Complexity of Graph Editing Problems Professors and Supervisors

Fedor Fominمشاهده پروفایل

استاد

University of Michigan-Ann ArborUnited States

Fedor Fomin is a Professor at the Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Bergen. He is renowned for his contributions to theoretical computer science, particularly in parameterized complexity and exact exponential algorithms, earning him the ACM Fellow 2023 distinction. University: University of Bergen School: Faculty of Mathematics and Natural Sciences Department: Department of Informatics Academic Rank: Professor Research Interests: Fomin's work focuses on designing efficient algorithms for computationally hard problems, with a specialization in parameterized and exact exponential algorithms. His research spans graph theory, combinatorial optimization, and computational complexity, addressing foundational challenges in sparse graphs, planar graphs, and treewidth-based techniques. Key Contributions: His research includes kernelization methods, subexponential algorithms for planar graphs, and novel approaches to edge domination and satisfiability problems. He has published extensively in top venues like STOC, FOCS, and SODA. Awards: ACM Fellow 2023 EATCS Award 2019 ERC Advanced Grant 2016 Nordic Researcher Award in Theoretical Computer Science 2010 Publications: His work covers parameterized algorithms for cluster editing, feedback vertex sets, and induced subgraph problems, with applications in computational biology and network science. Collaborations: Fomin collaborates with leading researchers in theoretical computer science, including Petr Golovach and Saket Saurabh, mentoring numerous PhD students and shaping the field's future.

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Romain Raveauxمشاهده پروفایل

استادیار

University of La RochelleFrance

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

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Thomas Baarمشاهده پروفایل

استاد

Berlin University of Applied SciencesGermany

Prof. Dr. Thomas Baar is a Professor at HTW Berlin, specializing in cyber-physical systems, formal verification, and model-driven engineering. His research focuses on hybrid systems, automated driving platforms like CeCar, and improving usability of verification tools such as KeYmaera. He contributes to software engineering education and legacy system modernization through domain-specific languages (DSLs) and graphical editors. Baar's work spans academia and industry, with collaborations in aerospace safety, multiagent systems, and cybersecurity for control systems. His research interests include formal methods for hybrid systems, software reliability, and educational platforms for autonomous systems. He has published extensively on topics like verification frameworks, syntax improvements for formal tools, and safety analysis methodologies. His interdisciplinary approach bridges theoretical computer science with practical applications in aerospace, automotive, and industrial automation. Key contributions include the CeCar platform for autonomous driving research, modularization enhancements for KeYmaera, and DSL-based solutions for legacy software maintenance. Baar collaborates with institutions like the IFAF Berlin and participates in EU-funded projects focusing on smart cities and sustainable technologies. His publications reflect a strong emphasis on verification techniques, with over 15 peer-reviewed articles since 2015. He is affiliated with the HTW Berlin's Wilhelminenhof Campus and actively engages in knowledge transfer initiatives to bridge research and industry needs.

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Renjie Liaoمشاهده پروفایل

استادیار

University of British ColumbiaCanada

Renjie Liao is an Assistant Professor (tenure-track) in the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC), with an associated appointment in the Department of Computer Science. He is also a Faculty Member at the Vector Institute and a Canada CIFAR AI Chair. Prior to UBC, Dr. Liao was a Visiting Faculty Researcher at Google Brain and held a Senior Research Scientist position at Uber Advanced Technologies Group during his PhD. He earned his B.Eng. (Automation) from Beihang University, M.Phil. (CS) from the Chinese University of Hong Kong, and PhD (CS) from the University of Toronto. His research focuses on probabilistic and geometric deep learning , with key contributions in deep generative models, geometric deep learning, neural algorithmic reasoning, and generalization bounds. Notable areas include 3D point cloud analysis, self-driving systems, and healthcare applications using graph neural networks. His work bridges theoretical foundations (e.g., PAC-Bayes bounds) with practical applications like motion forecasting and medical imaging. Education: B.Eng. in Automation, Beihang University (2011) M.Phil. in Computer Science, CUHK (2015) PhD in Computer Science, UofT (2021) Dr. Liao has received awards such as the RBC Graduate Fellowship and Connaught International Scholarship. His lab (Deep Structured Learning Lab) emphasizes principled mathematical approaches to solving complex problems. He advises students in machine learning, computer vision, and robotics, encouraging applications from those with strong coding/mathematical backgrounds. Labs/Teams: Deep Structured Learning Lab (UBC) Vector Institute Collaboration

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Jia Liuمشاهده پروفایل

استاد

University of West FloridaUnited States

Dr. Jia Liu is a Professor and Chair of the Department of Mathematics and Statistics at the University of West Florida, within the Hal Marcus College of Science and Engineering. She has been a key academic figure at UWF since 2006, leading both research and departmental initiatives in computational and applied mathematics. Her educational background includes a Ph.D. in Mathematics from Emory University, funded by the National Science Foundation, focusing on preconditioned Krylov subspace methods for incompressible flow problems. She earned her M.S. and B.A. in Mathematics from Central China Normal University, where her bachelor's thesis received the highest honor. Dr. Liu's research lies at the intersection of numerical linear algebra, scientific computing, and interdisciplinary applications. Her primary interests include: Numerical solvers for large sparse linear systems Krylov subspace iterative methods Preconditioning techniques Applications to Navier-Stokes and optimization problems Geometric and topological analysis of ellipsoids Complex networks and community detection via spectral clustering Machine learning for disease prediction and biological modeling The trends in her publications reflect a consistent focus on robust numerical algorithms with applications across fluid dynamics, network science, and biomedical modeling. Her work emphasizes both theoretical development and practical implementation in high-performance computing environments. Dr. Liu has served as an editor and editorial board member for several peer-reviewed journals and regularly reviews submissions. While specific awards are not listed, her sustained publication record in prestigious venues such as SIAM Journal on Scientific Computing and Journal of Biological Dynamics underscores her scholarly impact. As department chair and professor, she plays a central role in academic advising, curriculum development, and research mentorship. She teaches core courses including Differential Equations, Numerical Analysis, and Real Analysis, contributing significantly to both undergraduate and graduate education. Her leadership extends to managing research grants and fostering collaborations across disciplines. Though no formal lab name is mentioned, her research activities suggest involvement with computational modeling groups, likely associated with applied mathematics and data science initiatives at UWF. Her ongoing work continues to advance numerical methods for complex systems in science and engineering.

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Sara Brunettiمشاهده پروفایل

دانشیار

University of SienaItaly

Sara Brunetti is an Associate Professor in the Department of Information Engineering and Mathematics at the University of Siena, Italy. She holds a PhD in Computer Science from the University of Florence (2001) and has been with the University of Siena since 2007, initially as an Assistant Professor before being promoted in 2022. Her academic work spans research, teaching, and international leadership in discrete computational methods. Graduate Degree in Mathematics, University of Florence, 1997 PhD in Computer and Automation Engineering, University of Florence, 2001 Her research focuses on the design of efficient algorithms, particularly in Discrete Tomography, Computational Geometry, Bioinformatics, and Digital Image Analysis . She investigates computational complexity, uniqueness in reconstruction problems, shape descriptors, and majority-rule protocols in distributed systems. Her work bridges theoretical computer science with applications in imaging and molecular systems. Recent publications show a strong trend in discrete geometry and mathematical morphology , with applications in image reconstruction, molecular modeling, and social network analysis. Her interdisciplinary work appears in mathematics, computer science, and web science venues, reflecting a broad impact across algorithmic disciplines. Sara Brunetti holds leadership roles in the international research community, notably as Co-Chair of Technical Committee-18 on Discrete Geometry and Mathematical Morphology of the International Association for Pattern Recognition (IAPR) from 2022 to 2024. She has been actively involved in research projects funded by MIUR, the University of Florence and Siena (PAR scheme), and the COST Action MP1207. She collaborates internationally with institutions including Technische Universität München, University of Szeged, LIMOS (Clermont Auvergne), and Université Louis Pasteur Strasbourg. While no formal list of advisees is provided, she supervises PhD theses and has co-edited special issues in her field. She is engaged in teaching, offering courses such as Foundations of Programming and Graph Theory at both undergraduate and graduate levels.

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Siddhartha Bhattacharyyaمشاهده پروفایل

استاد

National Research Institute for Mathematics and Computer ScienceNetherlands

Dr. Siddhartha Bhattacharyya is a Professor in the Department of Computer Science and Engineering at Christ University, Bangalore, with expertise spanning hybrid intelligence, quantum computing, and multimedia data processing. He has authored/edited 65 books and published over 300 research articles, focusing on interdisciplinary applications of machine learning and computational methods. Editorial Board Member, PeerJ Computer Science Holder of two PCT patents Active in academic leadership (organizing conference committees) His research integrates Artificial Intelligence , Computer Vision , and Quantum Computing to solve complex problems in education, healthcare, and environmental monitoring. Recent work includes multimodal student learning assessment, gas plume detection, and Metaverse applications. He leads an active academic lab focused on hybrid intelligence systems and their practical implementations. Key trends in his publications include: deep learning architectures for computer vision (YOLOv7, CNN-Transformer), quantum-inspired algorithms for graph coloring and bioinformatics, and educational technology innovations for remote learning environments. His work bridges theoretical advancements with real-world applications across diverse domains.

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Rosa Di Feliceمشاهده پروفایل

استاد

University of Southern CaliforniaUnited States

Rosa Di Felice is a Professor of Physics and Astronomy and Quantitative and Computational Biology at the University of Southern California. Her career spans prestigious roles at institutions including CNR-NANO Institute (Modena, Italy) and Xerox Palo Alto Research Laboratory. She specializes in computational methods bridging quantum physics and biology. PhD in Physics, University of Rome Tor Vergata (1992-1995) BSc/MSc in Physics, University of Rome Tor Vergata (Summa cum Laude, 1986-1992) Her research focuses on quantum computing for biological systems , DNA-based nanodevices , and protein-surface interactions . She has pioneered studies on charge transfer in DNA, CRISPR/Cas9 dynamics, and quantum nanomaterials using molecular dynamics and density functional theory. Recent publications highlight quantum algorithms for corrosion inhibition , electrostatics in CRISPR complexes , and topology-driven charge transport in G-quadruplexes . Her work integrates quantum computing , molecular modeling , and nano-bio interfaces . She contributes to scientific leadership as Associate Editor for European Physical Journal B and Scientific Reports , and has organized workshops on quantum computing and DNA nanotechnology.

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Mehmet Gönenمشاهده پروفایل

استاد

Koç UniversityTurkey

Mehmet Gönen is a Professor in the Department of Industrial Engineering at Koç University's College of Engineering. His academic career spans multiple disciplines at the intersection of engineering, computer science, and biomedical research. He maintains an active research program with significant contributions to machine learning applications in biological and medical contexts. Education: PhD, Boğaziçi University (2010) MSc, Boğaziçi University (2005) BS, Boğaziçi University (2003) Professor Gönen's research primarily focuses on developing and applying machine learning methodologies, particularly multiple kernel learning techniques, to solve complex problems in computational biology and medicine. His work bridges theoretical algorithm development with practical applications in cancer biology, infectious disease modeling, and drug discovery. He has made significant contributions to single-cell multiomics analysis, antibiotic resistance research, and cancer genomics. His methodological innovations in kernel-based machine learning have found applications across diverse biological domains, demonstrating the versatility and power of his computational approaches. Analysis of his recent publications (2022-2025) reveals a consistent research trajectory centered on applying advanced machine learning techniques to pressing biomedical challenges. His work demonstrates strong interdisciplinary collaboration, spanning computational methods development, clinical applications, and biological discovery. Key thematic areas include cancer genomics (particularly pathway analysis and biomarker discovery), infectious disease modeling (with emphasis on antibiotic resistance mechanisms), and methodological innovations in kernel learning for biological data integration. His research has practical implications for precision medicine, drug discovery, and healthcare analytics. Professor Gönen has maintained a robust publication record with significant contributions to both methodology development and domain-specific applications. His work on scMKL for single-cell multiomics analysis represents cutting-edge integration of computational techniques with modern biological data. The consistent focus on interpretable machine learning methods suggests an emphasis on creating tools that provide biological insights rather than just predictive accuracy. His research group appears to collaborate extensively with domain experts in microbiology, oncology, and clinical medicine, ensuring that computational approaches address real-world biomedical challenges.

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Michael R. Douglasمشاهده پروفایل

استاد

Stony Brook UniversityUnited States

Michael R. Douglas is a Professor at the Simons Center for Geometry and Physics at Stony Brook University . A renowned string theorist , he contributed to matrix models, noncommutative geometry, Dirichlet branes, and the statistical approach to string phenomenology. Previously, he was Professor of Physics and Director of the New High Energy Theory Center at Rutgers University before joining Stony Brook in 2008. Education : B.A. in Physics (Harvard, 1983), Ph.D. in Physics (Caltech, 1988) His research bridges theoretical physics and mathematics , focusing on string theory , quantum field theory , and Calabi-Yau manifolds . Recently, he has pioneered the application of machine learning and symbolic computation to solve complex mathematical and physical problems, such as computing Calabi-Yau metrics. His publications span string compactification , flux vacua , noncommutative geometry , and AI-driven scientific discovery . He explores the intersection of physics and computation , including AI models for economic simulations and mathematical data science. Scientific Awards : Sackler Prize in Physical Sciences Louis Michel Visiting Professor at IHES Clay Mathematical Institute Mathematical Emissary He is a Fellow of the American Mathematical Society and Member of the American Physical Society . Douglas has edited Journal of High Energy Physics and Communications in Mathematical Physics , and organized workshops like 'String Theory for Mathematicians' and 'Mathematical Foundations of Quantum Field Theory'.

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Eran Yahavمشاهده پروفایل

دانشیار

National and Kapodistrian University of AthensGreece

Eran Yahav is an Associate Professor in the Computer Science Department at the Technion - Israel Institute of Technology. He previously served as a research staff member at IBM T.J. Watson Research Center from 2004 to 2010. His academic journey began with a B.Sc. from the Technion in 1996, followed by a Ph.D. from Tel Aviv University in 2005. Yahav's research focuses on program analysis, program synthesis, program verification, and machine learning for programming. His work bridges theoretical foundations with practical applications, particularly in developing techniques that help programmers work more effectively with complex frameworks and APIs. He has pioneered approaches that combine static analysis with machine learning to address challenges in code search, completion, and understanding. His recent work heavily intersects with neural network applications to programming tasks, demonstrating how deep learning can enhance traditional program analysis techniques. His publication record shows a clear evolution from traditional program analysis and verification toward integrating machine learning with programming language processing. The most recent articles reveal a strong focus on neural methods for code understanding, including structural language models, adversarial examples for code models, and neural approaches to binary analysis and program synthesis. This represents a significant shift toward leveraging AI techniques to solve longstanding problems in programming languages and software engineering. Yahav has received numerous accolades including the prestigious Alon Fellowship for Outstanding Young Researchers, the Andre Deloro Career Advancement Chair in Engineering, and an ERC Consolidator Grant. He also earned best paper awards at ISSTA 2006 and 2007. As an advisor, Yahav has mentored numerous Ph.D. and Master's students who have gone on to make significant contributions in academia and industry. His research has been supported by substantial grants, including the ERC Consolidator Grant. He also serves as CTO at Tabnine, demonstrating the practical impact of his research. Yahav leads multiple research projects including PRIME (Programming with Millions of Examples), Fender (Preserving Correctness under Weak Memory Models), Saint (Synthesis using Abstract Interpretation), and several others focused on program analysis, verification, and synthesis. His work often involves building practical tools that translate theoretical advances into usable software engineering solutions.

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