جست‌وجوی استاد و پژوهشگر

Valerie Kingمشاهده پروفایل

استاد

• Graph Algorithms
• Randomized Algorithms
• Probabilistic Analysis
• +۳ مورد دیگر

Valerie King is a Professor in the Department of Computer Science at the University of Victoria, Canada. Her research focuses on graph algorithms, randomized algorithms, probabilistic analysis, and fault-tolerant distributed computing. She holds a PhD from UC Berkeley. Her work emphasizes scalable and secure distributed systems, with contributions to consensus protocols, network algorithms, and adversarial resilience. Dr. King's research bridges theoretical foundations and practical applications in computing systems. Education: PhD in Computer Science from UC Berkeley. Research Interests: Graph algorithms, randomized algorithms, fault-tolerant distributed computing, and algorithmic defense mechanisms against cyberattacks. Her work often addresses challenges in network security, consensus protocols, and efficient distributed algorithms. Key research trends in her articles include Byzantine agreement protocols, communication-efficient distributed algorithms, and sublinear-time graph algorithms. She explores geometric network models, secure multi-party computation, and energy-efficient communication strategies. Her contributions span algorithm design, complexity analysis, and practical implementations in adversarial environments. Advising and Grants: While specific student names or grant details are not listed here, her extensive publication record reflects sustained research activity in distributed systems and algorithms. She contributes to foundational research in theoretical computer science with applications to real-world distributed networks. Labs/Teams: Affiliated with the Computer Science department at UVic, collaborating on projects related to distributed computing and algorithmic security. Her work often involves interdisciplinary approaches to network resilience and computational efficiency.

Michael Kapralovمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Sublinear Algorithms
• Big Data Analysis
• +۹ مورد دیگر

Michael Kapralov is an Associate Professor in the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL) , where he leads research at the Theory Group . His work spans multiple departments including the Laboratory of Theory of Computation 4 and the Doctoral Program in Computer Science and Communications . Kapralov's research focuses on theoretical computer science , particularly sublinear algorithms for big data analysis , with applications in streaming , sketching , sparse recovery , and Fourier sampling . University: EPFL School: School of Computer and Communication Sciences Department: Theory Group Academic Rank: Associate Professor Education: Kapralov earned his Ph.D. in Computer Science from Stanford iCME under the supervision of Ashish Goel . He subsequently held postdoctoral positions at the Mit CSAIL Theory of Computation Group with Piotr Indyk and as a Herman Goldstine Postdoctoral Fellow at IBM T. J. Watson Research Center . Ph.D.: Stanford iCME (2012), advisor: Ashish Goel Postdoctoral: MIT CSAIL (2012-2014), IBM Watson (2014) Research Interests: Kapralov's work addresses fundamental challenges in processing large-scale data through rigorous mathematical models. His contributions include advancements in sublinear algorithms , streaming complexity , spectral sparsification , sparse Fourier transforms , and differential privacy . He has developed techniques for dimension-independent signal processing , kernel ridge regression , and graph spanners , with theoretical guarantees and practical implications for machine learning and data analysis. Scientific Awards: Kapralov received the ERC Starting Grant SUBLINEAR (2018-2023) and the Gene H. Golub Dissertation Award (2012). Advising: He has supervised numerous Ph.D. students and postdoctoral researchers, including Ekaterina Kochetkova , Grzegorz Gluch , Kshiteej Sheth , and Amir Zandieh , many of whom have taken academic or industry positions at institutions like UC Berkeley, National University of Singapore, and Google Zurich. Collaborations and Teaching: Kapralov co-organizes the Turing Course for high school students, leads the Reading Group on Foundations of Deep Learning , and contributes to academic initiatives such as Theory Coffee and the Swiss Winter School on Theoretical Computer Science . He teaches courses like Sublinear Algorithms for Big Data Analysis and Algorithms II , focusing on advanced algorithm design and analysis.

Tom Gurمشاهده پروفایل

استاد

• Theoretical Computer Science
• Quantum Computing
• Classical and Quantum Complexity Theory
• +۶ مورد دیگر

Tom Gur is a Professor in the Department of Computer Science and Technology at the University of Cambridge, with research affiliations in the Algorithms and Complexity and Quantum Computing groups. His work bridges theoretical computer science and quantum computation, supported by major grants including the UKRI Future Leaders Fellowship and ERC Starting Grant . Research Focus : Tom Gur's research centers on Quantum Complexity Theory , Sublinear Algorithms , and Coding Theory , with deep connections to mathematical domains like Harmonic Analysis and Additive Combinatorics . His work explores the interplay between classical and quantum computational models, emphasizing Interactive Proofs , Zero-Knowledge Systems , and Quantum Learning Algorithms . Article Trends reveal a focus on quantum advantage, complexity theory, and cryptographic protocols. Key themes include probabilistically checkable proofs , quantum generalization bounds , and sublinear verification systems across venues like STOC, FOCS, and QIP. Scientific Awards : UKRI Future Leaders Fellowship Advising includes supervision of current PhD and Master's students like Hugo Aaronson and Jack O'Connor , while alumni such as Marcel Dall'Agnol (Princeton) and Aditya Jain (PsiQuantum) reflect his mentorship impact. Professional leadership includes organizing STOC workshops and serving on editorial boards for Quantum and SICOMP .

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

• 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.

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.

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.

• Distributed Computing
• Communication Complexity
• Quantum Computing
• +۴ مورد دیگر

Rotem Oshman is affiliated with Tel-Aviv University, Israel , where he contributes to research in theoretical computer science, focusing on distributed computing, communication complexity, and quantum algorithms. His work addresses fundamental challenges in network protocols, concurrent data structures, and cryptographic verification.

• Proof Complexity
• Computational Logic
• Parameterized Complexity
• +۸ مورد دیگر

Dr. Stefan Dantchev is an Assistant Professor in the Department of Computer Science at Durham University. His work focuses on theoretical computer science, particularly proof complexity, computational logic, and parameterized complexity. He has contributed extensively to research on propositional proof systems like Resolution, Sherali-Adams, and Lovász-Schrijver, analyzing their limitations and capabilities through combinatorial principles and optimization techniques. His research also explores sublinear-time algorithms, simplicial complex entropy, and dynamic cellular automata models. His research interests are centered around proof systems and their computational boundaries, including topics such as binary encoding of combinatorial problems, complexity gaps in QBF Resolution, and the interplay between cutwidth parameters and optimization algorithms. He has investigated foundational questions in computational logic, such as relativization effects on contradiction proofs and the axiomatisation of PageRank in edge-weighted networks. Dr. Dantchev’s publications span conferences like LATIN, COCOON, and workshops such as STOC and FOCS, alongside journals in computational complexity and combinatorial optimization. His work demonstrates a persistent engagement with core theoretical challenges in computer science, particularly in proof systems and algorithmic efficiency.

Ronitt Rubinfeldمشاهده پروفایل

استاد

• Theory of Computation
• Algorithms
• Randomized Algorithms
• +۳ مورد دیگر

Ronitt Rubinfeld is the Edwin Sibley Webster Professor of Electrical Engineering and Computer Science at MIT, affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). She holds a PhD from UC Berkeley (1991) and a BSE in Electrical and Computer Engineering from the University of Michigan. Prior to MIT, she was at Cornell University and NEC Research Laboratories. Her research focuses on randomized and sublinear-time algorithms, exploring how to understand data with minimal sampling. Notable roles include ONR Young Investigator, Sloan Fellow, and Radcliffe Fellow. She advises numerous PhD and master’s students, contributing to fields like algorithmic theory and data science. Education: PhD UC Berkeley (1991), BSE University of Michigan. Affiliations: MIT CSAIL, Theory of Computation Group, Algorithms Group. Research Interests: Sublinear algorithms, randomized algorithms, distribution testing, and complexity theory. Her work bridges theoretical foundations with practical applications in big data analysis. Awards: ONR Young Investigator, Sloan Research Fellowship, Cornell Teaching Award, Radcliffe Fellowship. Advising: Supervised 20+ PhD students (e.g., Funda Ergun, Badih Ghazi) and 10+ master’s students. Current advisees include Amartya Biswas and Ted Pyne. Grants include NSF funding for distribution testing and sublinear algorithms. Labs/Teams: Core member of MIT Theory of Computation group, CSAIL Algorithms Group, and collaborator on projects like WOLA (Workshop on Sublinear Algorithms).

Moses Charikarمشاهده پروفایل

استاد

• Algorithms
• Machine Learning
• Approximation Algorithms
• +۴ مورد دیگر

Moses Charikar is the Donald E. Knuth Professor of Computer Science at Stanford University, with a courtesy appointment in Mathematics. He earned his B.Tech. from the Indian Institute of Technology Bombay (1995) and a Ph.D. from Stanford University (2000). His research focuses on high-dimensional data processing, machine learning optimization, approximation algorithms, and metric embeddings. Charikar has held faculty positions at Princeton University (2001–2015) and spent time at Google Research. Research interests include algorithmic techniques for massive data sets, convex optimization for non-convex problems, and low-distortion metric embeddings. His work has led to influential contributions like locality-sensitive hashing (LSH), earning the Paris Kanellakis Award (2012) and ACM Fellowship (2021). He has also received the Simons Investigator award (2014) and multiple best-paper accolades at top conferences. Awards: Donald E. Knuth Professorship (Stanford) 2012 Paris Kanellakis Theory and Practice Award ACM Fellow (2021) Simons Investigator in Theoretical Computer Science (2014) Charikar’s research bridges theoretical computer science and practical applications, with notable contributions to clustering, approximation algorithms, and data analysis. His publications span algorithm design, computational geometry, and machine learning theory.