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Hausi MüllerView profile

Professor

• Software Engineering
• Quantum Computing
• Self-Adaptive Systems
• +11 more

Hausi Müller is a Professor in the University of Victoria 's Department of Computer Science under the Faculty of Engineering & Computer Science . He serves as Chair of the IEEE Quantum Technical Community and co-founded the IEEE International Conference on Quantum Computing & Engineering . His academic journey began with a Ph.D. in Computer Science from Rice University (1986), followed by a Master's degree (1984) and a Dip.-Ing. from Swiss Federal Institute of Technology (1979). Research Interests: Dr. Müller's work spans quantum software engineering , self-adaptive systems , cyber-physical systems , and program understanding . He pioneered the Rigi reverse engineering tool and developed frameworks like SAVI for adaptive video analytics. Recent Publications: His 2025 articles focus on penalty-free quantum optimization and hybrid quantum software engineering , while 2024 contributions explore quantum cryptography education and notation translation tools . Earlier works address energy consumption modeling and cyber-physical system design . Awards: 2024 IEEE Computer Society Distinguished Leadership Award 2023 IEEE iCON Award for Quantum Week 2011 IBM Canada CAS Research Project of the Year for Personal Context Sphere 2012 CASCON Best Paper Award for SmarterDeals Fellow of the Canadian Academy of Engineering (FCAE) and Canadian Software Engineering Association (FCSSE) Grants & Projects: 2024-29 NSERC Quantum Software Consortium 2023-28 NSERC Discovery Grant for Hybrid Quantum Systems 2021-25 IBM CAS Cognitive Digital Twin Framework 2018-25 NSERC CREATE for Dependable IoT Applications Leadership: Dr. Müller has held multiple IEEE leadership roles since 2015, including Vice President of IEEE CS Technical & Conferences Board (2016-18), and active involvement in organizing international workshops in Shonan (Japan) and Dagstuhl (Germany).

• Algorithms
• Data Structures
• Dynamic Graph Algorithms
• +3 more

Monika Henzinger is a Professor at the Institute of Science and Technology Austria (ISTA), where she has been serving since 2023, and additionally holds the position of Vice President for Technology Transfer since 2024. Her academic journey includes professorships at the University of Vienna (2009-2023) and EPFL in Switzerland (2005-2009), as well as industry experience at Google (1999-2005) and Digital Equipment Corporation (1996-1999). She earned her PhD from Princeton University in 1993 and served as an Assistant Professor at Cornell University from 1993-1996. Dr. Henzinger's research focuses on the design and analysis of efficient algorithms and data structures, with particular emphasis on dynamic settings where inputs change repeatedly, privacy-preserving algorithms, and translating theoretical algorithms into practical implementations. Her work bridges theoretical computer science with practical applications, addressing fundamental questions about computational efficiency in evolving data environments. Her recent publications (2024-2025) demonstrate a consistent focus on dynamic graph algorithms, differential privacy, and optimization problems. These works explore cutting-edge approaches to maintaining graph structures under continuous updates, developing privacy-preserving mechanisms for streaming data, and creating efficient approximation algorithms for fundamental graph problems. The research shows strong connections between theoretical guarantees and practical implementations, with many papers addressing both theoretical bounds and experimental validation. Dr. Henzinger has received numerous prestigious awards throughout her career, including: The 2024 Best Paper Award at the Symposium on Discrete Algorithms The 2021 Wittgenstein Award Two ERC Advanced Grants (2014, 2021) Fellow of the Association of Computing Machinery (2016) Member of the Austrian Academy of Sciences (2017) The Carus medal of the German Academy of Sciences Leopoldina (2019) She currently leads an active research group comprising PhD students and postdoctoral researchers, and her team is supported by multiple significant grants including an ERC Advanced Grant for 'The design and evaluation of modern fully dynamic data structures,' a Wittgenstein Award from the Austrian Science Fund, and several other projects focused on dynamic graph algorithms and data structures. Her collaborative work spans theoretical computer science, algorithm design, and practical implementations. Dr. Henzinger's research group operates within a vibrant ecosystem of algorithmic research at ISTA, focusing on transforming theoretical insights about computational efficiency into practical tools for handling dynamic data. The group maintains strong connections with both theoretical and applied research communities, bridging the gap between abstract algorithm design and real-world implementation challenges.

Leonie RyvkinView profile

Lecturer

• Computational Geometry
• Algorithms
• Bioinformatics
• +2 more

Leonie Ryvkin is a University Lecturer in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). Her teaching responsibilities include: Discrete Mathematics (since September 2022) Formal Algorithm Analysis for Premaster (since September 2019) Logic and set theory for P&T (September 2018 to August 2024) Her research spans computational geometry and algorithms with significant applications in bioinformatics. She develops geometric similarity measures for biological location data analysis, investigates free space structures for curves, and examines complexity in combinatorial systems. Her interdisciplinary work bridges computer science and biological sciences, particularly in migratory bird movement patterns and cryptochrome protein analysis. Recent publications reveal a consistent focus on geometric algorithms applied to real-world biological problems and theoretical computer science. Key themes include Fréchet distance applications, free space realizability, and computational complexity of puzzle mechanics, demonstrating strong connections between algorithmic theory and biological data analysis. She has supervised one student as indicated by "Supervised Work (1)" in her profile. Her research activities suggest active involvement in interdisciplinary projects at the computer science-biology interface, though specific grant details are not provided in available sources.

Nathan LindzeyView profile

Assistant Professor

• Algebraic Combinatorics
• Graph Theory
• Discrete Mathematics
• +3 more

Nathan Lindzey is an Assistant Professor in the Department of Mathematical Sciences at the University of Memphis. His research focuses on algebraic combinatorics, graph theory, and discrete mathematics, with particular emphasis on extremal combinatorics, permutation patterns, and computational complexity. He has published extensively on topics such as intersecting families of spanning trees, derangement graphs, and applications of algebraic methods in combinatorial optimization. Dr. Lindzey's work bridges theoretical foundations and practical applications, including studies on association schemes, semidefinite optimization, and harmonic polynomials. His recent articles address challenges in graph theory, coding theory, and algorithm analysis, reflecting a commitment to advancing interdisciplinary approaches in discrete mathematics. His research trends highlight contributions to understanding extremal problems in combinatorics, leveraging algebraic tools and computational techniques. Though no awards are explicitly listed, his prolific publication record underscores his active role in the academic community. Dr. Lindzey advises no listed students and has no documented grants in the provided text. He is affiliated with the University of Memphis' Mathematical Sciences department, contributing to both teaching and research initiatives in the field.

Amy N. MyersView profile

Senior Lecturer

• Combinatorics
• Probability Theory
• Enumerative Combinatorics
• +3 more

Amy N. Myers serves as Senior Lecturer in Mathematics and Math Program Coordinator at Bryn Mawr College's Department of Mathematics, where she teaches core courses including Calculus I-II, Multivariable Calculus, Probability Theory, and specialized graduate seminars in Enumerative Combinatorics and History of Mathematics. Her academic credentials include a Ph.D. in Mathematics from Dartmouth College (1999), establishing her foundational expertise in discrete mathematical structures. Dr. Myers' research demonstrates exceptional depth in combinatorial mathematics , particularly in pattern avoidance phenomena and probabilistic enumeration . Her work on interval orders bridges order theory with topological combinatorics, while her contributions to the Coupon Collector's Problem reveal novel asymptotic behaviors with applications in computational biology. The SIGEST recognition of her probability research by SIAM Review underscores its interdisciplinary significance across theoretical computer science and stochastic modeling. Analysis of her publication trajectory shows consistent innovation in multiset permutation statistics and forbidden substring constraints, with recent focus on shellability properties in ordered structures. Her combinatorial frameworks increasingly incorporate computational topology, reflecting evolving methodological sophistication. No scientific awards are documented in the available institutional records. As Math Program Coordinator, Dr. Myers oversees curriculum development and student advising for mathematics majors, though specific mentorship details remain undisclosed. Grant funding information is not publicly accessible through current university channels. Research infrastructure details including laboratories or collaborative teams are not referenced in her official academic profile.

Erika Roldan RoaView profile

Visiting Assistant Professor

• Stochastic Topology
• First Passage Percolation
• Extremal Topological Combinatorics
• +3 more

Erika Roldán Roa is a mathematician with academic affiliations at Ohio State University (Visiting Assistant Professor, 2019-2020) and Technische Universität München (Marie Skłodowska-Curie Fellow, 2020-2022). She holds a Ph.D. in Probability and Statistics from CIMAT (2018) and has conducted postdoctoral research at institutions like EPFL and Ohio State. Her work bridges stochastic topology, combinatorics, and educational technology, focusing on topics like cubical sliding puzzles, Eden growth models, and extremal topological problems in polyominoes. Education: Ph.D. in Probability and Statistics, CIMAT (2018) M.Sc. in Mathematics, CIMAT (2014) B.Sc. in Mathematics, Universidad de Guanajuato (2010) Research Interests: Her research spans stochastic topology, extremal combinatorics, and applications of discrete geometry to puzzles and games. She explores configuration spaces, topological properties of random structures, and innovative educational approaches like the Music Math Project, which integrates music and mathematics. Awards: Includes the Marie Skłodowska-Curie Fellowship (2020-2022), Arana-Ordaz Prize (2019), and Sofia Kovalevskaia Prize (2019). She has also received grants from CONACYT and CIMAT. Grants & Outreach: Developed workshops in marginalized communities using music and math, and contributed to software tools like the Eden Model analysis package. Active in science popularization through articles and outreach programs. Labs/Teams: Collaborated on projects like the Eden Model (topology analysis software) and extremal animal configurations, often involving interdisciplinary teams in mathematics and computer science.

• Computational Geometry
• Discrete Geometry
• Graph Drawing
• +7 more

Irene Parada is an Assistant Professor and Serra Húnter Fellow at the Universitat Politècnica de Catalunya (UPC BarcelonaTech), Spain, where she is affiliated with the Department of Computer Science in the College of Mathematics and Computer Science. She is a member of the Research Group on Discrete, Combinatorial and Computational Geometry, contributing to both theoretical and applied aspects of geometric algorithms. Her academic background includes a PhD in Computer Science from Graz University of Technology (2019), a Master’s in Advanced Mathematics and Mathematical Engineering from UPC (2015), and a Bachelor’s in Mathematical Engineering from Universidad Complutense de Madrid (2014). She has held postdoctoral positions at the Technical University of Denmark, TU Eindhoven, and Utrecht University. Her research lies at the intersection of computational geometry, graph drawing, and algorithmic robotics. She investigates geometric graph representations, reconfiguration of modular robots, crossing numbers, and motion planning algorithms. Her work combines theoretical depth with practical applications in robotics and visualization. Her recent publications, appearing in top venues like SoCG, GD, ESA, and Algorithmica, reveal a strong trend toward geometric graph theory, reconfiguration problems, and algorithmic complexity. Topics include optimal compaction of sliding cubes, upward planar embeddings, and Fréchet-based trajectory analysis. Best paper award at EuroCG 2022 Best paper award at ICALP 2021 Excellent Course Evaluation at TU Eindhoven (2020/2021) She has advised and collaborated with numerous researchers in the field, though no formal students are listed. She has taught courses in Numerical Computing, Algorithms, Data Structures, and Motion in Robotics at UPC, Utrecht University, and TU Eindhoven. She has not received public mention of grants, but her Serra Húnter Fellowship and Margarita Salas postdoctoral position indicate competitive funding support. She is actively involved in the Research Group on Discrete, Combinatorial and Computational Geometry at UPC and collaborates extensively with researchers across Europe, particularly in Austria, the Netherlands, and Germany.

Vitaliy KurlinView profile

Professor

• Topological Data Analysis
• Geometric Data Science
• Materials Science
• +7 more

Professor Vitaliy Kurlin is a full professor at the University of Liverpool's School of Electrical Engineering, Electronics, and Computer Science, leading the Data Science Theory and Applications group since 2017. His work bridges Geometric Data Science with applications in Materials Innovation Factory , focusing on crystallography, structural biology, and computer vision. PhD in Geometry and Topology (Moscow State University, 2003) Developed Geometric Data Science for continuous parametrization of periodic crystals and protein structures Organizer of MACSMIN annual conference and LMS network since 2020 His research includes complete isometry invariants for point clouds, enabling polynomial-time algorithms for crystal structure prediction and protein backbone analysis. Key projects involve UKRI New Horizons grants , Royal Society APEX fellowships , and collaborations with Mila , Cambridge Crystallographic Data Centre , and Lawrence Berkeley National Lab . Scientific awards include the Royal Society APEX fellowship (2023), Royal Academy of Engineering Industry Fellowship (2021-2023), and Lecturer of the Year (Durham Student's Union, 2013). He has mentored 15+ PhD students, including Dr. Dan Widdowson and Dr. Olga Anosova, with joint publications in CVPR , NeurIPS , and Scientific Reports . His group's geometric invariants resolve data ambiguity in crystallography and structural biology, supported by grants from EPSRC , NERC , and Intel . Current projects include building geographic-style maps of the material space and organizing workshops at ICERM and SIAM meetings.

Benjamin SoltoffView profile

Senior Lecturer

• Computational Social Science
• Data Science
• Technology and Equity
• +3 more

Benjamin Soltoff is a Senior Lecturer in Information Science at Cornell University, teaching courses in data science, research design, and web design. He holds a BA in Political Science from James Madison University and MA/PhD from Penn State in Political Science with concentrations in American Government, Political Methodology, and Law & Courts. His research explores computational methods in social sciences, including judicial politics and data-driven policy analysis. Previously, he co-developed the Masters in Computational Social Science at the University of Chicago and served as its Associate Director. His work bridges computational modeling and social science theory, often applying machine learning and text analysis to study institutions and governance. He maintains an active blog analyzing puzzles from FiveThirtyEight, focusing on probability, game theory, and mathematical modeling. Notable contributions include analyses of sports strategy, cultural traditions, and optimization problems.

• Nonlinear Partial Differential Equations
• Mathematical Physics
• Soliton Theory
• +3 more

Robert Jenkins is an Associate Professor in the Department of Mathematics at the University of Central Florida (UCF), within the College of Sciences. His research focuses on nonlinear partial differential equations, soliton theory, and asymptotic analysis. He holds an office in MSB 429 and can be reached via email at Robert.Jenkins@ucf.edu . His research interests include the study of integrable systems, dispersive equations, and their asymptotic behaviors. Recent work has explored soliton gases, self-focusing phenomena, and long-time asymptotics of solutions to equations like the KdV and nonlinear Schrödinger equations. His contributions highlight the interplay between analytical methods, spectral theory, and applied mathematical physics. Jenkins' publications demonstrate expertise in analyzing complex wave interactions, regularization of shocks, and semiclassical limits of nonlinear systems. His work bridges theoretical developments with applications in mathematical physics and nonlinear optics.

Gabriel ValienteView profile

Professor

• Algorithms
• Bioinformatics
• Computational Biology
• +5 more

Gabriel Valiente is an accredited Full Professor in the Department of Computer Science at Universitat Politècnica de Catalunya - BarcelonaTech (UPC). His research focuses on algorithms, bioinformatics, and combinatorial pattern matching with applications in computational biology. He is affiliated with the Algorithms, Bioinformatics, Complexity and Formal Methods Research Group and the Institute of Mathematics of UPC. Valiente has authored influential books like Algorithms on Trees and Graphs and Combinatorial Pattern Matching Algorithms in Computational Biology . His work spans phylogenetic network analysis, graph algorithms, and metagenomic sequence analysis. Key contributions include methods for comparing phylogenetic trees (e.g., generalized Robinson-Foulds distance), aligning biological networks (e.g., virus-host protein interaction networks via ILP), and taxonomic classification in metagenomics (e.g., MetaShot). His research integrates algorithm design with applications in microbiology, virology, and systems biology. Valiente’s publications demonstrate expertise in tree-based algorithms, graph theory, and bioinformatics tool development. His recent work addresses challenges in virus-host interaction modeling and scalable analysis of large biological networks.

• Graph Theory
• Combinatorics
• Theoretical Computer Science
• +3 more

Christian Elbracht is a Researcher in the Department of Mathematics within the Faculty of Mathematics, Informatics and Natural Sciences at the University of Hamburg. He is based in Geomatikum (Bundesstraße 55), Room 232, Hamburg, with office hours conducted online by appointment. His primary academic activities involve teaching exercise sessions for mathematics courses targeting computer science students and physicists. His research focuses intensely on advanced graph theory, particularly tangle theory, separation systems, and structural decompositions in both finite and infinite graphs. Key themes include tree structures, connectivity hierarchies, submodular systems, ubiquity conjectures, and combinatorial optimization. His work bridges theoretical mathematics with applications in clustering algorithms and data science, as evidenced by collaborations extending into machine learning domains. Christian's publication trends reveal a concentrated specialization in tangle-tree duality and structural graph theory since 2018, with significant contributions to abstract separation systems and infinite graph theory. His collaborative work frequently involves researchers like J. Kneip and M. Teegen, with publications appearing in journals such as the Journal of Combinatorial Theory and Advances in Combinatorics . While no scientific awards are publicly documented, his research output demonstrates consistent contributions to combinatorial theory. His teaching responsibilities include leading exercise sessions for foundational mathematics courses for computer science students (Mathematics I/II) and specialized topics like Graph Theory I and Optimization. Christian maintains active research collaborations across multiple institutions, particularly evident in his work on graph ubiquity theorems and clustering applications. His technical work centers on structural analysis of graphs, with implications for theoretical computer science and discrete mathematics.

Oren WeimannView profile

Professor

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Data Structures
• +9 more

Oren Weimann is a Professor in the Department of Computer Science at the University of Haifa, Faculty of Natural Sciences. His research lies at the intersection of theoretical computer science, algorithm design, and data structures, with a strong focus on planar graphs, combinatorial pattern matching, and fine-grained complexity. He has published extensively in top-tier venues such as STOC, SODA, ICALP, PODC, and ESA. Education: Ph.D., Massachusetts Institute of Technology (MIT), 2005–2009. Advisor: Erik Demaine. Dissertation: "Accelerating Dynamic Programming" Postdoc, Weizmann Institute of Science, 2009–2011. Host: David Peleg M.Sc., University of Haifa, 2004–2005. Advisor: Gad Landau. Dissertation: "Using PQ trees for Comparative Genomics" B.A., Technion – Israel Institute of Technology, 1999–2002 Oren Weimann's research centers on the design and analysis of efficient algorithms, particularly for planar and structured graphs. His work explores fundamental problems such as shortest paths, distance oracles, fault tolerance, edit distance, and pattern matching. He investigates both upper and lower bounds, often pushing the limits of what is computationally feasible under fine-grained complexity assumptions. His contributions include optimal labeling schemes, compressed data structures, and breakthroughs in dynamic and distributed graph algorithms. His recent publications reveal a consistent trend in developing highly efficient algorithms for planar graphs, with a focus on distance computation, fault tolerance, and compression. Keywords across these works include planar graphs, dynamic programming, string matching, and conditional lower bounds, reflecting a deep integration of algorithmic techniques and complexity theory. He frequently collaborates with leading researchers such as Shay Mozes, Paweł Gawrychowski, and Philip Bille. Scientific Awards: Best Paper Award, CPM 2007 Best Paper Award, ICALP 2020 (mentioned in context of work) Oren Weimann has advised numerous PhD and Master’s students, including Yaseen Abd-Elhaleem, Nathan Wallheimer, Aviv Bar-natan, and Shon Feller, whose dissertations have led to publications in major conferences. He has also mentored several postdoctoral researchers such as Shay Golan, Itai Boneh, and Panagiotis Charalampopoulos. His work has been supported by competitive research grants, though specific grant titles are not listed in the text. He has served on the program committees of key conferences including SODA, ICALP, CPM, ESA, and SPIRE, demonstrating active leadership in the theoretical computer science community. He is associated with a vibrant research group focused on algorithms and data structures, likely involving collaboration with students and postdocs on projects related to graph algorithms, string processing, and complexity. While no formal lab name is mentioned, his collaborative output suggests a strong, productive research team at the University of Haifa.

Tomohiro IView profile

Associate Professor

• Algorithms on Strings
• String Pattern Matching
• Indexing
• +9 more

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.

• Computational Social Science
• Data Science
• Social Network Analysis
• +4 more

Termeh Shafie is a Professor of Computational Social Science and Data Science at the University of Konstanz, Department of Politics and Public Administration. She specializes in statistical network analysis, multigraph modeling, and interdisciplinary applications across social sciences and digital humanities. Her research includes network entropy, data privacy, and archaeological network reconstruction.