جست‌وجوی استاد و پژوهشگر

Wojciech Rytterمشاهده پروفایل

استاد

• Design and analysis of computer algorithms
• Automata and formal languages
• Parallel algorithms
• +۵ مورد دیگر

Wojciech Rytter is a full professor at the Institute of Informatics, Department of Mathematics and Informatics at the University of Warsaw, Poland, holding this position continuously since October 1971. His academic career includes significant international appointments as full professor at New Jersey Institute of Technology (2002-2004), Liverpool University (1997-2002), and Bonn University (1994-1995), and as visiting professor at University of California, Riverside (1992-1993) and University of Warwick (1985-1986). He earned his MSc in 1971, PhD in 1975, habilitation in 1985, and was awarded the scientific degree of professor in 1997, all from Warsaw University. Professor Rytter's research focuses on the design and analysis of computer algorithms, with particular expertise in automata and formal languages, parallel algorithms, and text algorithms. His work spans efficient sequential and parallel algorithms, automata theory, complexity of recognition and parsing of context-free languages, pattern matching, algorithmics of WWW, parallel combinatorial computing, graph-theoretic algorithms, and algorithmics of highly compressible objects. His theoretical contributions have practical applications in computational biology, bioinformatics, and text processing systems. His recent publications (2022-2025) demonstrate continued activity in string algorithms, particularly in pattern matching, string covers, and combinatorics on words, with a strong focus on theoretical computer science with applications in bioinformatics. 200 problems on automata, languages, computations (Cambridge University Press 2023) 125 Problems in Text Algorithms (Cambridge University Press, 2021) Jewels of Stringology (World Scientific, 2002) Fast parallel algorithms for matching problems in graphs (Oxford University Press 1998) Text algorithms (Oxford University Press 1994) Professor Rytter has collaborated extensively with researchers including Jakub Radoszewski, Tomasz Walen, Tomasz Kociumaka, and Maxime Crochemore. He is a member of the Academy of Europe (elected 2011, Informatics section) and has authored or co-authored more than 130 publications. He maintains an active research laboratory focused on string algorithms and combinatorics on words at the University of Warsaw.

Jonathan A. Kelnerمشاهده پروفایل

استاد

• Algorithms
• Spectral Graph Theory
• Combinatorial Optimization
• +۷ مورد دیگر

Jonathan A. Kelner is a Professor of Applied Mathematics in the Department of Mathematics at the Massachusetts Institute of Technology (MIT) and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on applying techniques from pure mathematics to solve fundamental problems in algorithms and complexity theory, with the goal of developing practical algorithms for real-world questions. Dr. Kelner received his undergraduate degree from Harvard University and his Ph.D. in Computer Science from MIT in 2006. Before joining the MIT faculty, he spent a year as a member of the Institute for Advanced Study. His educational background has provided a strong foundation for his interdisciplinary research spanning mathematics and computer science. His research interests include combinatorial optimization, mathematical programming, spectral graph theory, distributed computing, machine learning, computational geometry and topology, computational biology, signal processing, and random matrix theory. Kelner's work demonstrates how deep theoretical insights can lead to practical algorithmic improvements, particularly in graph algorithms and optimization problems. His approach often involves connecting seemingly disparate areas of mathematics to create novel algorithmic techniques. Analysis of his recent publications reveals a strong focus on spectral graph theory, optimization algorithms, and the Sum-of-Squares method. His work frequently addresses fundamental questions in theoretical computer science with practical implications for algorithm design. A notable trend in his research is the development of nearly-linear-time algorithms for various graph problems, which represents significant improvements over previous approaches. NSF CAREER Award Alfred P. Sloan Research Fellowship NEC Award for Research in Computers and Communication Sprowls Doctoral Dissertation Award Best Student Paper Award at STOC 2004 Best Paper Award at STOC 2011 Kokusai Denshin Denwa Junior Faculty Chair 2008 Harold E. Edgerton Faculty Achievement Award 2011 School of Science Award for Excellence in Undergraduate Education 2012 Professor Kelner has been actively involved in mentoring students and has received recognition for his teaching excellence, including the School of Science Award for Excellence in Undergraduate Education in 2012. His research has been supported by prestigious grants including the NSF CAREER Award. He has collaborated extensively with researchers across multiple institutions, often working with other leading figures in theoretical computer science to produce groundbreaking results in algorithm design. At MIT, Kelner is part of both the Mathematics Department and CSAIL, positioning him at the intersection of theoretical mathematics and practical computer science. This dual affiliation reflects the interdisciplinary nature of his work, which bridges pure mathematical theory with concrete algorithmic applications.

Yihan Sunمشاهده پروفایل

استادیار

• Parallel Computing
• Algorithms
• Data Structures
• +۱۲ مورد دیگر

Yihan Sun is an Assistant Professor at the University of California, Riverside (UCR) since January 2020. He earned his Ph.D. in Computer Science from Carnegie Mellon University (CMU) , advised by Guy Blelloch , and holds a Bachelor's degree in Computer Science from Tsinghua University . Research Interests: Yihan Sun focuses on the theory and practice of parallel computing , including Parallel algorithms and data structures Write-efficient algorithms for Non-Volatile Memory (NVM) Computational geometry (range trees, Delaunay triangulations) Graph algorithms (SSSP, SCC, cluster-based BFS) Concurrent and persistent data structures Multi-version concurrency control (MVCC) with garbage collection Applications in databases, transactional systems, and computational biology Recent Research Trends: His work on join-based parallel balanced trees has been foundational, supporting four balancing schemes (AVL, red-black, weight-balanced, treaps) and enabling efficient implementations in graph analytics, spatial queries, and dynamic programming. Recent publications focus on output-sensitive algorithms , scalable graph libraries (PASGAL) , and pedagogical approaches to teaching parallel algorithms. Teaching: He teaches CS260 (Parallel Algorithms) at UCR and has served as a guest lecturer for MIT 6.886 (Algorithm Engineering) and CMU 15-859 (Algorithms in the real world) . He also contributed to algorithm education through a tutorial at the ACM Symposium on Principles and Practice of Parallel Programming (PPoPP 2019) . Labs & Collaborations: Yihan is a core contributor to the PAM (Parallel Augmented Maps) library, which has been integrated into systems like Aspen (graph-streaming) and C-trees . He collaborates with teams at CMU-Parlay , PBBS , and Ligra , with his code available on Github for community feedback.

• Clustering
• Edit Distance
• Approximation Algorithms
• +۹ مورد دیگر

Debarati Das is an Assistant Professor in Computer Science and Engineering , specializing in Clustering Algorithms , Edit Distance , and Approximation Algorithms . Her research focuses on theoretical computer science, particularly in algorithm design for data streams, permutation clustering, and sequence alignment. Grants: NSF CAREER Award (2024), NSF Student Travel Grant (2023). Her work includes breakthroughs in consensus clustering, achieving sub-2-approximation, and developing space-efficient algorithms for edit distance in distributed models. Recent projects explore dynamic shortest paths in planar graphs and pseudorandomness extraction. Her publications span journals like the Journal of the ACM and conferences such as SODA, STOC, and FOCS. Collaborations include researchers from institutions in the U.S. and Europe, with applications in computational biology and parallel computing.

Francesca Ievaمشاهده پروفایل

دانشیار

• Biostatistics
• Health Data Science
• Statistical Learning
• +۵ مورد دیگر

Francesca Ieva serves as Associate Head of the Health Data Science Centre at Human Technopole and Associate Professor of Statistics at the Politecnico di Milano, where she leads the MOX - Modeling and Scientific Computing laboratory within the Department of Mathematics. She also co-leads the Di Angelantonio & Ieva Group, a collaborative research team focused on integrating molecular data with clinical information to advance precision medicine. Dr. Ieva received her PhD in Mathematical Models and Methods for Engineering in 2012. Her academic journey has positioned her at the forefront of health data science, bridging statistical methodology with biomedical applications. Her research focuses on statistical learning in biomedical sciences, with particular emphasis on developing advanced models for integrating complex clinical data to inform predictions in clinical decision-making. Francesca's work spans multiple domains including: Development of novel survival analysis techniques for healthcare applications Integration of DNA methylation data for cardiovascular risk prediction Application of machine learning to clinical pathway analysis in mental health Innovative approaches to polygenic risk scoring incorporating SNP interactions Development of radiomics models for cancer prognosis and treatment response Analysis of Dr. Ieva's recent publications reveals a strong trend toward federated learning approaches in healthcare data analysis, reflecting growing concerns about data privacy while maintaining analytical power. Her work increasingly integrates multiple data types (genomic, epigenetic, imaging, and clinical records) to create more comprehensive patient profiles for precision medicine applications. A notable pattern is her focus on translating complex statistical models into clinically actionable tools that can directly inform patient care and healthcare policy decisions. As Associate Head of the Health Data Science Centre at Human Technopole, Dr. Ieva oversees a research group comprising epidemiologists, statisticians, and data scientists working collaboratively to bridge the gap between genotype and phenotype. The Di Angelantonio & Ieva Group develops innovative studies that integrate biomolecular data with medical records, imaging, and portable medical device data. Her team utilizes both existing healthcare data and newly generated population-based studies, applying novel analytical methods that integrate clinical epidemiology with health research to improve data interpretation. Dr. Ieva actively mentors several PhD students including Andrea Lampis, Katherine Marie Logan, Alessia Mapelli, Michela Carlotta Massi, and Andrea Mario Vergani. Her research has been supported by collaborations with major healthcare institutions and likely receives funding from research councils and health technology initiatives, though specific grant details are not provided in the available information.

Ioan Todincaمشاهده پروفایل

استاد

• Graph Algorithms
• Theoretical Computer Science
• Exact Algorithms
• +۶ مورد دیگر

Ioan Todinca is a Professor of Computer Science at the University of Orléans, France, affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) research laboratory. His academic career spans over two decades, with significant contributions to theoretical computer science, particularly in graph algorithms and distributed computing. Faculty of Science, University of Orléans LIFO Research Laboratory Member of Institut thématique pluridisciplinaire Modélisation, Systèmes, Langages (since 2014) Former director of MIPTIS doctoral school (2012-2014) Former head of Computer Science degree program (2007-2011) Former leader of LIFO Graphs, Algorithms and Computational Models team (2008-2012) Todinca's research focuses primarily on graph algorithms, with expertise in exact algorithms (moderately exponential), parameterized algorithms, and algorithms for specific graph classes. He has made significant contributions to techniques involving tree decompositions, treewidth, minimal separators, and potential maximal cliques. More recently, his work has expanded into distributed algorithms, especially in communication-constrained models like the broadcast congested clique. His research bridges theoretical foundations with practical algorithmic approaches for NP-hard problems. The analysis of his recent publications reveals a strong trend toward distributed computing problems, particularly in congested network models. His work spans from fundamental graph theory problems (cycle detection, graph modification) to applications in quantum computing and model checking. The consistent focus on communication complexity, verification, and efficient algorithms across different computational models demonstrates his ability to adapt theoretical computer science principles to emerging computational paradigms. Todinca has supervised numerous PhD students and has been actively involved in the theoretical computer science community through conference organization and editorial work. His publications appear consistently in top-tier venues including SIAM Journal on Computing, Algorithmica, and proceedings of major conferences like STACS, WG, and DISC. Teaching responsibilities include algorithms, graph theory, and discrete structures for undergraduate and graduate students, with previous experience teaching databases, programming, and software engineering. His educational materials are hosted on the university's Celene platform.

Christian Blouinمشاهده پروفایل

استاد

• Bioinformatics
• Algorithms
• Phylogenetics
• +۵ مورد دیگر

Christian Blouin is a Professor and Associate Dean, Academic in the Faculty of Computer Science at Dalhousie University. His interdisciplinary research bridges computer science and molecular biology, with a strong focus on bioinformatics and computational biophysics. Education: Ph.D. in Computer Science, Dalhousie University (2001) B.Sc. in Computer Science, Université Laval (1997) His research interests lie at the intersection of algorithms, phylogenetics, protein evolution, and molecular modeling. He develops computational methods to analyze protein structure evolution, multiple sequence alignments, and phylogenetic tree reconstruction. His work integrates high-performance computing and statistical mechanics to model biophysical properties of proteins, particularly in conformational dynamics and electrostatic interactions. The most recent publications reveal a consistent trend in developing algorithmic solutions for biological problems—especially in text mining for biological events, phylogenetic distance computation, and 3D mapping of evolutionary data. His work emphasizes automation, accuracy, and scalability in bioinformatics pipelines. Scientific Awards and Honors: TULA Fellow Dr. Blouin has secured significant research funding from NSERC, the TULA Foundation, and the CFI. His research group has contributed to tools like GenGIS for geospatial genomics and libcov for bioinformatics programming. He has advised students such as Haibin Liu and Vlado Keselj, who have co-authored key publications in text mining and phylogenetics. His lab integrates algorithm development with biological validation, aiming to bridge computational innovation with real-world biological insights.

Benjamin Raichelمشاهده پروفایل

دانشیار

• Computational Geometry
• Algorithms
• Theoretical Computer Science
• +۳ مورد دیگر

Benjamin Raichel is an Associate Professor in the Department of Computer Science at the University of Texas at Dallas, affiliated with the Erik Jonsson School of Engineering and Computer Science. His research focuses on computational geometry and geometric approximation algorithms, with contributions to areas such as Voronoi diagrams, Fréchet distance, clustering, and algorithmic efficiency. Dr. Raichel holds a PhD from the University of Illinois Urbana-Champaign under the supervision of Sariel Har-Peled. He currently teaches Computational Geometry (CS 6319) and has advised multiple PhD students, including Md. Billal Hossain and Jonathan James Perry. His work is supported by NSF grants such as 'Shape Matching in a Messy World Using Fréchet Distance' and 'Metric Violation Distance: Hardness and Approximation.' He is a member of the Algorithms and Theory Group at UT Dallas.

• Theoretical Computer Science
• Logic in Computer Science
• Algorithms
• +۷ مورد دیگر

Alexandre Vigny is a junior professor at University Clermont Auvergne, France, where he conducts research at the intersection of logic, algorithms, and graph theory. His work focuses on theoretical computer science, particularly in model checking, query enumeration, and distributed algorithms on sparse graph classes. Research Interests: Theoretical Computer Science Logic in Computer Science Parameterized and Distributed Algorithms Graph Theory and Structural Sparsity Database Query Evaluation Reconfiguration Problems His recent publications explore algorithmic meta-theorems, first-order logic with connectivity, elimination distance, and distributed domination, primarily on sparse and structurally constrained graphs. His work appears in top venues such as LICS, ICALP, PODS, and JACM. Scientific Awards: No awards explicitly mentioned. Advising and Grants: Co-supervising Jona Dirks, PhD student since October 2024, with Mamadou Kanté. No specific grants mentioned, but active in collaborative research with prominent figures like Sebastian Siebertz, Luc Segoufin, and Patrice Ossona de Mendez. Labs and Teams: Previously part of Sebastian Siebertz’s team at the University of Bremen. Collaborates with researchers across Europe, including in Warsaw and Paris. Involved in the theoretical computer science community, co-organizing the PODC-DARE workshop.

Bernardo Magniniمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Human-Centered AI
• Multilinguality
• +۲ مورد دیگر

Bernardo Magnini is a researcher at Fondazione Bruno Kessler (FBK) in Trento, Italy, specializing in Natural Language Processing and Human-Centered AI. He contributes to the Humane AI project (Horizon 2020 grant 952026), focusing on multilingual AI systems that enable natural human-machine interaction while preserving European linguistic diversity. His core research spans Natural Language Processing with emphasis on textual entailment, language modeling, and cross-lingual technologies. Magnini advocates for AI that supports all European languages regardless of speaker population, stressing language as essential for cultural preservation. Key contributions include the IRSTLM language modeling toolkit and foundational work on tree-edit-distance algorithms for semantic inference. Magnini's publication trends reveal sustained focus on enabling machines to understand human language through multilingual frameworks, with early work in textual entailment evolving into broader human-centered AI applications. Scientific recognition includes: META Prize META Seal of Recognition No student advisement details are provided, but his leadership in Horizon 2020 projects like Humane AI indicates substantial grant funding for multilingual AI research. He collaborates within FBK's Natural Language Processing group on tools bridging linguistic theory and practical AI systems. Magnini operates within FBK's NLP research unit, developing language resources and technologies for European language preservation, with ongoing work targeting conversational AI systems that transcend domain-specific limitations.

• Machine Learning
• Computer Vision
• Image Analysis
• +۳ مورد دیگر

Prof. Jean-Yves RAMEL is a faculty member at Savoie Mont Blanc University's Department of Computer Science (LISTIC laboratory). Previously, he served as a Professor at Polytech’Tours (2007–2023) and held roles at INSA Lyon (1997–2002). His research focuses on machine learning on graphs, medical and document image analysis, and interactive learning systems. He has published extensively in journals like Pattern Recognition and Medical Image Analysis . His work spans medical imaging segmentation, graph-based algorithms, and historical document processing. Notably, he contributed to projects like BVH (Bibliothèques Virtuelles Humanistes) and developed methods for graph edit distance and dynamic classification. He maintains active collaborations in digital humanities and has supervised numerous projects in computer science education. Education: PhD in Computer Science (1996), INSA Lyon: 'Interpretation automatique de documents graphiques' Maitre de conférences positions at INSA Lyon (1997–2002) and Polytech’Tours (2002–2023) Research Interests: Machine Learning on Graphs: Graph neural networks, graph embedding, and graph matching algorithms. Image Analysis: Medical imaging segmentation, historical document processing, and 3D texture analysis. Interactive Learning Systems: Dynamic classification, anytime algorithms, and one-class classifiers. Publications: Over 140 publications, focusing on graph-based methods, medical imaging, and document analysis. Recent work includes advancements in CNN/GNN pipelines for brain imaging and grammar-reinforcement learning for graphs (2025). Grants & Projects: Led the LIFAT lab (2018–2021) and contributed to the BVH digital humanities program. Active in collaborative projects involving medical imaging segmentation and historical document digitization. Labs/Teams: LISTIC laboratory (Savoie Mont Blanc University) and prior roles in LIFAT (Tours) and INSA Lyon's Interaction Collaborative lab.

Nikolaus Augstenمشاهده پروفایل

استاد

• Database Systems
• Data-Centric Applications
• Approximate Matching
• +۴ مورد دیگر

Prof. Nikolaus Augsten is a Professor and Head of the Database Research Group at the Department of Computer Science, University of Salzburg. He previously held positions at the Free University of Bozen-Bolzano, Italy, and visited Technische Universität München (TUM) and Washington State University. His research focuses on data-centric applications, approximate matching techniques for complex data structures, and efficient index structures for similarity search. Education: PhD from Aalborg University (Denmark, 2008), supervised by Prof. Michael Böhlen. Master's from Adam Mickiewicz University (Poland). Research interests include hierarchical data similarity (e.g., tree structures), scalable algorithms for tree edit distance, and applications in e-government and XML search engines. His work emphasizes practical solutions for tree-to-tree matching and similarity joins. Selected Awards: ICDE 2010 Best Paper Award for TASM: Top-k Approximate Subtree Matching Advising includes PhD student Mateusz Pawlik (thesis: Efficient Computation of the Tree Edit Distance). His group develops open-source tools like the RTED algorithm and approximate tree matching libraries. Labs/Teams: Database Research Group at University of Salzburg, collaborating on projects like Tree Edit Distance reference implementations and set similarity join algorithms.

Jiří Kosinkaمشاهده پروفایل

دانشیار

• Computer Graphics
• Geometric Modelling
• Computer Aided Design
• +۳ مورد دیگر

Jiří Kosinka is an Associate Professor (Tenure Track) at the University of Groningen, affiliated with the Faculty of Science and Engineering and the Bernoulli Institute. He leads the Scientific Visualization and Computer Graphics research group. His roles include coordinating and lecturing in Computer Graphics and Advanced Computer Graphics courses, as well as serving as an editor for journals like Computer-Aided Design and Graphical Models . Academic Position: Associate Professor, Tenure Track Affiliations: Bernoulli Institute, Faculty of Science and Engineering Research Group: Scientific Visualization and Computer Graphics Education PhD in Mathematics (2006), Charles University, Prague MSc in Mathematics (2002), Charles University, Prague Research Interests Kosinka's work focuses on geometric modeling, computer graphics, and image processing. He develops algorithms for subdivision surfaces, numerical quadrature, and fluid simulation, with applications in surgical planning and medical visualization. His research bridges theoretical contributions with practical implementations in CAD systems and real-time rendering. Conference Contributions Co-organizer of DGMM 2025 (Discrete Geometry and Mathematical Morphology) in Groningen Program Chair for AniNex 2022/2023 (Next Generation Computer Animation) IPC member for SGP, SPM, Pacific Graphics, and other key conferences Editorial Roles He serves on the editorial boards of Computer-Aided Design and Graphical Models , and has guest-edited special issues in Computer Aided Geometric Design . Labs & Teams He leads the Scientific Visualization and Computer Graphics group, collaborating on projects like BoneStory (3D surgical planning) and fluid dynamics simulations. His lab focuses on advancing geometric algorithms and their real-world applications.

• Computer Science
• Mathematical Morphology
• Data Visualization
• +۴ مورد دیگر

Jos Roerdink is a Professor at the Bernoulli Institute within the Faculty of Science and Engineering at the University of Groningen , Netherlands. His research spans multiple disciplines including computer science, mathematical morphology, data visualization, and neuroscience, with a focus on algorithm development and applications in medical imaging and astronomical data analysis.

• 3D Shape Analysis
• Graph-Based Machine Learning
• Computer Vision
• +۴ مورد دیگر

Andrea Torsello is a researcher at the University of York, specializing in 3D shape analysis, graph-based machine learning, and quantum computing applications in computer vision. His work bridges theoretical and applied domains, focusing on pattern recognition, network thermodynamics, and remote sensing. PhD from University of York (2004) Published extensively in journals like IEEE Transactions and Pattern Recognition Research interests include: Quantum-inspired graph analysis 3D reconstruction techniques Manifold learning for complex networks Thermodynamic modeling of time-evolving systems Key contributions involve: Quantum walk-based graph similarity measures k-Anonymity for graph data Physics-driven CNN models for ocean wave reconstruction Game-theoretic approaches to shape matching