Find Academic Supervisors

• machine translation
• analogy
• multilingual alignment
• +7 more

Yves Lepage is a Professor at Waseda University's Faculty of Science and Engineering, specifically within the Graduate School of Information, Production, and Systems. He maintains an active research laboratory (lepage-lab.ips.waseda.ac.jp) and teaches courses including Example-based machine translation/NLP, Natural language processing, and Master's/Doctoral thesis supervision for the 2025 academic year. His research focuses on the application of analogical reasoning to natural language processing problems, particularly machine translation. Lepage's work spans formal analogy between strings, sentence-level analogies, morphological analysis, and multilingual systems. His research interests include machine translation, analogy, multilingual alignment, multilingual large language models, and foreign language aids. He has made significant contributions to understanding analogical density in corpora and developing methods to leverage analogies for translation, especially in low-resource scenarios. His publication record shows consistent output through 2024, with research evolving from foundational work on proportional analogy to sophisticated applications with neural networks. Recent work explores masked prompt learning for analogies, fuzzy analogies for translation, and organizing lexica into analogical grids for morphological generation across languages. Waseda University Teaching Award (Spring semester 2016) Lepage has successfully led multiple research projects funded by the Japan Society for the Promotion of Science, including "Theoretically founded algorithms for the automatic production of analogy tests in NLP" (2021-2024) and "Self-explainable and fast-to-train example-based machine translation using neural networks" (2018-2021). His work has involved international collaboration, including a 2023-2024 research period at the University of Montreal. He serves as Concurrent Researcher at the Waseda Research Institute for Science and Engineering (2024-2026) and has been active in professional organizations including the Information Processing Society of Japan and the Japanese Natural Language Processing Association.

• Computational Topology
• Geometry
• Linear Algebra
• +3 more

Claudia LANDI is a Full Professor at the University of Modena and Reggio Emilia, affiliated with the Department of Engineering Sciences and Methods. Her primary academic rank is Professor, focusing on research in computational topology, geometry, and their applications. She teaches courses such as Computational Topology, Geometry and Linear Algebra, and Additional Training Requirements for engineering programs. Her work integrates algebraic topology with data science, emphasizing topological data analysis (TDA) for biomedical and environmental applications. Research Interests: Dr. LANDI specializes in computational topology, with a focus on persistent homology, Reeb graphs, and discrete Morse theory. Her applied work spans proteomics, fibrosis treatment, and environmental safety assessments. She has contributed to developing algorithms for multi-parameter persistence and topological metrics, bridging theoretical mathematics with practical applications in imaging and machine learning. Teaching: She leads courses on computational topology for mathematics and engineering students, emphasizing practical skills in TDA libraries and visualization. Her geometry and linear algebra courses are foundational for management and mechatronic engineering programs, integrating theoretical rigor with problem-solving exercises. Education: Not explicitly detailed in provided texts. Labs/Teams: Engaged in interdisciplinary projects involving proteomic analysis and topological data science. Grants/Awards: None explicitly mentioned, though her extensive publication record suggests active research funding.

Gilbert BernsteinView profile

Assistant Professor

• Human-Centered Computing
• Interaction with the Physical World
• Software & Hardware Systems
• +10 more

Gilbert Bernstein is an Assistant Professor at the University of Washington in the Computer Science & Engineering department within the Paul G. Allen School of Computer Science & Engineering . He specializes in Computer Graphics and Programming Languages , with a focus on high-performance domain-specific languages (DSLs). Postdoctoral scholar at UC Berkeley and MIT with Jonathan Ragan-Kelley PhD from Stanford University under Pat Hanrahan His research spans Human-Centered Computing , Interaction with the Physical World , and Software & Hardware Systems , including projects in: Compiler design for knitting machines using knot theory Differentiable rendering of neural signed distance fields Hardware DSLs for accelerator programming Geometric pattern completion for quilting Responsive web retargeting His recent publications demonstrate expertise in combining Mathematical Modeling with Language Design for applications in Graphics , Simulation , and Fabrication .

Matt ClayView profile

Professor

• Geometric Group Theory
• Automorphisms of Free Groups
• Mapping Class Groups
• +3 more

Matthew T. Clay is a Professor and Chair of the Department of Mathematical Sciences at the University of Arkansas, Fayetteville. He holds a PhD in Mathematics from the University of Utah (2006) and has held academic positions at Allegheny College and the University of Oklahoma before joining the University of Arkansas in 2012. His research focuses on Geometric Group Theory , particularly on automorphisms of free groups, mapping class groups, and group actions on trees. He has organized conferences such as the Redbud Topology Conference and co-edited the book *Office Hours with a Geometric Group Theorist* (Princeton University Press, 2017). His work bridges geometric, algebraic, and dynamical perspectives in group theory. Key honors include NSF grants, a Simons Foundation Collaboration Grant, and the University of Utah’s Outstanding Graduate Student Award. He has advised three doctoral students and contributed to software tools for geometric group theory, such as *sage-train-track* and *bsscl*. Clay’s outreach includes organizing the G3 conference and co-hosting the *Running* section of his webpage, reflecting his passion for ultrarunning and its intersection with problem-solving resilience in mathematics.

Ramon Ferrer-i-CanchoView profile

Associate Professor

• Quantitative Linguistics
• Computational Linguistics
• Complexity Science
• +3 more

Ramon Ferrer-i-Cancho is an Associate Professor in the Department of Computer Science at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB). He leads the Complexity & Quantitative Linguistics Lab and is part of the LQMC research group (Quantitative, Mathematical, and Computational Linguistics). His research focuses on quantitative linguistics, mathematical models of language structure, and computational methods to analyze linguistic universals like Zipf’s law and syntactic dependency distances. Education: PhD in Computer Science from UPC (2003), with a thesis on 'Language: universals, principles and origins.' Editorial Roles: Section editor of Encyclopedia of Language and Linguistics (3rd edition), editorial board member of Glottometrics, Journal of Quantitative Linguistics, and Lingua. Research Interests: Quantitative analysis of syntactic structures, language evolution, cognitive constraints in grammar, and cross-species communication patterns (e.g., chimpanzees, dolphins). He applies principles from complexity science, information theory, and optimization to linguistic problems. Notable Achievements: Recipient of the Joan Lluís Vives Prize (2003) for popular science communication, Barcelona City Research Prize (2003), and UPC’s Best PhD Thesis award (2003-2004). Authored influential papers on dependency distance minimization and Zipfian laws, with over 200 indexed publications. Teaching & Mentorship: Advised Master’s theses on dolphin communication, syntactic dependency distances, and data mining applications. Active in the Master in Innovation and Research in Informatics (MIRI), clarifying academic requirements like SIRI seminar credits. Outreach: Authored popular science books, including Lingüística quantitativa i lleis lingüístiques , and featured in media like National Geographic and Scientific American for bridging linguistics with biology and physics.

Virginia Vassilevska WilliamsView profile

Professor

• Theoretical Computer Science
• Algorithms
• Computational Complexity
• +2 more

Virginia Vassilevska Williams is Professor of Computer Science and Artificial Intelligence + Decision-making at MIT EECS. Her research focuses on theoretical computer science with emphasis on algorithms, computational complexity, and graph theory. She has made significant contributions to matrix multiplication complexity and fine-grained hardness results. Recent publications explore fundamental problems in graph algorithms including cycle detection, shortest paths, and clique enumeration. Her work demonstrates consistent advancement in understanding computational limits for graph problems and matrix operations. Key research themes include: Breaking barriers in matrix multiplication exponents Establishing hardness thresholds for approximation algorithms Developing efficient graph traversal methods for sparse structures Her 2024 publications continue this trajectory with refinements to the laser method for matrix multiplication and improved clique listing techniques. The research consistently pushes boundaries in algorithm optimality proofs and computational complexity theory.

Francesco CauteruccioView profile

Assistant Professor

• Social and Complex Network Analysis
• Data Mining and Data Science
• Internet of Things (IoT)
• +3 more

Francesco Cauteruccio is a Tenure-Track Assistant Professor at the Department of Information Engineering, Electrical Engineering and Applied Mathematics (DIEM) at the University of Salerno, Italy. He is affiliated with the CORE and S 3 labs, focusing on interdisciplinary research at the intersection of social networks, IoT, and hybrid AI models. Ph.D. in Mathematics and Computer Science, University of Calabria (thesis: "Generalizing Identity-Based String Similarity Metrics: Theory and Applications") Italian National Scientific Qualification (ASN) for Associate Professor in Computer Science and Computer Engineering Visiting researcher at University of Klagenfurt (AI & Cybersecurity) and University of Lyon 1 (CERMEP/CREATIS) His research spans: Social & Complex Networks: Modeling cross-blockchain ecosystems, Reddit community dynamics, and sentiment propagation IoT & Hybrid Intelligence: Scope formalization, anomaly detection in MIoT networks, and air quality prediction systems Logic Programming: Integrating Answer Set Programming with machine learning for medical informatics and pattern mining Algorithm Design: Multi-Parameterized Edit Distance (MPED) for bioinformatics and network analysis Recent publications address network theory applications, hybrid reasoning frameworks, and high-utility pattern mining with formal constraint specification. He serves as guest editor for journals like Big Data and Cognitive Computing and Frontiers in Medical Engineering , and organizes international workshops on hybrid intelligence (HYDRA, VIPERC). Academic distinctions: Italian National Scientific Qualification for Associate Professor positions (2024) Guest Editor for 12+ special issues in data science and IoT journals Program/General Chair for 8 international workshops (2022-2024) His teaching portfolio includes courses on algorithms, distributed systems, and data analytics at University of Salerno, Polytechnic University of Marche, and University of Calabria.

Xin LiView profile

Associate Professor

• Theory of Computation
• Randomness Extractors
• Pseudorandomness
• +6 more

Xin Li is an Associate Professor in the Department of Computer Science at the Whiting School of Engineering, Johns Hopkins University. He is a member of the theory group and conducts research in the theory of computation, with a focus on randomness in computation, complexity theory, coding theory, and cryptography. He has advised several Ph.D. students and is actively involved in organizing seminars and serving on program committees of major theoretical computer science conferences. Ph.D., University of Texas at Austin, 2011 B.S. and M.S., Tsinghua University, Beijing, China His research interests lie broadly in theoretical computer science, particularly in pseudorandomness , randomness extractors , and explicit combinatorial constructions . He has made significant contributions to the construction of two-source extractors and non-malleable codes. His earlier work includes research in quantum computing and human-computer interaction. The most recent publications show a strong trend in coding theory (especially insertion-deletion codes), extractors (two-source, affine, non-malleable), and streaming algorithms. His work combines deep combinatorial insights with applications in cryptography and complexity theory. Scientific Awards and Honors: Simons Postdoctoral Fellowship NSF CAREER Award CCF-1845349 Invited to Theory of Computing Special Issue (RANDOM 2018) Invited to SICOMP Special Issue (FOCS 2013) SICOMP Special Issue (FOCS 2011) Advising and Grants: Xin Li has advised multiple Ph.D. students including Kuan Cheng, Zhengzhong Jin, Yu Zheng, Songtao Mao, and Yan Zhong. He has served as a postdoctoral mentor and research assistant supervisor. He is supported by several grants including NSF Award CCF-1617713, NSF CAREER Award CCF-1845349, and the Johns Hopkins Catalyst Award. He co-organizes the theory seminar at CS@JHU and has served on the program committees of STOC, RANDOM, and other top conferences. Labs and Teams: Xin Li is a member of the theory group at the Johns Hopkins Department of Computer Science, which focuses on foundational aspects of computation, algorithms, and complexity.

Marc SebbanView profile

Professor

• Machine Learning
• Domain Adaptation
• Metric Learning
• +5 more

Marc Sebban is a Professor in Computer Science at the Hubert Curien Laboratory (LabHC) and Deputy Director of this research unit. He leads the Inria project-team MALICE, focusing on machine learning, domain adaptation, and metric learning. Research Interests Metric learning with theoretical guarantees Domain adaptation via optimal transport Physics-informed neural networks Imbalanced data classification Tree-structured data similarity learning Recent Publications His 2025 work introduces provably accurate adaptive sampling for collocation points in PINNs and theoretically grounded quadrature methods using residual Hessians. 2024 publications explore physics-informed ML for laser-matter interaction, predictive modeling of body shape changes, and approximation error analysis in tanh neural networks. Earlier works address graph diffusion Wasserstein distances, metric learning for imbalanced data, and boosting algorithms with confidence oracles.

• Theoretical Computer Science
• Computational Complexity
• Data Structures
• +3 more

Prof. Michal Koucký is a faculty member at the Institute of Informatics, Faculty of Mathematics and Physics, Charles University in Prague. His research focuses on theoretical computer science, particularly computational complexity, data structures, algorithms, and combinatorics. He leads significant research projects including EPAC (supported by GA CR) and LBCAD (funded by ERC). His educational background includes a Ph.D. from Rutgers University (2003) and a Master's degree from Charles University (1998). Prof. Koucký's research spans multiple areas of theoretical computer science with emphasis on computational complexity , data structures , algorithms , and combinatorics . His work explores fundamental questions about computational efficiency, lower bounds, and algorithmic techniques. He has made significant contributions to edit distance algorithms, string processing, and circuit complexity. His research often bridges theoretical foundations with practical algorithmic applications, particularly in areas like dynamic data structures and approximation algorithms. An analysis of his recent publications (2019-2024) reveals a strong focus on string algorithms, particularly edit distance problems. His work develops novel approximation techniques, sketching methods, and sublinear-time algorithms for pattern matching. There's also significant work on data structure lower bounds and connections to popular complexity conjectures. His research demonstrates a consistent pattern of tackling fundamental problems with innovative approaches that push theoretical boundaries while maintaining practical relevance. Antonin Svoboda award for best dissertation in 2014 (awarded to Jan Bulánek) Best paper award at SAGT 2016 for 'The Big Match in Small Space' Best paper award at FOCS 2018 for 'Approximating Edit Distance Within Constant Factor in Truly Sub-Quadratic Time' He has successfully advised numerous graduate students including Pavel Dvořák, Karel Král, Debarati Das, and Jan Bulánek who have gone on to positions at prestigious institutions like Bristol, Google (Zurich), and Penn State. His research is supported by significant grants including the EPAC project (GA CR) and LBCAD project (ERC), demonstrating the impact and relevance of his work in theoretical computer science. Prof. Koucký is an active member of the theoretical computer science community, regularly presenting at major conferences including STOC, FOCS, ICALP, and SODA. His work often involves collaborations with researchers from institutions worldwide, contributing to a vibrant research environment at Charles University's Institute of Informatics.

Adam Teodor PolakView profile

Assistant Professor

• Algorithms
• Fine-Grained Complexity
• Learning-Augmented Algorithms
• +3 more

Adam Teodor Polak serves as an Assistant Professor in the Department of Computing Sciences at Bocconi University, where his research centers on theoretical algorithms with dual emphases on fine-grained complexity and learning-augmented algorithms. His work investigates fundamental questions about computational hardness while developing prediction-enhanced algorithms that maintain worst-case guarantees. Polak earned his PhD from Jagiellonian University in 2019 under Paweł Idziak, including a research visit at MIT with Virginia Vassilevska Williams. He subsequently held postdoctoral positions at the Max Planck Institute for Informatics and EPFL before joining Bocconi. His research program addresses why computational problems resist efficient solutions and how imperfect predictions can robustly improve algorithmic performance. This manifests in two interconnected streams: establishing conditional lower bounds for problems like 3SUM and Orthogonal Vectors, and designing learning-augmented frameworks for dynamic graph problems, caching, and optimization that blend theoretical rigor with practical machine learning insights. Recent publications reveal accelerating momentum in algorithms with predictions, with over half of his 2023-2025 output appearing in top ML venues (ICML, NeurIPS, ICLR) alongside traditional theory conferences (STOC, SODA). This cross-pollination demonstrates how worst-case theoretical guarantees can coexist with data-driven performance gains across graph algorithms, scheduling, and combinatorial optimization. Scientific recognition includes: Best Paper Award at ESA 2024 for knapsack algorithm breakthroughs Bronze Medal at ACM ICPC World Finals (2011) 2nd Place in PACE 2018 Challenge for Steiner tree algorithms Polak actively shapes the field through program committee service (ESA, ICALP, SOSA) and community building, notably co-organizing the 2022 Workshop on Algorithms with Predictions (ALPS) and decade-long high-school algorithmics workshops. His industry collaborations with Teroplan and Google demonstrate real-world impact in route planning and distributed systems. Current teaching includes graduate Algorithms courses at Bocconi, while his experimental work on GPU-accelerated graph algorithms and medical computer vision continues to bridge theoretical insights with practical implementation challenges.

Konstantinos FokianosView profile

Professor

• Time Series Analysis
• High-Dimensional Statistics
• Integer-Valued Time Series
• +2 more

Konstantinos Fokianos is a Professor of Statistics at the University of Cyprus. He completed undergraduate studies in Mathematics at the University of Ioannina and earned his M.A. (1993) and Ph.D. (1996) in Statistics from the University of Maryland, USA. His academic career includes positions at Ohio State University (Visiting Assistant Professor), Lancaster University (2018-2020), and the University of Cyprus where he advanced from Lecturer to full Professor. Professor Fokianos specializes in time series analysis with focus areas including integer-valued time series models, high-dimensional time series, spectral analysis, semiparametric modeling, and isotonic estimation. His methodological research encompasses network autoregression, multivariate count time series, causal inference for health questionnaires, and clustering techniques. Applied work includes epidemiological network analysis, medical prognostics for leukemia, and tourism competition modeling. He has held visiting positions at Ludwig Maximilians University, National Institutes of Health, EPFL, and other institutions. Elected member of the International Statistical Institute since 2005, he serves on editorial boards of Journal of Time Series Analysis and Statistical Modeling, and edits Springer's 'Frontiers in Probability and Statistical Sciences' series.

Darren NarayanView profile

Professor

• Graph Theory
• Combinatorics
• Social Networks
• +3 more

Darren Narayan is a Professor of Mathematics in the School of Mathematics and Statistics at Rochester Institute of Technology (RIT), part of the College of Science. He holds a BS from the State University of New York at Binghamton and MS/PhD degrees from Lehigh University. His research focuses on Graph Theory , Combinatorics , Social Networks , and Functional Connectivity of the Brain , with applications to real-world systems like brain networks and transportation infrastructure. Narayan has authored numerous publications analyzing graph metrics for social and neural networks, including studies on helmet impact effects and network efficiency in subway systems. He leads NSF-funded research initiatives, such as the STEM Real World Applications of Mathematics Project , and has been honored by the Mathematical Association of America. He teaches courses like Graph Theory, Combinatorics, and Discrete Mathematics, emphasizing practical applications in STEM fields. His work integrates mathematical modeling to address challenges in neuroscience, sports safety, and network optimization, often collaborating with students on interdisciplinary projects. He also serves as Director of Undergraduate Research at RIT, fostering student engagement in advanced mathematical research.

Debashis PaulView profile

Professor

• Multivariate statistics
• Random matrix theory
• Functional data analysis
• +4 more

Debashis Paul is a Professor in the Department of Statistics at the University of California, Davis. His research focuses on high-dimensional statistics, random matrix theory, functional data analysis, and their applications in neuroimaging and spatial statistics. He has contributed to methodologies for spectral analysis, covariance modeling, and nonparametric estimation in complex datasets. His work spans theoretical developments in multivariate analysis and practical applications in fields such as medical imaging and genomics. Recent projects include modeling fiber orientation in diffusion MRI, analyzing high-dimensional genomic data, and studying pandemic dynamics through statistical frameworks. Key research themes include: High-dimensional time series analysis Random matrix theory applications Functional data smoothing techniques Non-Gaussian spatial field modeling Covariance structure inference His recent publications highlight advancements in spectral estimation, latent graph inference, and meta-learning frameworks in high-dimensional settings. He has also contributed to methodological improvements in GWAS analysis and nonautonomous dynamical systems modeling.

• Graph Matching
• Combinatorial Optimization
• Document Analysis
• +3 more

Romain Raveaux is an Assistant Professor at the University of Tours' Computer Science Laboratory (LIFAT). His research intersects machine learning, operations research, and graph theory for environmental monitoring and document analysis. Applications include insect counting for biodiversity assessment and document image quality evaluation. Raveaux holds a PhD from the University of La Rochelle and has authored over 40 publications. His methodologies combine combinatorial optimization with structural pattern recognition, addressing problems like graph matching and edit distance computation. Research Highlights Graph-based representations for pattern recognition Mixed-integer programming for graph matching Machine learning models for insect classification Document image analysis frameworks