جست‌وجوی استاد و پژوهشگر

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.

Nima Anariمشاهده پروفایل

استادیار

• Algorithms
• Probability
• Combinatorics
• +۶ مورد دیگر

Nima Anari is an Assistant Professor of Computer Science at Stanford University , where he works on theoretical computer science with a focus on algorithms, probability, and combinatorics. He is a member of the Theory Group and has been hosted by Amin Saberi during his postdoctoral research. Ph.D. in Computer Science from UC Berkeley (2017), advised by Satish Rao B.Sc. in Computer Engineering and Mathematics from Sharif University of Technology His research primarily centers around: Sampling algorithms and Markov chains High-dimensional expanders Geometry of polynomials Parallel computing techniques Applications to determinantal point processes His recent publications indicate strong trends in: Parallel sampling methods (2024 STOC, COLT) High-dimensional mixing time analysis (2023 STOC, FOCS) Polynomial-based algorithm design (2021 STOC, 2020 FOCS) Scientific recognition includes: STOC Best Paper Award (2019) Google Faculty Research Award NSF CAREER Award Sloan Research Fellowship Michael and Sheila Held Prize

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.

• Algorithms and Complexity
• Theoretical Computer Science
• Probability Theory
• +۷ مورد دیگر

Dr. John A Lapinskas is a Senior Lecturer in the School of Computer Science at the University of Bristol, where he conducts research in theoretical computer science and discrete mathematics. His work is centered on algorithmic foundations, probabilistic models, and networked systems. His research interests include: Algorithms and Complexity Probability Theory in Computing Random Graphs and Spatial Networks Contention Resolution and Distributed Protocols Sublinear and Randomized Algorithms The recent publications of Dr. Lapinskas reflect a strong focus on theoretical models of computation and communication, particularly in distributed and stochastic environments. His work often bridges computer science and mathematical probability, with applications in network stability, graph algorithms, and protocol design. Key themes include the analysis of backoff protocols, first-passage percolation, and oracle-based estimation in hypergraphs. He collaborates with researchers across Europe and contributes to peer-reviewed journals and conferences in theoretical computer science. Dr. Lapinskas has authored or co-authored multiple publications in recent years, with a consistent output in high-quality venues such as the Journal of Computer and System Sciences and the Electronic Journal of Probability. His research is openly accessible and contributes to foundational knowledge in algorithm design and analysis. While no formal advising or grant details are listed, his active collaboration network and recent publications indicate ongoing research leadership. He is also associated with datasets related to contact-tracing applications, showing applied interest in public health modeling through computational methods.

Piotr Indykمشاهده پروفایل

استاد

• Algorithms
• Theoretical Computer Science
• Data Mining
• +۳ مورد دیگر

Piotr Indyk is a Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), School of Engineering, specializing in efficient algorithms for high-dimensional geometric problems and massive data analysis. His seminal contributions include: Co-inventing locality sensitive hashing for approximate nearest neighbor search, revolutionizing applications in data mining and computer vision by enabling similarity detection without full dataset scans. Pioneering sublinear algorithms for massive data streams that operate with minimal memory, addressing scalability challenges in big data. Developing accelerated sparse Fourier transform algorithms that outperform FFT for signals with sparse spectral representations.

• Analysis of Algorithms
• Data Analysis
• Massive Data Sets
• +۱۵ مورد دیگر

Robert Krauthgamer is the Harry Weinrebe Professor of Computer Science and currently serves as Department Head in the Department of Computer Science & Applied Mathematics at the Weizmann Institute of Science , within the Faculty of Mathematics and Computer Science . He is a leading researcher in theoretical computer science, particularly in the analysis of algorithms. Research Interests: His research focuses on Analysis of Algorithms , with deep expertise in Data Analysis and Massive Data Sets , Combinatorial Optimization , Approximation Algorithms , Hardness of Approximation , Embeddings of Finite Metrics , and Routing and Peer to Peer Networks . He also maintains a broad interest in Discrete Mathematics and High-Dimensional Geometry . His recent publications highlight work in graph algorithms, parameterized complexity, streaming algorithms, and metric embeddings. Publication Trends: His most recent work, including papers from SODA 2016, demonstrates a strong trend in the design and analysis of efficient algorithms for fundamental problems in graph theory, optimization, and data streams. Key themes include kernelization and sampling techniques for dynamic graph streams, subexponential parameterized algorithms, deterministic derandomization of the polynomial method, and structural results for graph modification problems. His research often bridges theoretical insights with applications in computational biology and network science. Service and Recognition: Journal Editorial: Editor-in-Chief of SIAM Journal on Computing (2019–2025), Associate Editor (2012–2017); Managing Editor of Theory of Computing (2007–2018), and current Editorial Board Member. Conference Leadership: Program Committee Chair for SODA 2016 and HALG 2018; Steering Committee member for SODA, ESA, and HALG; and committee member for the Gödel Prize (2019–2021). Workshops: Organizer of numerous workshops on sublinear algorithms, fine-grained complexity, and high-dimensional data. Teaching and Mentorship: He regularly teaches advanced courses such as Randomized Algorithms and Sublinear Time and Space Algorithms . He advises a large group of MSc and PhD students and hosts postdoctoral researchers, demonstrating a strong commitment to training the next generation of computer scientists. His former students have gone on to successful academic and research careers. Laboratories and Research Groups: He is a key member of the Foundations of Computer Science (theory) seminar at Weizmann and has organized the TheoryLunch and Reading Group in Algorithms, fostering a vibrant research community within the department.

Xin Liمشاهده پروفایل

دانشیار

• Theory of Computation
• Randomness Extractors
• Pseudorandomness
• +۶ مورد دیگر

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

Christian Scheidelerمشاهده پروفایل

استاد

• Distributed Algorithms
• Data Structures
• Security in Distributed Systems
• +۴ مورد دیگر

Christian Scheideler is a Professor at the Institute for Computer Science (Theory of Distributed Systems) within the Faculty of Electrical Engineering, Computer Science, and Mathematics at the University of Paderborn. He serves as institute director, advisory committee member of SPAA, and associate editor for Journal of the ACM and Journal of Computer and System Sciences . His editorial and leadership roles extend to managing Journal of Interconnection Networks and steering committees for conferences like DISC, SIROCCO, and ALGOSENSORS. Director, Institute for Computer Science Advisory Committee, SPAA Associate Editor, Journal of the ACM and Journal of Computer and System Sciences Managing Editor, Journal of Interconnection Networks Steering Committee member for DISC, SIROCCO, ALGOSENSORS His research focuses on distributed algorithms, data structures, security in distributed systems, randomized algorithms, stochastic processes, network theory, and discrete mathematics . Notably, he investigates hybrid programmable matter systems, self-stabilizing overlay networks, and robust distributed protocols under adversarial conditions. Recent publications (2021-2025) highlight work on 3D programmable matter shape formation , low-diameter graph decompositions , reconfigurable circuits in amoebot models , and blockchain lightweight state replication . Topics span distributed computing, computational geometry, and network security. Scientific awards include the 2022 Edsger W. Dijkstra Prize in Distributed Computing for foundational work in self-stabilization and overlay networks. He has served as PC Chair (DISC 2022) and organized numerous conferences (SPAA, SSS, SIROCCO).

• Numerical Analysis
• Computational Mathematics
• Sparse Signal Processing
• +۴ مورد دیگر

Dr. Toni Volkmer is a researcher affiliated with the Faculty of Mathematics at Chemnitz University of Technology. His work focuses on computational mathematics and signal processing, particularly in high-dimensional data analysis and sparse Fourier transforms. His research intersects numerical analysis, approximation theory, and algorithm design for efficient data processing. Key research areas include Sparse spectral estimation Rank-1 lattice sampling Multivariate function approximation High-dimensional data analysis Fast matrix-vector operations Graph Laplacian computations His publications demonstrate expertise in developing sublinear-time algorithms for harmonic analysis and creating efficient numerical methods applicable to modern data science challenges. While no formal awards are listed, his contributions to scientific computing have been documented in multiple peer-reviewed publications since 2012.

• Machine Learning
• Online Learning
• Computational Game Theory
• +۳ مورد دیگر

Andrea Celli is an Assistant Professor in the Department of Computing Sciences at Bocconi University, where he is a member of the theory group and the Bocconi Institute for Data Science and Analytics. He previously spent one year as a postdoctoral researcher with the Core Data Science team at Meta (London). He obtained his Ph.D. in Computer Science from Politecnico di Milano, where he was advised by Nicola Gatti, and was a Visiting Researcher at Carnegie Mellon University working with Tuomas Sandholm. His educational background includes: Ph.D. in Computer Science from Politecnico di Milano, advised by Nicola Gatti Visiting Researcher at Carnegie Mellon University with Tuomas Sandholm Postdoctoral researcher with the Core Data Science team at Meta (London) Andrea Celli's research focuses on problems at the intersection of computer science, machine learning, and economics, with particular interest in computational problems arising from strategic interactions in online settings where learning processes and incentives are fundamentally intertwined. His work spans machine learning theory, online learning algorithms, computational game theory, and algorithmic economics. His research has been published in top venues including NeurIPS, ICML, STOC, and EC, with a best paper award at NeurIPS 2020. Analysis of his recent publications shows a strong focus on online learning algorithms with constraints, bandit problems, game-theoretic aspects of machine learning, and algorithmic game theory. His work often bridges theoretical foundations with practical applications in economics and decision-making systems. Key themes include no-regret learning, Bayesian persuasion, knapsack problems in online settings, and equilibrium computation in extensive-form games. His scientific achievements include: Best paper award at NeurIPS 2020 ERC Starting Grant (2024) for the project PLA-STEER MUR-PRIN grant (2022) Andrea Celli actively mentors students and postdocs, currently supervising Riccardo Poiani, Sarah Sachs, and Martino Bernasconi as postdocs, and Annalisa Barbara, Emanuele Coccia, Davide Drago (now at Bending Spoons), and Antonio Preiti (now at Amazon) as MSc students. His research is supported by competitive grants including an ERC Starting Grant and a MUR-PRIN grant, indicating strong recognition of his work's potential impact. He is also a member of the ELLIS Society, connecting him to a broader European network of excellence in AI research. He is affiliated with the theory group and the Bocconi Institute for Data Science and Analytics at Bocconi University, contributing to a vibrant research environment focused on theoretical foundations of data science and their applications.

Antonia Kalbمشاهده پروفایل

پژوهشگر

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

Antonia Kalb is a researcher at Chair 11: Algorithm Engineering within the Department of Computer Science at Technical University of Dortmund. She works under the supervision of Prof. Maike Buchin and Dr. Bernd Zey in the field of computational geometry and algorithm design. Her research focuses on: Algorithms Graph Theory Computational Geometry Matching Problems Property Testing Sublinear Algorithms Antonia has made significant contributions to geometric algorithms, particularly in graph augmentation techniques with maximal matchings. Her research bridges theoretical computer science with practical applications in discrete geometry and optimization problems. She has taught exercises for 'Data Structures, Algorithms and Programming 2' during summer semesters 2019 and 2022, demonstrating her commitment to computer science education at the undergraduate level.

Domagoj Matijevićمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Bioinformatics
• +۵ مورد دیگر

Domagoj Matijević is an Associate Professor at the School of Applied Mathematics and Informatics within Josip Juraj Strossmayer University of Osijek . His academic career spans computational geometry, optimization algorithms, and bioinformatics applications. PhD in Computer Science (Algorithms and Complexity), Max-Planck-Institute for Computer Science, Saarbrücken (2007) MS in Computer Science, Saarland University (2002) BS in Mathematics and Computer Science, University of Osijek (2001) Research interests focus on Machine Learning , Computational Geometry , and Bioinformatics , particularly through software tools like Fortuna for RNA splicing analysis and Trajan for comparing single-cell trajectories. His work bridges theoretical computer science with practical implementations in C++ , Python , and CUDA for high-dimensional data processing. 2023 Best Paper Award at MIPRO's Artificial Intelligence Systems track Key contributor to Neural Network-Based Pollen Prediction and Well-Separated Pair Decomposition implementations Developed Trajan for dynamic pseudotime warping and Fortuna for novel splicing event detection Currently teaches Algorithm Complexity , Computational Geometry , and Embedded Systems . Past projects include NVIDIA-funded GPU implementations and German-Croatian collaborations on kinetic spanners.

• Theoretical Computer Science
• Computational Complexity
• Data Structures
• +۳ مورد دیگر

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

• Stochastic Modeling of Communication, Information and Service Systems
• Queueing Theory
• Stochastic Processes
• +۹ مورد دیگر

Alexander Stolyar is Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign , where he has taught courses on stochastic processes, queueing systems, and service engineering. Before joining Illinois in January 2017, he was the Timothy J. Wilmott Endowed Chair Professor at Lehigh University (2014–2017) and spent nearly two decades in industrial research at Bell Laboratories , Motorola, and AT&T Labs-Research. Stolyar’s research centers on stochastic modeling of large-scale communication, information, and service systems , including cloud data centers, communication networks, and call/contact centers. He develops mathematical frameworks for dynamic resource allocation, scheduling, congestion control, and distributed algorithms , leveraging tools from queueing theory, stochastic processes, fluid and diffusion limits, large deviations, and mean-field analysis . His recent publications (2021–2025) reveal sustained focus on asymptotic optimality in complex systems: designing lightweight algorithms that become provably optimal as system scale grows. Key themes include greedy and randomized policies for inventory systems with stochastic lead times , parallel-server systems with redundancy and cancel-on-completion , particle systems with mean-field interactions exhibiting traveling-wave behavior , and packing-constrained service systems motivated by cloud data-center operations . Scientific honors include election as INFORMS Fellow (2021) , the INFORMS Applied Probability Society Best Publication Award (2004) , and the ACM SIGMETRICS Best Simulation Paper Award (1996) . He serves or has served on the editorial boards of Queueing Systems (Editor, 2008–present), IEEE Transactions on Information Theory (2019–2021), Operations Research (2006–2011), Advances in Applied Probability / Journal of Applied Probability (2008–2015), and as Technical Program Committee member for leading conferences such as SIGMETRICS and IFIP Performance . Stolyar’s research has generated more than 30 U.S. patents spanning wireless network scheduling, cloud resource allocation, dynamic load balancing, and packet-data network control—many of which have been incorporated into commercial cellular and cloud systems. His work is supported by NSF and industry grants, and he has advised numerous PhD students and postdocs who now hold faculty and research positions worldwide.

• Analysis of Algorithms
• Data Analysis
• Massive Data Sets
• +۱۰ مورد دیگر

Robert Krauthgamer holds The Harry Weinrebe Professorial Chair of Computer Science at The Weizmann Institute of Science, where he also serves as Department Head of the Department of Computer Science & Applied Mathematics within the Faculty of Mathematics and Computer Science. His distinguished career includes editorial leadership as Editor-in-Chief of SIAM Journal on Computing (2019-2025) and significant service on numerous program committees for major theoretical computer science conferences. Dr. Krauthgamer's primary research focuses on Analysis of Algorithms , with particular expertise in Data Analysis and Massive Data Sets, Combinatorial Optimization, Approximation Algorithms and Hardness of Approximation, Average-case Analysis and Heuristics, Embeddings of Finite Metrics, and Routing and Peer to Peer networks. His work also spans broader areas of Discrete Mathematics and High-Dimensional Geometry, reflecting his deep mathematical approach to algorithmic problems. His recent publications reveal a strong emphasis on sparsification techniques, high-dimensional computational geometry, streaming algorithms, and graph-theoretic problems. Throughout his career, Krauthgamer has supervised numerous graduate students at both MSc and PhD levels, with recent theses addressing cutting-edge topics in algorithm design and analysis. His service contributions include steering committee roles for major conferences like SODA, ESA, and HALG, as well as committee membership for the prestigious Gödel Prize (2019-2021). As an educator, he regularly teaches advanced courses including Randomized Algorithms and Sublinear Time and Space Algorithms, often in collaboration with other leading researchers. He also organizes Weizmann's TheoryLunch and has coordinated the Foundations of Computer Science seminar for many years, demonstrating his commitment to fostering academic community and intellectual exchange.