جستجوی استاد راهنمای دانشگاهی

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.

• Algorithms on Discrete Structures
• Parameterized Algorithms
• Structural Graph Theory
• +۴ مورد دیگر

Michał Pilipczuk is an Associate Professor at the Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics of the University of Warsaw. His research focuses on theoretical computer science, particularly algorithms on discrete structures, parameterized algorithms, structural graph theory, and logic in computer science. He leads the ERC-funded project "BOBR: Decomposition Method for Discrete Problems" and previously led a grant on optimality in parameterized complexity funded by the Polish National Science Center. His research interests include parameterized algorithms , structural graph theory , graph algorithms , and computational complexity . He has made significant contributions to the understanding of problems such as Independent Set in restricted graph classes, graph editing problems, and structural decompositions. The recent publications highlight a strong focus on structural graph theory and exact algorithms . Key themes include quasi-polynomial time algorithms for Independent Set in claw-free graphs, diameter computation in bounded genus graphs, and kernelization in trivially perfect graphs. His work often bridges combinatorial insights with algorithmic applications, particularly in the context of parameterized complexity. Principal Investigator, ERC Grant BOBR: Decomposition Method for Discrete Problems (2021–2026) Principal Investigator, Polish National Science Center Grant on Optimality in Parameterized Complexity (2014–2017) He has advised or collaborated with several researchers, including Marcin Wrochna and Marcin Pilipczuk. His work is published in top venues such as STOC, SODA, ESA, and ICALP.

Andre Bergerمشاهده پروفایل

دانشیار

• Operations Research
• Optimization Algorithms
• Combinatorial Optimization
• +۳ مورد دیگر

Andre Berger is an Associate Professor of Operations Research at Maastricht University, affiliated with the QE Operations Research department within the School of Business and Economics. He holds a PhD in Mathematics from Emory University (2006) and completed a postdoc at Technical University Berlin. His research focuses on optimization algorithms, combinatorial optimization, and their applications in scheduling, network design, and operations research. Notable contributions include work on the many-visits Traveling Salesman Problem and cluster editing algorithms. Berger’s recent publications span scheduling theory, MRI-based clinical research collaborations, and theoretical advancements in facility location models. He is based at Tongersestraat 53, Maastricht, and can be reached via a.berger@maastrichtuniversity.nl. Education: MSc in Mathematics, Emory University (2003) PhD in Mathematics, Emory University (2006) Research Interests: Berger’s work bridges theoretical computer science and practical applications in operations research, emphasizing algorithm design for complex optimization problems. Key areas include scheduling algorithms, network flow optimization, and geometric optimization challenges such as the Apollonius problem in facility location. His interdisciplinary approach integrates mathematical programming with real-world scenarios in telecommunications and healthcare.

Vijay Vaziraniمشاهده پروفایل

استاد

• Algorithmic Game Theory
• Matching Markets
• Computational Complexity
• +۶ مورد دیگر

Vijay Vazirani is a Distinguished Professor in the Department of Computer Science at the University of California, Irvine , where he directs the ACO Center @ UCI . He earned his Ph.D. in Computer Science from UC Berkeley and a S.B. from MIT. Vazirani is a Guggenheim Fellow , ACM Fellow , and 2022 INFORMS John von Neumann Theory Prize recipient. Research Areas: Algorithmic Game Theory, Matching Markets, Computational Complexity, Approximation Algorithms His groundbreaking work includes co-founding algorithmic game theory and solving a 30-year-old problem with an NC algorithm for perfect matching in planar graphs . Recent research focuses on matching-based market design, with a $500K NSF grant for advancing algorithms in matching and market equilibria. His 15 most recent papers explore topics like core imputations, stable matching lattices, and Nash bargaining solutions. Scientific Awards: Guggenheim Fellowship ACM Fellow 2022 INFORMS John von Neumann Theory Prize Vazirani advises numerous Ph.D. students and postdocs, including Tung Mai , Thorben Trobst , and Rohith Reddy Gangam . He contributes to major workshops and co-edited foundational texts like Algorithmic Game Theory and Online and Matching-Based Market Design .

Federico Battistonمشاهده پروفایل

دانشیار

• Network Science
• Complex Systems
• Computational Social Science
• +۸ مورد دیگر

Federico Battiston is an Associate Professor of Network Science and Director of the PhD Program in Network Science at Central European University (CEU), the first such program in Europe. He holds a PhD in Applied Mathematics from Queen Mary University of London and degrees in statistical physics from Sapienza University of Rome. His research focuses on network science, complex systems, and computational social science, with contributions in leading journals like Nature Physics , Physical Review Letters , and Science Advances . He coordinates the software project Hypergraphx and was Chair of NetSci2023, the largest Network Science conference. He has received awards including the Complex Systems Society's Junior Award (2022) and the European Physical Society's Early Career Prize (2021). Education: PhD in Applied Mathematics, Queen Mary University of London MSc in Theoretical Physics, Sapienza University of Rome BSc in Physics, Sapienza University of Rome Research Interests: Battiston explores generalized network structures (e.g., multilayer and higher-order networks), dynamics on networks (epidemics, social/cultural dynamics, synchronization), and their applications in social systems, neuroscience, and ecology. He emphasizes how network topology influences collective behavior and emergent phenomena. Key Contributions: His work includes hypergraph modeling, collaboration in escape rooms, and the role of higher-order interactions in brain networks. He co-authored the book Higher-order systems and guest-edited a Focus Collection on higher-order networks in Communications Physics . Awards & Roles: Junior Award of the Complex Systems Society (2022) Early Career Prize, European Physical Society (2021) Elected Member, Complex Systems Society Council Editor, Communications Physics Advising & Grants: Advised PhD students such as Milan Janosov, Luis Natera, and Rebeka Szabo. Two students received CEU Advanced Awards. His projects include Mapping the Higher-Order Dynamics of Neurodegeneration and DYNASNET . Labs/Teams: Leads the Hypergraphx team and collaborates on interdisciplinary projects in network science, including ecological networks and urban mobility analysis.

Gérard Ben Arousمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Analysis
• Large Deviations
• +۶ مورد دیگر

Gérard Ben Arous is a Silver Professor of Mathematics at New York University's Courant Institute of Mathematical Sciences, where he has served as Director and Vice Provost for Science and Engineering Development since 2011. He holds a PhD in Mathematics from the University of Paris VII (1981) and has previously taught at the University of Paris-Sud, École Normale Supérieure, and the Swiss Federal Institute of Technology in Lausanne. His research focuses on probability theory, stochastic analysis, and their applications to physics and industrial problems, particularly exploring complex systems' long-time behavior and aging phenomena in disordered media. Education: PhD in Mathematics, University Paris 7, France (1981) M.Sc. in Statistics, University Paris-Sud Orsay, France (1979) B.S. in Mathematics, École Normale Supérieure (Paris), France (1978) His research interests bridge probability with partial differential equations, dynamical systems, and statistical mechanics. Key contributions include studies on random media, random matrices, and the interplay between complexity, disorder, and aging in physical systems. He has held leadership roles in academic institutions, including directing the mathematics departments at Orsay and École Normale Supérieure, and founded Lausanne's Bernoulli Center. Notable awards include Fellow of the Institute of Mathematical Statistics and the Montyon Prize from the French Academy of Sciences. His work is published in top journals like Annals of Probability and Communications in Pure and Applied Mathematics , and he co-edits Probability Theory and Related Fields . Ben Arous has advised numerous researchers and contributed to interdisciplinary projects, including studies on machine learning landscapes and financial mathematics. His lab focuses on stochastic modeling and its applications across disciplines.

Daniel Royمشاهده پروفایل

استاد

• Machine Learning
• Statistical Learning Theory
• Foundations of Statistics
• +۱۱ مورد دیگر

Daniel Roy is a Full Professor at the University of Toronto, holding cross-appointments in the Department of Statistical Sciences, Computer Science, Electrical and Computer Engineering, and the Department of Computer and Mathematical Sciences at UTSC. He is also a Canada CIFAR AI Chair and Research Director at the Vector Institute, reflecting his leadership in AI and machine learning research. His educational background includes a PhD, MEng, and BSc in Computer Science from MIT, where his doctoral work earned the MIT EECS Sprowls Award. Prior to joining Toronto, he was a Newton International Fellow at the Royal Society and a Research Fellow at Emmanuel College, University of Cambridge. His research centers on foundational principles in machine learning, statistics, and probabilistic reasoning. Key interests include statistical learning theory, Bayesian nonparametrics, probabilistic programming, and information-theoretic generalization. His work bridges theoretical computer science, mathematical logic, and applied probability. His recent publications, appearing in ICML, NeurIPS, COLT, and JMLR, reflect a strong focus on theoretical advances in generalization, online learning, and stochastic optimization. Themes include minimax rates, conditional mutual information, and the role of data in PAC-Bayes bounds. His group has made foundational contributions to probabilistic programming, including work on Church and the computability of conditional probability. NSERC Discovery Accelerator Supplement Ontario Early Researcher Award Google Faculty Research Award Newton International Fellowship MIT EECS Sprowls Award Daniel Roy advises numerous PhD students and postdoctoral researchers, many of whom have gone on to prestigious positions in academia and industry. His group actively collaborates with leading researchers in machine learning and statistics. He is also an Action Editor for the Journal of Machine Learning Research and Transactions of Machine Learning Research, underscoring his role in shaping the field. He leads a vibrant research group focused on theoretical machine learning and probabilistic modeling, and maintains active collaborations with institutions such as MIT, Cambridge, and the Vector Institute. He is also the founder and maintainer of the probabilistic-programming.org wiki, a key resource in the community.

• Software Engineering
• Programming Languages
• Systems
• +۴ مورد دیگر

Manuel Rigger is an Assistant Professor at the National University of Singapore (NUS), leading the TEST Lab (Trustworthy Engineering of Software Technologies) within the PL/SE group at the School of Computing. His research focuses on improving the reliability of data-centric systems through automated testing frameworks and formal methods. Education : PhD from Johannes Kepler University Linz (supervised by Hanspeter Mössenböck), postdoctoral work at ETH Zurich (Advanced Software Technologies Lab under Zhendong Su). Research Interests : Automated testing of database systems Programming language design and verification Incremental build systems Formal methods for software reliability Key Contributions : Developed tools like SQLancer (for finding bugs in databases) and CERT (performance issue detection). His work has uncovered over 800 bugs in real-world systems. Awards : Recipient of the ERC Consolidator Grant (2025) for groundbreaking research in software security and testing. Service Roles : Organizer of ICFP/SPLASH 2025 Outdoor Activities, committee member for OOPSLA Review, PLDI Artifact Evaluation, and ICSE Program Committee. Also actively involved in organizing workshops (e.g., Fuzzing & Software Security Summer School 2025).

• 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.

• Theoretical Computer Science
• Sublinear Algorithms
• Complexity Theory
• +۴ مورد دیگر

Elena Grigorescu is an Adjunct Associate Professor in the Department of Computer Science at Purdue University, where she has been a faculty member since Fall 2012. Her research program spans theoretical computer science with a focus on foundational algorithmic challenges in large-scale data processing and computational limits, maintaining strong connections to cryptography, communications, and optimization applications. Her educational background includes a PhD from the Massachusetts Institute of Technology (MIT), establishing her expertise in rigorous theoretical frameworks. Professor Grigorescu's research emphasizes designing algorithms that operate in sublinear time or space for massive datasets, analyzing complexity of error-correcting codes and lattices, and exploring information-theoretical computation limits. Current investigations integrate differential privacy with learning-augmented techniques to solve online optimization problems, network design challenges, and data stream processing bottlenecks. Her work bridges abstract theory with practical implementations in cryptographic systems and quantum computing paradigms, demonstrating consistent innovation in algorithmic foundations. Analysis of her recent publications (2022-2025) reveals a dominant focus on sublinear-time algorithms, particularly at the intersection with differential privacy and machine learning augmentation. Key contributions include novel spanner constructions for network design, privacy-preserving clustering frameworks, and breakthroughs in trace reconstruction and coding theory. A pronounced trend shows increasing integration of learning-based predictions to enhance classical online algorithms for packing/covering problems while maintaining theoretical guarantees, alongside sustained contributions to error-correcting code analysis and graph-theoretic foundations. No specific scientific awards or major fellowships were documented in the provided materials, though her publication record in premier venues like STOC, FOCS, and APPROX/RANDOM indicates significant peer recognition. Professor Grigorescu actively mentors graduate students in theoretical computer science research, guiding investigations in sublinear algorithms, complexity theory, and coding theory. Her collaborative projects involve interdisciplinary teams across institutions, focusing on cryptographic applications and quantum information theory, though specific grant details were not included in the source texts. Ongoing work suggests expansion into quantum algorithm design and privacy-preserving machine learning frameworks. While dedicated laboratory facilities were not specified, her research operates within Purdue's theoretical computer science group, leveraging university-wide computational resources and fostering collaborations through conference participation and workshop organization.

Emily Kyle Foxمشاهده پروفایل

دانشیار

• Computational Geometry
• Computational Topology
• Graph Algorithms
• +۳ مورد دیگر

Emily Kyle Fox is an Associate Professor in the Department of Computer Science at The University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. She holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC, 2013), followed by postdoctoral positions at Duke University and Brown University’s Institute for Computational and Experimental Research in Mathematics (ICERM). Her research focuses on algorithmic foundations with an emphasis on computational geometry, topology, and graph algorithms, particularly leveraging topological methods to design efficient algorithms for complex problems. Education Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2013) M.S. in Computer Science, University of Illinois at Urbana-Champaign (2010) B.S. in Computer Science, University of Illinois at Urbana-Champaign (2008) Research Interests Computational Geometry & Topology Graph Algorithms & Optimization Algorithm Design for Surface-Embedded and Geometric Networks Applications of Topology in Algorithm Development Publications Trends Her work includes breakthroughs in geometric transportation problems, minimum cut algorithms on hypergraphs/surface graphs, and efficient approximation schemes for transshipment and Fréchet edit distance. Recent contributions emphasize deterministic algorithms with near-linear time complexity and applications of topology to graph algorithm design. Awards NSF CAREER Award (2020) Best Teacher in Computer Science (UTD, 2020) Stutzke Dissertation Completion Fellowship (UIUC, 2013) Grants & Affiliations Dr. Fox secured a $586,654 NSF CAREER grant (2020) for topology-driven algorithm design. She serves on UTD’s Graduate Admissions Committee and actively contributes to the Algorithms and Theory Group. Labs/Teams Active in the Algorithms and Theory Group at UTD, focusing on foundational algorithm research with geometric and topological applications.

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.

• Combinatorics
• Latin squares
• Matrix permanents
• +۵ مورد دیگر

Prof Ian M. Wanless is a Professor in the School of Mathematics at Monash University, Melbourne, Australia. He has held academic positions at institutions including the Australian National University (ANU), University of Melbourne, Christ Church Oxford, and Charles Darwin University. His research primarily focuses on combinatorics, with specializations in Latin squares, matrix permanents, graph theory, and algebraic structures. He has made significant contributions to the enumeration and properties of Latin squares, including groundbreaking work on transversals, orthogonality, and symmetry. Wanless has also explored connections between Latin squares and algebraic structures like quasigroups and loops. His education includes a PhD from ANU (supervised by Brendan McKay) and postdoctoral fellowships at Oxford and ANU. He has been awarded an Australian Research Council Future Fellowship (2011) and has led major research initiatives, including organizing international conferences (e.g., 5ICC in 2017). His work spans theoretical results and computational methods, with over 100 publications in top journals like Journal of Combinatorial Theory and SIAM Journal on Discrete Mathematics . Wanless’s research interests extend to design theory, hypergraphs, and permutation polynomials. He co-edits the Electronic Journal of Combinatorics and has held leadership roles in professional societies, including president of the Combinatorial Mathematics Society of Australasia. His current projects include studies on perfect 1-factorizations, covering radii of permutation sets, and algebraic properties of Latin squares.

• Algorithms
• Graph Theory
• Computational Complexity
• +۳ مورد دیگر

Kitty Meeks is a Reader in the School of Computing Science at the University of Glasgow, having transitioned from the School of Mathematics and Statistics in 2016. Her research focuses on algorithm design, particularly parameterized and counting complexity, with applications in graph theory and precision medicine. She holds an EPSRC Fellowship (2021–2026) and previously a Royal Society of Edinburgh Personal Research Fellowship (2016–2021). Education: MMath in Mathematics and Computer Science (Oxford, 2009), DPhil in Mathematics (Oxford, 2013). Postdoctoral research at Queen Mary University of London (2012–2014). Research: Explores efficient algorithms for network analysis, including structural properties of real datasets and applications in precision medicine. Current projects address combinatorial optimization for multiple solution spaces. Awards: Royal Society of Edinburgh Fellowship, EPSRC Fellowship. Grants: EPSRC projects on multilayer algorithmics and precision medicine, plus Scottish Crucible Seed Funding. Students: Supervises Peace Ayegba (Student-Project Allocation Problems) and Laura Larios-Jones (Temporal Graph Algorithms).