explicitly mentioned in the provided text. Advising & Grants**: No student advisees or grant details listed here. His contributions extend to industry partnerships, such as those in autonomous vehicle development and online education platforms like Udacity. Labs/Teams**: Likely associated with Stanford’s AI Lab and Udacity initiatives, though specific lab affiliations are not detailed in the text.
Sebastian Maneth is a Heisenberg Professor at the Universität Bremen , affiliated with the Faculty of Mathematics and Computer Science and the Department of Computer Science . His research focuses on Databases , Automata Theory and Applications , and Data Compression , with significant contributions to tree transducers and XML processing. Editor of Theory of Computing Systems (TOCS) Associate Editor for Frontiers in Computer Science Editor of Algorithms His recent work explores the boundaries of tree transducers , regular expressions , and grammar-based compression , with decidable properties in formal language theory. He organizes major conferences like the Dagstuhl Seminar on Regular Expressions and serves on numerous program committees (CIAA, ICALP, FoIKS, etc.). Current projects include FO-Query Enumeration over compressed data, shape-preserving tree transducers , and user identification via eye-tracking data . His editorial and organizational roles highlight leadership in theoretical computer science communities.
