جست‌وجوی استاد و پژوهشگر

• Algorithms
• Data Structures
• Big Data
• +۵ مورد دیگر

Rasmus Pagh is a Professor in the Algorithms and Complexity section of the Computer Science Department at the University of Copenhagen, Denmark. He is an ACM Fellow and a core researcher at the BARC (Basic Algorithms Research Copenhagen) center, with additional affiliation at the DeLTA Lab. From January 2019 to December 2020, he served as a visiting faculty researcher at Google Research. His primary research interests focus on algorithms and data structures with emphasis on big data applications. His work spans information retrieval, the role of randomness in computing, database applications, knowledge discovery, and more recently, differential privacy. His research has practical implications across multiple domains requiring efficient data processing solutions. Pagh's recent work shows a strong emphasis on differential privacy, particularly exploring correlated noise mechanisms and multiple release scenarios that maintain privacy guarantees while maximizing utility. His research demonstrates how to create multiple privacy-preserving releases without degrading overall privacy protection. ACM Fellow As an active supervisor, Pagh currently mentors four PhD students (Sia Sejer, Lukas Retschmeier, Joel Andersson, and Johanna Düngler) and has successfully graduated twelve PhD students whose careers now span academia and industry at organizations including Akamai Technologies, Microsoft, and Oracle. He serves on numerous prestigious program committees including STOC, FOCS, and ICALP, and has held leadership roles such as steering committee chair for the European Symposium on Algorithms (ESA) from 2022-2024. His research is centered at BARC (Basic Algorithms Research Copenhagen) where he leads work on fundamental algorithmic problems with applications to real-world data challenges. The center provides a collaborative environment for theoretical and applied algorithm research.

Seth Pettieمشاهده پروفایل

استاد

• Algorithms
• Graph Theory
• Distributed Computing
• +۳ مورد دیگر

Seth Pettie is a faculty member in the Department of Electrical Engineering and Computer Science (EECS) at the University of Michigan. His research focuses on algorithms, graph theory, distributed computing, and data structures, with significant contributions to problems like minimum spanning trees, Davenport-Schinzel sequences, and energy complexity in radio networks. Research Interests : Algorithms, graph theory, distributed systems, combinatorics, and computational complexity. Students : Mentored numerous PhD students and postdocs, including Dingyu Wang, Shang-En Huang, and Yi-Jun Chang, some of whom have won prestigious awards like the Principles of Distributed Computing Doctoral Dissertation Award. Scientific Contributions : Authored over 15 recent articles (2021–2025) on topics spanning connectivity labeling, fraud detection, extremal combinatorics, and energy-efficient distributed algorithms. His work often bridges theoretical insights with practical applications in databases and network optimization. Awards : Recipient of the Outstanding Dissertation Award (2004) and Best Student Paper Award at ICALP 2002. His students have also received recognition for their work. Professional Service : Organized workshops (e.g., Dagstuhl, Bertinoro) and served on steering/editorial/program committees for major conferences like SODA, STOC, and PODC.

• 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.

Pavel Dvurechenskyمشاهده پروفایل

پژوهشگر ارشد

• Optimization algorithms
• Stochastic optimization
• Optimal transport
• +۷ مورد دیگر

Dr. Pavel Dvurechensky is a Research Fellow at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) since 2015 and a member of Math+, the Berlin cluster of excellence. His research focuses on theoretical and applied aspects of optimization algorithms, particularly first- and second-order methods for convex and non-convex large-scale problems. 2023: Habilitation in mathematics at Humboldt University Berlin 2014-2015: Research assistant at Institute for Information Transmission Problems, Moscow 2009-2015: Junior researcher at Moscow Institute of Physics and Technology His work addresses stochastic optimization, optimal transport, distributed computing, and applications in machine learning, energy systems, and traffic modeling. He has contributed to complexity analysis of mirror descent variants, barrier methods for non-convex problems, and high-probability bounds in heavy-tailed noise scenarios. Recent publications appear in top venues like ICML, NeurIPS, and Mathematical Programming. Selected Scientific Awards: Habilitation in mathematics (2023) He has taught courses on modern optimization at Humboldt University and Higher School of Economics, covering gradient methods, mirror descent, and optimal transport. Collaborations include projects with Math+ cluster on energy system equilibria and brain signal analysis.

• Machine Learning
• High-Dimensional Statistics
• Reinforcement Learning
• +۴ مورد دیگر

Martin J. Wainwright is the Cecil H. Green Professor at the Massachusetts Institute of Technology (MIT) , affiliated with the Department of Electrical Engineering and Computer Science (EECS) and the Department of Mathematics . He is also associated with the Statistics and Data Science Center , the Laboratory for Information and Decision Systems , and the Institute for Data, Systems and Society . His research bridges machine learning , high-dimensional statistics , and information theory , with a focus on theoretical guarantees for algorithms in reinforcement learning, optimization, and graphical models. Books : High-Dimensional Statistics: A Non-Asymptotic Viewpoint (2019, Cambridge University Press), Statistical Learning with Sparsity: The Lasso and Generalizations (2015, CRC Press). Research Themes : Statistical and computational trade-offs, robustness in adaptive learning, posterior contraction rates, and decentralized estimation. His recent work explores non-asymptotic analysis , stochastic approximation , and instance-dependent guarantees in reinforcement learning and optimization. Key contributions include minimax optimality in value estimation, variance-reduced Q-learning , and adaptive inference under elliptical constraints. Awards : IMS Medallion Lecturer , COPSS Presidents' Award , Loève Prize in Probability , Fellow of the Institute of Mathematical Statistics , NIPS Outstanding Paper Award .

Marina Miloševićمشاهده پروفایل

دانشیار

• Medical Image Processing
• Breast Cancer Detection
• Biomedical Engineering
• +۷ مورد دیگر

Dr. Marina Milošević is an Associate Professor at the Department of Computer and Software Engineering, Faculty of Technical Sciences in Čačak, University of Kragujevac. She specializes in medical image processing, particularly breast cancer detection using thermography and mammography, along with database systems and human-computer interaction. Her work bridges computer engineering with biomedical applications, focusing on practical implementations for healthcare improvement. Her educational background includes a Bachelor's degree in Computer Engineering from the Technical Faculty in Čačak (2010, GPA 9.47) and a PhD from the Faculty of Electronics, University of Niš (2016). She has received multiple awards for academic excellence and was a scholarship recipient from several prestigious institutions including the Fund for Young Talents and the Ministry of Education of Serbia. Dr. Milošević's research interests focus on medical image processing, particularly breast cancer detection using thermography and mammography. She has developed systems for remote mammogram processing and comfort level classification during patient transport. Her work also extends to database systems, geographic information systems, and human-computer interaction. She holds multiple professional certifications in database technologies from INTERGRAPH and ORACLE ACADEMY. Her publication record shows a strong trend toward interdisciplinary research combining computer engineering with medical applications. Recent work has expanded into transportation systems, earthquake detection using mobile phones, and agile software development methodologies. Her research consistently focuses on practical implementations with real-world applications, particularly in healthcare diagnostics and monitoring. Scholarship recipient of the Fund for Young Talents Scholarship recipient of the University of Kragujevac Scholarship recipient of the Ministry of Education of the Republic of Serbia Vuk Karadžić diploma for exceptional academic performance Dr. Milošević teaches across all academic levels, from Bachelor's courses in Fundamentals of Computer Engineering and Databases to PhD-level courses on Virtual Reality Applications in Medicine and Biomedical Image Processing. Her work on a patent for a random number generator based on Geiger-Muller counter technology demonstrates her innovative approach to solving engineering problems. She has been actively involved in adapting engineering education for remote learning during the pandemic. She leads research in the Computer Science Laboratory at the Faculty of Technical Sciences, focusing on medical image processing systems and database applications. Her recent work includes developing web-based systems for remote mammogram processing and analyzing vehicle vibration signals for comfort assessment. She collaborates with researchers across multiple institutions on interdisciplinary projects that bridge computer engineering with healthcare applications.

• Combinatorial Optimization
• Distributed and Parallel Computing
• Coding Theory
• +۴ مورد دیگر

Bernhard Haeupler is a Professor at INSAIT (Institute for Computer Science, Artificial Intelligence and Technology) under Sofia University "St. Kliment Ohridski" since 2024. He is also a Post-Professor and Researcher at ETH Zurich 's Computer Science Department since 2020 and an Adjunct Professor at Carnegie Mellon University 's Computer Science Department since 2022. His roles span theoretical computer science, algorithm design, and distributed/parallel systems. Research Interests : Haeupler focuses on algorithms for combinatorial optimization, distributed and parallel computing, coding theory, graph/network algorithms, and information theory. His work bridges theoretical foundations with practical applications in network resilience, synchronization, and quantum/parallel optimization. Article Trends : His 15 most recent articles (2023-2025) emphasize distributed algorithms (e.g., dynamic routing, fault-tolerant spanners), parallel computing (e.g., low-congestion shortcuts, near-linear work), and coding theory (e.g., synchronization strings, insertion-deletion codes). Topics include graph theory , network design , and quantum shortest paths . Scientific Awards : ERC Starting Grant (2020) Sloan Research Fellow (2019) NSF CAREER Award (2018) ACM-EATCS Doctoral Dissertation Award (2014) George Sprowls Award for MIT PhD Thesis Advising & Grants : Haeupler has advised eight PhD students, including Jason Li (CMU, now at Google Research Zurich) and Nicolas Resch (University of Amsterdam). He has secured multiple NSF grants for projects like Distributed Optimization Beyond Worst-Case Topologies and New Coding Techniques for Synchronization Errors .

Peter Manoharمشاهده پروفایل

پژوهشگر

• Theoretical Computer Science
• Algorithms
• Coding Theory
• +۴ مورد دیگر

Peter Manohar is a postdoctoral researcher in the Computer Science and Discrete Math group at the Institute for Advanced Study , focusing on Theoretical Computer Science with emphasis on algorithms, coding theory, and cryptography. His work explores spectral algorithms for semirandom and smoothed instances of NP-hard constraint satisfaction problems, linking these methods to coding theory, extremal combinatorics, and cryptography. Education: PhD in Computer Science from Carnegie Mellon University, advised by Venkatesan Guruswami and Pravesh K. Kothari B.S. in EECS from UC Berkeley, advised by Alessandro Chiesa and Ren Ng Research Trends: His recent publications highlight advancements in spectral refutation techniques, locally decodable/correctable codes, and connections between complexity theory and coding. Articles span venues like FOCS, STOC, APPROX, and arXiv, reflecting his interdisciplinary approach. Awards: He has received prestigious NSF and Cylab Presidential Fellowships, along with ARCS scholarships during his PhD. His work on quantum proofs (TCC 2019) and constraint satisfaction problems has been recognized in invited journal special issues. Teaching & Collaboration: Peter has taught courses at Carnegie Mellon, including Quantum Computing and Computer Graphics. He interned at TTIC in Summer 2023 and co-organized CMU's Theory Club, demonstrating active engagement in academic communities.

• Cryptography
• Computer Security
• Electronic Voting Systems
• +۲ مورد دیگر

Daniel Rausch is a Researcher at the University of Stuttgart , affiliated with the Institute of Information Security . His work focuses on cryptographic protocols, particularly in electronic voting systems, universal composability, and accountable security frameworks. Key research areas include Cryptography , Computer Security , and Formal Verification . Recent publications explore password-hardened encryption, zero-knowledge proofs, and tally-hiding mechanisms for e-voting. Scientific contributions span: Provably secure password-hardened encryption with round-optimal schemes Accountable bulletin boards with UC security guarantees Zero-knowledge proofs for ballot validity in ElGamal-encrypted elections Publicly tally-hiding protocols preserving vote privacy UC-IITM model unification for composability frameworks Collaborations include researchers like Ralf Küsters , Nicolas Huber , and Marvin Bechtold . Notable implementations include Fabric*BB for distributed ledger applications and Kryvos for verifiable elections.

Artem Sokolovمشاهده پروفایل

استاد

• Machine Translation
• Natural Language Processing
• Structured Prediction
• +۴ مورد دیگر

Artem Sokolov serves as an Honorary Professor in the Department of Computational Linguistics at Heidelberg University and as a Research Scientist at Google Berlin. His primary research focuses on machine translation and structured prediction within natural language processing. Previously, he held positions at Amazon, the Statistical NLP Group at Heidelberg University led by Prof. Stefan Riezler, LIMSI, and Orange Labs in France, contributing to advancements in statistical and neural machine translation systems. He earned his PhD in Computer Science and Artificial Intelligence from the IRTCITS research center in Kyiv. His doctoral thesis investigated randomized algorithms for locality-sensitive embeddings of the Levenstein edit distance, establishing foundational work for efficient string similarity search in computational linguistics and intrusion detection systems. Dr. Sokolov's research expertise spans machine translation, imitation learning, bandit algorithms, and weakly supervised learning. He has pioneered methods for learning from partial feedback in structured prediction tasks, particularly addressing exposure bias in sequence generation and multi-facet evaluation of translation systems. His work bridges theoretical machine learning with practical NLP applications, emphasizing robustness against noisy data and scalable optimization techniques for real-world deployment. Analysis of his recent publications reveals trends toward scalable influence functions for model interpretability, multi-attribute control in machine translation, and rigorous auditing of multilingual datasets. His research consistently intersects natural language processing, machine learning optimization, and data quality assessment, with increasing emphasis on ethical AI considerations and efficient learning from weak supervision signals. Scientific awards include: 1st place at ECML/PKDD Discovery Challenge 2010 (English quality task) 2nd place at ECML/PKDD Discovery Challenge 2010 (general task) 2nd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 3rd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 As co-Principal Investigator for the 2015-2017 grant "Weakly Supervised Learning of Cross-Lingual Systems", Dr. Sokolov developed techniques for learning cross-lingual rankings from weakly supervised data sources like patent citations and Wikipedia hyperlinks. He has mentored students through teaching advanced courses including Imitation Learning, Stochastic Learning, and Statistical Machine Translation at Heidelberg University, supervising seminar projects on structured prediction and optimization algorithms. Dr. Sokolov is an active member of the Statistical NLP Group at Heidelberg University and collaborates with research teams at Google Berlin. His current work focuses on advancing production-scale machine translation systems through scalable inverse reinforcement learning and robust training methodologies, building on his extensive background in both academic research and industrial applications.

• theoretical aspects of cryptography
• computational assumptions
• theoretical aspects of machine learning
• +۶ مورد دیگر

Mohammad Mahmoody is an Associate Professor in the Computer Science Department at the University of Virginia (UVA), where he conducts theoretical research at the intersection of cryptography and machine learning. His research focuses on understanding fundamental limitations in cryptography and machine learning through the lens of lower bounds, impossibility results, and security barriers. Key areas include cryptographic protocols, adversarial robustness in learning systems, and computational complexity. His recent publications show a strong trend toward bridging cryptographic techniques with machine learning security, particularly in understanding data poisoning attacks, robust learning under adversarial conditions, and the computational foundations of security guarantees. His work spans both theoretical computer science venues (CRYPTO, EUROCRYPT, TCC) and machine learning conferences (NeurIPS, ICML). He actively advises graduate students and postdocs, with a track record of mentoring PhD candidates through completion of their degrees. His service to the academic community includes extensive program committee work for major conferences in both cryptography and machine learning. Dr. Mahmoody teaches advanced courses including Theory of Computation, Algorithms, and Cryptography at both undergraduate and graduate levels, demonstrating his commitment to educating the next generation of theoretical computer scientists.

• Cryptography
• Quantum Computing
• Lattice-based Cryptography
• +۳ مورد دیگر

Zvika Brakerski is a Professor at the Department of Computer Science and Applied Mathematics of the Weizmann Institute of Science. He is an active researcher in theoretical computer science with a focus on cryptography and quantum computing. His work bridges foundational aspects of computer science with practical cryptographic applications. His educational background includes: Ph.D. from Weizmann Institute of Science (2011), advised by Shafi Goldwasser M.Sc. from Faculty of Engineering of Tel-Aviv University (2002), advised by Boaz Patt-Shamir B.Sc. joint degree from Faculty of Engineering and School of Computer Science of Tel-Aviv University (2001) Brakerski's research primarily focuses on the foundations of computer science, with particular emphasis on cryptography and quantum computing. His work explores the intersection of these fields, developing new cryptographic primitives that are secure against quantum adversaries while also leveraging quantum phenomena for cryptographic purposes. He has made significant contributions to lattice-based cryptography, fully homomorphic encryption, and quantum cryptography, establishing new connections between computational complexity and cryptographic security. His recent publications demonstrate a consistent focus on advancing the theoretical foundations of post-quantum cryptography while exploring novel applications of quantum computing to cryptographic problems. There's a clear trend toward establishing connections between seemingly disparate areas such as quantum information theory, computational complexity, and traditional cryptographic constructions. His work often introduces new frameworks that unify previously separate concepts, creating bridges between theoretical computer science and practical cryptographic applications. Brakerski has mentored students and collaborated with numerous researchers in the field, contributing to the advancement of cryptographic theory and quantum computing. His work has been supported by various research grants, though specific details are not provided in the source material.

George Barmpaliasمشاهده پروفایل

استاد

• Mathematical Logic
• Computability Theory
• Algorithmic Randomness
• +۳ مورد دیگر

George Barmpalias is a Professor at the Chinese Academy of Sciences (Institute of Software) , with a research focus on Mathematical Logic , Computability Theory , Algorithmic Randomness , and Kolmogorov Complexity . His work also extends to Network Science and Social Dynamics through Schelling segregation models. Ph.D. in Mathematics, University of Leeds (2004) Mathematics Degree, University of Thessaloniki (2000) Research Interests include: Algorithmic Randomness (Ω numbers, K-triviality, Martin-Löf randomness) Computability Theory (Turing degrees, reducibilities, effective closed sets) Network Dynamics (Schelling models, segregation tipping points, idemetric properties) Information Theory (Kolmogorov complexity, data compression, oracle use bounds) Scientific Awards : Distinguished Research Professor (CAS, 2022) National Natural Science grants (2015-2020) International Young Scientist Award (CAS, 2012) EPSRC Research Fellowship (2004) Teaching spans graduate courses in: Theory of Computation Network Science Mathematical Logic Computability and Randomness Supervision includes Ph.D./MSc students like Fang Nan, Cheng Shichao, and Charlotte Vlek. His software projects include high-performance C++ simulations for Schelling models and open-source tools for algorithmic experiments.

Shu Shenمشاهده پروفایل

دانشیار

• Econometric Theory
• Applied Econometrics
• Applied Microeconometrics
• +۶ مورد دیگر

Shu Shen is an Associate Professor and Graduate Program Chair in the Department of Economics at the University of California, Davis, specializing in econometric theory and its applications in microeconometrics. His academic credentials include: Ph.D. in Economics, University of Texas at Austin, 2011 M.S. in Economics, University of Texas at Austin, 2008 B.A. in Economics, Fudan University, Shanghai, 2006 Shen's research centers on advancing econometric methodologies for causal inference, with emphasis on regression discontinuity designs, GMM identification, multiple testing frameworks, and distributional/quantile analysis. His work bridges theoretical rigor with practical applications in program evaluation and treatment effect heterogeneity. His publications reveal consistent contributions to methodological econometrics, particularly in developing robust techniques for regression discontinuity, treatment effect analysis, and multiple hypothesis testing. These works frequently employ nonparametric and semiparametric approaches to address distributional questions in empirical economics, appearing in leading journals like the Review of Economics and Statistics and Journal of Econometrics . Shen's research impact is recognized through prestigious awards: Hellman Fellow, University of California, Davis (2015) Individual Research Grant, UC Davis Institute for Social Sciences (2014) Small research grant, University of California, Davis (2012–2014, 2019) Hale Fellowship, University of Texas at Austin (2008) As Graduate Program Chair, he oversees doctoral training in Economics while securing research funding from institutional sources. His teaching portfolio spans undergraduate econometrics (ECN 102, 140) and graduate-level courses (ECN 240D, 240F), emphasizing both theoretical foundations and empirical applications.

Marco Molinaroمشاهده پروفایل

استاد

• Online Algorithms
• Convex Optimization
• Machine Learning
• +۵ مورد دیگر

Marco Molinaro is a Professor in the Computer Science Department at PUC-Rio . He previously held Assistant Professor positions at TU Delft (EWI), Georgia Tech (ISyE, ACO), and was a PhD student in the ACO program at Carnegie Mellon University . Education: PhD in Algorithms, Combinatorics, and Optimization (ACO), Carnegie Mellon University Research Interests: Marco's work lies at the intersection of online algorithms , convex optimization , machine learning , and operations research . His recent publications analyze: Online scheduling and VM allocation in data centers Complexity of branch-and-bound trees Information-theoretic bounds for optimization problems Explainable decision trees and their theoretical limits Stochastic and adversarial bandit algorithms Curvature properties in optimization sets His research often bridges abstract mathematical frameworks (e.g., Lipschitz functions, supermodular norms) with practical applications in cloud computing, machine learning, and game theory. Publication Trends: Marco's recent work focuses on online convex optimization and its extensions to data center resource allocation (Kamino: latency-driven VM scheduling), dynamic bin packing , and information complexity in mixed-integer models. He frequently collaborates with researchers at institutions like Carnegie Mellon University and TU Delft, with recurring co-authors Santanu Dey, Amitabh Basu, and Thomas Kesselheim. Teaching: Marco teaches advanced courses in algorithm design and analysis at PUC-Rio, including: Analise de Algoritmos (2020–2025) Projeto e Analise de Algoritmos (postgraduate, 2020–2025) Algoritmos e Incerteza (postgraduate, 2020) Estruturas Discretas (2021) His curriculum emphasizes online learning , random-order models , and PAC learning , with connections to game theory and practical implementations.