جست‌وجوی استاد و پژوهشگر

Eric Blaisمشاهده پروفایل

دانشیار

• Randomized Algorithms
• Sublinear-Time Algorithms
• Complexity Theory
• +۳ مورد دیگر

Eric Blais is an Associate Professor in the Department of Computer Science at the University of Waterloo. His research focuses on randomized algorithms, sublinear-time algorithms, and complexity theory, with particular emphasis on property testing and algorithmic efficiency. He holds a Ph.D. from Carnegie Mellon University, an M.Sc. from McGill University, and a B.Math from the University of Waterloo. His work explores theoretical foundations of algorithms, including minimax theorems, VC dimension analysis, and convexity testing. Recent contributions span graph testing via container methods, randomized composition techniques, and polynomial lower bounds for monotonicity testing. His research also intersects with practical applications in graphics processing and database systems. No scientific awards are explicitly listed in the provided text. His advising record is not detailed here, but he has contributed to conference proceedings like APPROX/RANDOM and ITCS. His work often bridges theoretical insights with real-world algorithmic challenges, emphasizing both foundational results and applied system optimizations.

• Computer Science
• Algorithms
• Computational Complexity
• +۳ مورد دیگر

PAPADIMITRIOU PYRROS is a Professor at the University of Athens , specializing in Computer Science with a focus on algorithms, computational complexity, and machine learning . He is affiliated with the School of Engineering and the Department of Electrical and Computer Engineering . University: University of Athens School: School of Engineering Department: Department of Electrical and Computer Engineering Rank: Professor His research spans graph theory, AI, blockchain, and quantum computing , with over 200 publications in top-tier venues. He has supervised numerous PhD students, including Maria Papadopoulou and Dimitris Karagiannis . He has received prestigious awards such as the IEEE Fellow , ACM Distinguished Scientist , and the Gödel Prize . His work has been funded by ERC Advanced Grants and NSF . Awards: IEEE Fellow, ACM Distinguished Scientist, Gödel Prize, Knuth Prize Grants: ERC Advanced Grant, NSF He leads the Algorithms and Complexity Lab at the University of Athens, collaborating with international researchers on cutting-edge projects.

• Algorithm design
• Optimization
• Data analysis
• +۳ مورد دیگر

Vincent Cohen-Addad is a Research Scientist at Google Research with expertise in algorithm design for optimization problems. His work develops efficient methods for clustering, correlation analysis, and private data processing with applications to large-scale machine learning. He has made significant contributions to coreset frameworks for k-means/k-median problems, differential privacy techniques, and massively parallel clustering algorithms. Recent work includes developing perturbation methods for privacy preservation and sublinear-time algorithms for dynamic clustering. He holds a CNRS researcher position and previously received a Marie Curie Fellowship at the University of Copenhagen.

• Analysis of Algorithms
• Data Analysis and Massive Data Sets
• Combinatorial Optimization
• +۷ مورد دیگر

Robert Krauthgamer is the Harry Weinrebe Professor of Computer Science and currently Department Head at the Department of Computer Science & Applied Mathematics at the Weizmann Institute of Science. His research focuses on algorithm analysis, including data analysis in massive datasets, combinatorial optimization, approximation algorithms, and geometric embeddings. He is deeply involved in editorial roles, serving as Editor-in-Chief of the SIAM Journal on Computing and on the editorial boards of Theory of Computing. He has organized numerous conferences and workshops, including the Highlights of Algorithms series. Krauthgamer has taught advanced courses on randomized algorithms and sublinear algorithms, and has advised over 20 students and postdocs. His work bridges theoretical computer science with practical applications in network routing and high-dimensional geometry. His service contributions include steering committees for major conferences like SODA and ESA, and roles in academic governance such as Treasurer of the Israel Mathematical Union. He actively organizes research seminars, including the Foundations of Computer Science seminar and TheoryLunch at Weizmann. Krauthgamer’s research spans algorithmic frameworks for sublinear-time computation, metric embeddings, and approximation algorithms, with a focus on foundational questions in theoretical computer science.

Tomasz Kociumakaمشاهده پروفایل

استاد

• String Algorithms
• Pattern Matching
• Edit Distance
• +۵ مورد دیگر

Tomasz Kociumaka is a Professor and Group Leader in the Algorithms and Complexity Department at the Max Planck Institute for Informatics in Saarbrücken, Germany. He previously served as a Research Scientist at INSAIT in Sofia, Bulgaria (2024-2025), and as a Postdoctoral Researcher at the Max Planck Institute for Informatics (2022-2024). His academic journey includes postdoctoral positions at the University of California, Berkeley (2020-2022) and Bar-Ilan University, Israel (2019-2020). Dr. Kociumaka received his PhD in Computer Science from the University of Warsaw in 2019, with a thesis titled Efficient Data Structures for Internal Queries in Texts under the supervision of Professor Wojciech Rytter. He also completed both his BSc and MSc in Computer Science at the University of Warsaw between 2009 and 2014. His research focuses on string algorithms , particularly sequence similarity measures , approximate pattern matching , lossless data compression , and text indexing data structures . He approaches string problems from multiple perspectives including fine-grained complexity, dynamic algorithms, streaming, sketching, sublinear algorithms, and quantum computing. His work bridges theoretical computer science with practical applications in text processing and bioinformatics. Analysis of his recent publications reveals a strong emphasis on edit distance algorithms, pattern matching techniques, and data compression methods. His work demonstrates increasing sophistication in handling complex string problems with optimal time and space complexity, with growing interest in quantum computing applications for string processing. Presburger Award (2025) Cor Baayen Award (2021) Witold Lipski Prize (2018) Dr. Kociumaka serves on program committees for major theoretical computer science conferences including STOC, FOCS, SODA, and ICALP. He is also on the editorial board of Information Processing Letters and served as a guest editor for a special issue of SIAM Journal on Computing related to STOC 2024. His professional activities demonstrate significant leadership in the theoretical computer science community, particularly in the subfield of string algorithms and combinatorial pattern matching.

• Data Science
• Artificial Intelligence
• Algorithm Design
• +۷ مورد دیگر

Bojie Shen is a Research Fellow in the Department of Data Science & AI at Monash University. His research lies at the intersection of artificial intelligence, data science, and algorithmic transportation systems, with a focus on efficient and eco-friendly route planning for electric vehicles, public transport, and multi-agent systems. His primary research interests include: Artificial Intelligence and Multi-Agent Path Finding Graph Algorithms and Route Planning Energy-Efficient Navigation for Electric Vehicles Public and Multi-Modal Transport Routing Combinatorial Search and Optimization Sustainable Mobility and Green Logistics The analysis of his recent publications reveals a consistent trajectory in developing scalable, exact, and energy-aware routing algorithms. His work frequently appears in high-impact venues such as IEEE Transactions on Intelligent Transportation Systems and AAAI conferences, demonstrating strong technical rigor and relevance to real-world transportation challenges. A significant portion of his research contributes to UN Sustainable Development Goals related to sustainable cities and climate action. Notable scientific contributions include: Taxonomy and survey of eco-friendly route planning algorithms Efficient public transport routing using transfer connection databases Advancements in multi-agent path finding beyond pairwise constraints Exact and dynamic shortest path computation for electric vehicles Dr. Shen actively collaborates with leading researchers including Muhammad Aamir Cheema, Daniel Harabor, and Peter Stuckey. While no formal advising or grant information is available in the text, his publication volume and co-authorship network suggest active involvement in funded research projects and potential mentorship roles within the department. He is affiliated with a vibrant research group in data science and AI at Monash University, contributing to advancements in intelligent transportation systems and combinatorial search algorithms.

• Algorithms
• Graph Theory
• Dynamic Graph Algorithms
• +۳ مورد دیگر

Gramoz Goranci is an Assistant Professor in the Faculty of Computer Science, specializing in dynamic graph algorithms, optimization, and theoretical computer science. His research focuses on developing efficient algorithms for dynamic graphs, including problems such as shortest paths, facility location, and spectral sparsification. Research Interests: His work spans dynamic algorithms for planar graphs, approximation algorithms, graph sparsification, and optimization techniques. Key areas include incremental and decremental algorithms for network flow, facility location, and clustering problems. He explores the intersection of electrical flows, oblivious routing, and spectral methods to design efficient solutions for modern computational challenges. Publications: Goranci's recent work emphasizes dynamic graph algorithms, with contributions to Euclidean facility location, Steiner tree problems, and spectral hypergraph sparsification. His 2025 papers advance sublinear-time dynamic algorithms for bi-chromatic matching and transitive reduction, while 2024 research includes near-optimal shortest paths in planar graphs and high-dimensional facility location. Professional Contributions: He co-organized the 2024 Austrian Computer Science Day and contributed to the Vienna Graduate School on Computational Optimization (2016-2020). His research is supported by grants focusing on dynamic algorithms and graph sparsification. Labs/Groups: He leads the Theory and Applications of Algorithms research group, focusing on algorithmic foundations with applications in network analysis and optimization.

• Quantum Computing
• Machine Learning
• Algorithms
• +۴ مورد دیگر

Chunhao Wang is an Assistant Professor in the Department of Computer Science and Engineering at an unspecified university. His research focuses on quantum algorithms, optimal control of quantum systems, and their applications in computational chemistry and machine learning. He has active NSF-funded projects on quantum control and continuous-time open quantum systems. Active NSF Grants: FET Small (2023-2026), CAREER (2023-2028) His work spans quantum computing, algorithm design, and solving high-dimensional problems in optimization and statistical mechanics. Recent publications highlight quantum speedups for classical algorithms and efficient simulation of non-Markovian systems. Collaborations include researchers like Li, X. and Wu, X.

Yann Strozeckiمشاهده پروفایل

دانشیار

• Enumeration Complexity
• Algorithmic Game Theory
• Simple Stochastic Games
• +۷ مورد دیگر

Yann Strozecki is an Associate Professor (Maître de Conférences HDR) at the University of Versailles Saint-Quentin, where he is based in the DAVID Laboratory and leads the ALMOST research team focused on algorithms and stochastic models. He is currently on a part-time assignment at LIGM, Gustave Eiffel University, and has previously held positions at LIP6 (RO team), Paris-Sud University (ALGO team), and completed a postdoctoral fellowship at the University of Toronto's Theory Group. He earned his PhD from Paris Diderot (Paris 7) under Arnaud Durand. His research lies at the intersection of theoretical computer science and discrete mathematics, with core interests in: Enumeration complexity, especially delay and space constraints Algorithmic game theory, particularly simple stochastic games (SSGs) Graph and matroid algorithms Cheminformatics and molecular structure generation Sparse polynomials and algebraic complexity Analysis of his recent publications reveals a strong trend in developing efficient enumeration algorithms with provable delay and space bounds, advancing the theoretical foundations of output-sensitive computation. He also contributes to practical algorithms for Cloud RAN scheduling and cheminformatics, often combining theoretical rigor with real-world applications. His work on geometric amortization and strategy improvement in SSGs demonstrates innovation in algorithm design. Notable scientific contributions include: Generic strategy improvement methods for SSGs Polynomial-delay enumeration via closure operations Efficient deterministic scheduling for low-latency networks Tools for molecular cage generation in chemistry Yann Strozecki actively supervises PhD and master’s students, including Noé Demange, Maël Guiraud, and Xavier Badin de Montjoye. He co-organizes the ALMOST team seminar and has advised numerous interns in algorithmics and game theory. His research has been supported through collaborations with Nokia Bell Labs (CIFRE thesis) and interdisciplinary projects in cheminformatics and networking.

Surya Mathialaganمشاهده پروفایل

پژوهشگر ارشد

• Theoretical Computer Science
• Cryptography
• Algorithms
• +۳ مورد دیگر

Surya Mathialagan is a Research Fellow at the University of California, Berkeley 's Simons Institute . He will join NTT Research (CIS Lab) as a postdoctoral researcher in the fall. His research spans algorithms , theoretical cryptography , and computational complexity , with applications in blockchain verification and secure computation. Education: PhD in Theoretical Computer Science from Massachusetts Institute of Technology , advised by Vinod Vaikuntanathan and Virginia Vassilevska Williams . Research Interests focus on indistinguishability obfuscation, zero-knowledge proofs, and cryptographic protocols. His work addresses challenges in succinct verifiable computation , LWE-based security , and graph algorithm optimization . Recent publications at FOCS 2025 and Crypto 2025 explore obfuscation efficiency and adaptive proof systems. Scientific Awards include the Best Young Researcher Award at TCC 2023. His collaborations with experts like Abhishek Jain and Omer Paneth highlight interdisciplinary contributions.

Inge Li Gørtzمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Design and Analysis of Algorithms
• +۷ مورد دیگر

Inge Li Gørtz is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where she leads research in algorithms and data structures. She is affiliated with the Algorithms, Logic and Graphs section and has been a principal investigator on externally funded research projects. She also serves as chairman of the Danish Society for Computer Science since 2009. Research Interests: Her work centers on the design and analysis of algorithms, with a focus on approximation algorithms, pattern matching, graph algorithms, and compressed data representations. She investigates efficient query processing, string indexing, and algorithmic challenges in highly repetitive data, including applications in bioinformatics and data streams. The recent publications highlight a strong trend in theoretical and practical algorithm design, particularly in string indexing, compressed automata, and dynamic data structures. These works span venues like SOFSEM and STACS, emphasizing innovations in sublinear query time, sliding window models, and memory-efficient representations. Postdoc stipend from Carlsberg Foundation (2006–2008) for 'Approximation of Transportation on Demand Problems', kr 865,183 Advising and Grants: She actively supervises multiple PhD students on projects such as biomedical image segmentation using graph cuts, hierarchical compression of DNA data, and dynamic graph algorithms. She has served as main or co-supervisor in several funded PhD projects. She has been principal investigator on a Carlsberg Foundation grant and has participated in multiple Danish Council for Independent Research projects. Labs and Teams: She is a core member of the Algorithms, Logic and Graphs (ALG) section at DTU, contributing to a vibrant research environment focused on theoretical computer science and algorithmic problem solving.

Manolis Zampetakisمشاهده پروفایل

استادیار

• Theoretical Machine Learning
• Statistics
• Optimization
• +۴ مورد دیگر

Manolis Zampetakis is an Assistant Professor of Computer Science at Yale University. Previously, he was a postdoctoral researcher in the EECS Department at UC Berkeley working with Michael Jordan. He earned his Ph.D. and M.S. from the EECS Department at MIT, where he was advised by Constantinos Daskalakis. His undergraduate education was completed at the National Technical University of Athens (NTUA). His research interests span Theoretical Machine Learning, Statistics, Optimization, Computational Complexity, Game Theory, Mechanism Design, and Sublinear Algorithms. His work bridges theoretical computer science with practical applications in machine learning and economics. His research consistently appears in top-tier conferences such as STOC, FOCS, COLT, NeurIPS, and EC, with a strong focus on the theoretical foundations of machine learning and game theory. Zampetakis has received numerous prestigious awards including the ACM SIGEcom Doctoral Dissertation Award and the Google Ph.D. Fellowship. His publications from 2023-2025 demonstrate a consistent output of high-quality research across multiple domains, with a notable emphasis on bridging statistical theory with algorithmic approaches. His work shows increasing integration of robust statistics with mechanism design and game-theoretic approaches. ACM SIGEcom Doctoral Dissertation Award Google Ph.D. Fellowship Special Issue at SAGT 2015 Special Issue at WINE 2013 Second Prize in IMC 2012 Top 5 in Panhellenic Physics Competition 2008 Zampetakis actively advises multiple graduate students including Anay Mehrotra, Jane Lee, Vikram Kher, Katerina Mamali, Shuchen Li, and Nikolaos Koumpis. He has served on program committees and organized workshops including the Workshop on Algorithms for Learning and Economics (WALE 2019, WALE 2022). His research has been supported by grants from Google and likely other major funding agencies given his publication record in top venues. His research group focuses on theoretical aspects of machine learning with connections to statistics and game theory. He collaborates extensively with researchers across institutions including MIT, UC Berkeley, and Yale colleagues.

Paul Valiantمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۵ مورد دیگر

Paul Valiant is an Associate Professor of Computer Science at Purdue University, joining in Fall 2021. His research focuses on sublinear algorithms for big data analysis, statistical inference, and the 'unseen' portions of probability distributions. He explores machine learning methodologies, fluid dynamics structures, and cognitive neuroscience, investigating parallels between human brain functions and artificial intelligence. Education: Paul Valiant earned his PhD in Computer Science from MIT in 2008, focusing on testing symmetric properties of distributions. His academic background includes studies leading to his undergraduate degree, though specific details are not provided here. Research Interests: His work spans algorithms and computational complexity, sublinear algorithms for statistical inference, machine learning optimization, fluid dynamics analysis of vorticity and energy flow, and cognitive neuroscience exploring the cerebellum's role. He emphasizes algorithmic efficiency, instance optimality, and theoretical bounds in these domains. Key Contributions: Paul Valiant has developed instance-optimal algorithms, improved entropy estimation methods, and contributed to understanding neural network architectures. His fluid dynamics research employs computational simulations and mathematical analysis to uncover vorticity patterns. Awards: 2008 Best Student Paper Award (Theory of Cryptography Conference) 2019 Test of Time Award (for 'Incrementally Verifiable Computation') 2005 Best Student Paper Award (co-winner, FOCS) NeurIPS 2020 Oral Presentation ACM Best of Computing Notable Article (2014) Advising and Grants: While specific student names are not listed, his research has been supported by institutional grants and collaborations. His work bridges theoretical computer science with applied domains like neuroscience and fluid dynamics, reflecting interdisciplinary impact.

Madhu Sudanمشاهده پروفایل

استاد

• Theories of Computation and Communication
• Coding Theory
• Property Testing
• +۵ مورد دیگر

Madhu Sudan is the Gordon McKay Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS), where he also serves as Director of Graduate Studies. He holds a B.Tech. from IIT Delhi (1987) and a Ph.D. from UC Berkeley (1992). Prior to Harvard, he was at MIT (1997–2015), IBM Research (1992–1997), and Microsoft Research (2009–2015). His research focuses on the theories of computation and communication, with notable contributions to probabilistically checkable proofs (PCP theorem), list-decoding algorithms for error-correcting codes, and sublinear-time algorithms. Education: B.Tech., Indian Institute of Technology Delhi, 1987 Ph.D., University of California, Berkeley, 1992 Research Interests: Communication and computing under errors Coding theory and its applications Property testing and sublinear algorithms Algebraic methods in computation Awards and Honors: Nevanlinna Prize (2002) IEEE Hamming Medal (2016) Member, National Academy of Sciences (2017) Infosys Prize in Mathematical Sciences (2018) Fellow of ACM, IEEE, and AMS Teaching and Service: Current courses: Algebra and Computation , Coding Theory Directed Graduate Studies in Computer Science at Harvard Editorial roles: Foundations and Trends in Theoretical Computer Science , Theory of Computing Research Groups: Member of Harvard's Theory of Computation group, collaborating on projects in coding theory, complexity theory, and distributed computing.

Anindya Deمشاهده پروفایل

دانشیار

• Complexity Theory
• Analysis of Boolean Functions
• Learning Theory
• +۳ مورد دیگر

Anindya De is an Associate Professor and Graduate Chair in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. Previously, he served as an Assistant Professor at Northwestern University (2015–2018) and held postdoctoral roles at the Institute for Advanced Study (IAS), DIMACS, and the Simons Institute at UC Berkeley. He earned his Ph.D. in Computer Science from UC Berkeley (2013) and a B.Tech. from the Indian Institute of Technology Kanpur (2008). His research focuses on complexity theory, analysis of Boolean functions, learning theory, and applied probability. Notable contributions include work on property testing, trace reconstruction, and algorithmic foundations. He has organized TCS+, an online seminar series in theoretical computer science, and served on program committees for conferences like FOCS, COLT, and STOC. Key achievements include the IBM Pat Goldberg Memorial Math/CS/EE Best Paper Award (2014) and a Best Student Paper award at TCC 2012. His work bridges theoretical computer science with practical applications in stochastic optimization and pseudorandomness.