جست‌وجوی استاد و پژوهشگر

Russell Impagliazzoمشاهده پروفایل

استاد

• Computational Complexity
• Cryptography
• Randomness in Computation
• +۵ مورد دیگر

Russell Impagliazzo is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego (UCSD). He has held positions as Assistant Professor, Associate Professor, and Professor at UCSD since 1991 and was a Visiting Professor at the Institute for Advanced Study (Princeton) from 2007 to 2012. His academic journey includes a BA in Mathematics from Wesleyan University and a PhD in Mathematics from UC Berkeley. His research focuses on computational complexity theory , with key contributions to: Randomness in computation Cryptography (e.g., pseudorandom generators) Circuit lower bounds Proof complexity (e.g., polynomial calculus, resolution) Structural complexity (e.g., average-case hardness) Optimization heuristics (e.g., local search) The trends in his publications include foundational work on derandomization, hardness amplification, and algebraic proof systems. His papers often bridge theoretical computer science with mathematics, particularly in analyzing the limits of computational models. Scientific awards and honors include: NSF Young Investigator Sloan Fellow Fulbright Scholar Guggenheim Fellow Simons Investigator Best Paper Award (Computational Complexity Conference) Best Paper Award (STOC) Outstanding Paper Award (SIAM) He actively advises students and has contributed to grants and programs such as the Simons Institute’s Fine-Grained Complexity and Algorithms and the Meta-Complexity program at the Simons Lab in Spring 2023.

• Computer Vision
• Machine Learning
• Robotics
• +۷ مورد دیگر

Alexandre Alahi is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Visual Intelligence for Transportation (VITA) laboratory. He is affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC), the Institute of Infrastructure (IIC), and also contributes to diversity initiatives at ENAC. His research focuses on integrating computer vision, machine learning, and robotics to develop socially-aware AI for transportation and autonomous systems. University: École Polytechnique Fédérale de Lausanne (EPFL) School: School of Architecture, Civil and Environmental Engineering Department: Institute of Infrastructure, IIC Research Lab: Visual Intelligence for Transportation (VITA) Alexandre's research interests center on computer vision, machine learning, robotics, and AI safety, particularly in human trajectory prediction, depth estimation, and socially-aware autonomous navigation. He investigates how AI can understand and predict human behavior in complex environments to improve safety in mobility systems. His work bridges theoretical advances with real-world applications in autonomous driving, urban planning, and healthcare. His recent publications span a wide array of topics including omnidirectional stereo matching, trajectory forecasting, cross-view localization, AI security, and depth estimation. These works demonstrate a strong trend toward building generalizable, robust, and socially-compliant AI systems, with increasing focus on uncertainty quantification, safety certification, and real-world deployment. The integration of multimodal data and the development of foundation models are recurring themes. Alexandre has received numerous scientific accolades, including: Top 100 Most Influential Scholar in Computer Vision (2022–2023) Editor’s Choice Award, Image and Vision Computing (2021) Honorable Mention, ICCV Workshop (2019) CVPR Open Source Award (2012) ICDSC Challenge Prize (2009) Top 20 Swiss Venture Leaders (2010) He has advised numerous PhD students whose theses cover diverse topics such as human motion prediction, person re-identification, trajectory forecasting, and AI security. His lab has secured significant recognition and funding, enabling impactful research with real-world applications. Alexandre has also co-founded startups like Visiosafe, demonstrating strong industry engagement and technology transfer. The VITA lab fosters interdisciplinary collaboration, working across computer vision, robotics, transportation engineering, and human-centered AI. The team develops datasets, benchmarks, and open-source tools to advance the field and promote reproducibility.

• Algorithm Design
• Approximation Algorithms
• Fixed-Parameter Tractability
• +۱ مورد دیگر

Michael Lampis is an Associate Professor at Université Paris Dauphine, specializing in theoretical computer science with a focus on algorithm design, approximation algorithms, and fixed-parameter tractability. His research bridges computational complexity and practical algorithmic solutions for NP-hard problems. His work explores structural graph parameters, approximation schemes, and parameterized complexity, with applications in game theory, social choice, and combinatorial optimization. Research interests include: Design of sub-exponential and parameterized approximation algorithms Fine-grained complexity analysis Algorithmic meta-theorems for graph width parameters Lampis has supervised multiple PhD students and leads research projects funded by ANR and international collaborations. His publications demonstrate consistent focus on bridging theoretical foundations with computational efficiency across diverse domains including voting systems, hedonic games, and network design.

Prashant Nalini Vasudevanمشاهده پروفایل

استادیار

• Cryptography
• Complexity Theory
• Theoretical Computer Science
• +۲ مورد دیگر

Prashant Nalini Vasudevan serves as an Assistant Professor and NUS Presidential Young Professor in the Department of Computer Science at the National University of Singapore's Faculty of Computing. His research focuses on the theoretical foundations of cryptography and complexity theory, with particular emphasis on identifying computational hardness assumptions that can support cryptographic constructions. Dr. Vasudevan's research interests span Cryptography , Complexity Theory , and Theoretical Computer Science , with specific focus on statistical zero-knowledge proofs, fine-grained cryptographic primitives, and the intersection of cryptography with data privacy regulations. His work explores how computational hardness can be leveraged to build secure systems while addressing modern privacy concerns such as the right to be forgotten. Analysis of his recent publications reveals a strong trend toward statistical zero-knowledge systems, batch verification techniques, and formal approaches to data deletion. His research bridges theoretical computer science with practical privacy concerns, particularly in the context of machine learning systems and regulatory compliance. The work demonstrates increasing sophistication in cryptographic protocols while maintaining rigorous security proofs. NUS Presidential Young Professorship (2021-2026): Computational Hardness Assumptions and the Foundations of Cryptography NRF Fellowship (2022-2027): Fine-Grained Cryptography Dr. Vasudevan actively mentors multiple PhD students including Haoxing Lin, Yunqi Li, and Kel Zin Tan, along with postdoctoral researcher Rohit Chatterjee. His research group focuses on theoretical cryptography with applications to privacy-preserving systems. Current projects include developing new cryptographic primitives based on fine-grained hardness assumptions and exploring the theoretical foundations of data deletion in machine learning contexts.

Marc Rothمشاهده پروفایل

مدرس

• Theoretical Computer Science
• Algorithmics
• Computational Complexity Theory
• +۵ مورد دیگر

Dr. Marc Roth is a Lecturer in Theoretical Computer Science at Queen Mary University of London (QMUL), affiliated with the School of Electronic Engineering and Computer Science. He is also an Associate Member of the Department of Computer Science at the University of Oxford. Previously, he served as a Senior Research Associate at Oxford (until 2023) and held a Junior Research Fellowship at Merton College, Oxford. He earned his PhD in Computer Science from Saarland University under Prof. Holger Dell. His research focuses on computational counting problems, particularly in the context of graph theory and algorithmic complexity. Key interests include parameterized and fine-grained complexity, exact and approximate counting algorithms, and applications in network analysis. He has contributed to foundational work on subgraph counting, constraint satisfaction, and the theoretical limits of computational problems. Dr. Roth’s work bridges theoretical computer science and practical algorithm design, addressing challenges in analyzing large networks and understanding complexity hierarchies. He actively collaborates with institutions like the Centre for Fundamental Computer Science at QMUL and has supervised a PhD studentship exploring motif counting in higher-order networks.

Robbert Krebbersمشاهده پروفایل

دانشیار

• Semantics
• Separation logic
• Theorem proving
• +۱۳ مورد دیگر

Robbert Krebbers is an Associate Professor in the Department of Software Science at Radboud University Nijmegen, Netherlands. His research focuses on advancing program verification techniques for complex programming paradigms such as concurrency, higher-order functions, and modular code, with applications to systems languages like C, Rust, and Scala. He develops rigorous mathematical foundations and practical verification tools, primarily using the Coq proof assistant. His most significant research contribution is as co-designer and co-leader of Iris , a highly influential framework for concurrent separation logic in Coq. Iris has been widely adopted in verification projects worldwide. Before returning to Radboud, he served as an Assistant Professor at Delft University of Technology and completed a postdoctoral fellowship at Aarhus University. He earned his PhD cum laude from Radboud University between 2011 and 2015. His research interests include: Semantics Separation logic Theorem proving Coq Program verification Concurrent and higher-order programming His recent publications (2020–2025) show a consistent focus on mechanized verification, linearizability, session types, deadlock freedom, and foundational enhancements to Iris and separation logic. These works appear in top-tier venues like POPL, PLDI, ICFP, and OOPSLA, with several receiving distinguished paper or artifact awards. Notable scientific awards include: POPL 2023 Distinguished Paper Award (DimSum) POPL 2022 Distinguished Paper Award (Simuliris) PLDI 2021 Distinguished Paper and Artifact Awards (RefinedC) CPP 2021 Distinguished Paper Award (Machine-Checked Semantic Session Typing) ECOOP 2017 Distinguished Paper Award 2023 Alonzo Church Award (as part of Iris team) He has advised PhD students such as Ike Mulder and Jules Jacobs. He actively contributes to the academic community as a program committee member, steering committee member, and organizer for major conferences including POPL, PLDI, ICFP, and CPP. His work is supported by major grants such as ERC Consolidator Grant (RustBelt) and Villum Investigator Grant (CPV). He is a core member of the Logic and Semantics Group and the Iris research project, which involves collaborations across institutions globally. His personal website is https://robbertkrebbers.nl .

Ian James Miguelمشاهده پروفایل

استاد

• Artificial Intelligence
• Constraint Programming
• Combinatorial Optimization
• +۳ مورد دیگر

Prof Ian James Miguel is a Professor and Head of the School of Computer Science at the University of St Andrews, a position he has held since 2004. He specializes in Artificial Intelligence, particularly in solving combinatorial optimization problems using Constraint Programming and SAT techniques. His research emphasizes automated constraint modeling, including the development of the Constraint Modelling Pipeline, which translates high-level problem descriptions into solver-ready models. He teaches courses on Constraint Programming and Video Games. His research focuses on advancing constraint modeling techniques, including automated reformulation, tabulation, and the exploration of constraint models via graph transformations. Notable contributions include the Athanor system for local search over constraint specifications and the TabID framework for subproblem identification. His work frequently addresses challenges in combinatorial optimization, such as multi-task learning and algorithm selection. Prof Miguel leads projects funded by EPSRC and others, including initiatives on automated constraint modeling tools like Conjure and Savile Row. He has supervised PhD students in areas like constraint modeling, automated algorithm selection, and planning under constraints. His research extends to interdisciplinary applications, such as ecological modeling and computational algebra, through collaborations with centers like the Sir James Mackenzie Institute. His recent publications emphasize automated reformulation techniques, streamlined constraint solving, and the integration of machine learning into constraint-based systems. His work aims to enhance solver efficiency and broaden the applicability of constraint programming across diverse domains.

Nizar Habashمشاهده پروفایل

استاد

• Natural Language Processing
• Computational Linguistics
• Arabic Language Processing
• +۱۲ مورد دیگر

Nizar Habash is a Professor of Computer Science at New York University Abu Dhabi (NYUAD) and a Global Network Professor at the Courant Institute of Mathematical Sciences. He is the director of the Computational Approaches to Modeling Language (CAMeL) Lab, where he leads research in natural language processing, computational linguistics, and Arabic language technologies. His educational background includes a BS in Computer Engineering and a BA in Linguistics and Languages from Old Dominion University, and MS and PhD degrees in Computer Science from the University of Maryland, College Park. Habash's research focuses on artificial intelligence, particularly natural language processing for Arabic and its dialects. His work spans machine translation, morphological and syntactic analysis, sentiment analysis, dialogue systems, and dialect identification. He has developed foundational resources such as the MADAR corpus, CODA orthography, and tools like MADAMIRA and CamelParser. His publications reflect a strong emphasis on creating robust, multilingual, and dialect-aware NLP systems for low-resource and complex linguistic environments. Habash has been involved in over 20 research grants and has authored more than 150 publications, including the influential book Introduction to Arabic Natural Language Processing . His recent work centers on improving machine translation, modeling Arabic orthography and morphology, and building large-scale annotated corpora for dialectal Arabic. His scientific recognition includes the ELRA Antonio Zampolli Prize in 2024 for outstanding contributions to language resources and evaluation in human language technologies. Habash advises numerous research projects and capstone theses at NYUAD. He has taught courses such as Natural Language Processing, Arabic Computational Linguistics, Discrete Mathematics, and the Computer Science Research Seminar. He has secured significant grant funding, particularly through projects like QALB and MADAR, which have advanced Arabic NLP research globally. He leads the CAMeL Lab, a vibrant research group focused on AI-driven language modeling, with active projects in Arabic readability (SAMER), dialect identification (ADIDA), dialogue systems (TOIA, BOTTA), and corpus development (GUMAR, Curras, Arab-Acquis).

• Cryptography
• Public-key Cryptography
• Theoretical Computer Science
• +۷ مورد دیگر

Jiaxin Pan is a Professor at the University of Kassel (Germany) and an Associate Professor (førsteamanuensis, part-time) at the Norwegian University of Science and Technology (NTNU). He is affiliated with the Algebra Group at NTNU and has previously worked as a postdoctoral researcher at Karlsruhe Institute of Technology (KIT) and the University of Kassel. Pan earned his PhD from Ruhr-Universität Bochum (Germany) in 2016. Research Interests Tight security proofs in cryptography Lattice-based cryptosystems Identity-based encryption Zero-knowledge proof systems Structure-preserving signatures Fine-grained cryptography Research Trends and Publications His work focuses on enhancing cryptographic security reductions and designing efficient protocols for post-quantum environments. Key areas include key exchange mechanisms, attribute-based encryption, and signature schemes with tight security guarantees. Pan's papers are published in top-tier venues such as Journal of Cryptology, CRYPTO, and Eurocrypt. Grants and Projects NTNU Outstanding Academic Fellows Programme (2022–2026) Young Research Talent project (2021–2025), Research Council of Norway Peder Sather Grant (2021–2023), with Sanjam Garg (UC Berkeley) Professional Activities Pan serves on program committees for IACR Eurocrypt, Asiacrypt, PKC, CT-RSA, and ESORICS. He is a reviewer for journals and conferences like ACM Transactions on Algorithms, Journal of Cryptology, and DFG grants.

Yixin Caoمشاهده پروفایل

دانشیار

• Algorithmic Graph Theory
• Structural Graph Theory
• Fine-grained Complexity
• +۸ مورد دیگر

Yixin Cao is an Associate Professor in the Department of Computing at Hong Kong Polytechnic University, Faculty of Engineering. He conducts research in theoretical computer science with a focus on algorithmic and structural graph theory, combinatorial optimization, and their applications. He leads a research group and is actively involved in the theoretical computer science community through publications, talks, and conference organization. Research Interests: Algorithmic and structural graph theory, particularly on interval and circular-arc graphs Fine-grained complexity and parameterized algorithms Graph modification problems (editing, deletion, completion) Combinatorial optimization with applications in bioinformatics and social networks His recent publications highlight a strong trend in characterizing complex graph classes (e.g., proper Helly circular-arc graphs, chordal circular-arc graphs) and developing efficient parameterized algorithms, especially kernelization techniques for graph editing problems. He frequently publishes in top venues such as Algorithmica, Theoretical Computer Science, ICALP, ESA, and MFCS. Grants: PI, "(Parameterized) complexity of graph edge editing problems", NSFC, 01/2024–12/2027 PI, "Approximation algorithms for phylogenetic networks", RGC, 01/2021–12/2023 PI, "Algorithmic study on chordal and related graphs", NSFC, 01/2020–12/2023 PI, "Super-polynomial approximation of graph problems", RGC, 01/2018–12/2020 Multiple earlier grants from RGC, NSFC, and NLSDE on graph algorithms and complexity Research Group: Advises graduate students including Ying Xu, Haowei Chen, and Shenghua Wang Hosts visiting researchers such as Tomasz Krawczyk (Warsaw University of Technology) Collaborates extensively with researchers in China and internationally

• Theory of Computation
• Fine-Grained Complexity
• Graph Theory and Algorithms
• +۷ مورد دیگر

Dr. Amir Abboud is a Senior Scientist in the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science, a leading research institution in Israel. His work is centered on the theory of computation, particularly fine-grained complexity, aiming to establish exact computational complexity bounds for fundamental problems. Research Interests: Amir Abboud's research spans a broad spectrum of theoretical computer science, with a primary focus on Fine-Grained Complexity . He investigates conditional lower bounds based on popular conjectures such as SETH and 3SUM. His work extends to graph algorithms, dynamic data structures, pattern matching, sequence alignment, exact and parameterized algorithms, distributed computing, and circuit complexity. He employs deep connections between parsing, clique problems, edit distance, and spanner constructions to derive new hardness results. Publication Trends: His recent publications, appearing in top venues like FOCS and STOC, reveal a consistent focus on proving conditional lower bounds and developing improved algorithms for graph problems. Themes include subcubic algorithms for Gomory-Hu trees, additive spanners, and the interplay between parsing and clique detection. His work often bridges algorithmic improvements with complexity-theoretic limitations. Scientific Service: Program Committee Member, FOCS 2023 Program Committee Member, ICALP 2023 Program Committee Member, STOC 2023 Program Committee Member, SWAT 2022 Program Committee Member, SODA 2022 Program Committee Member, SOSA 2022 Program Committee Member, ESA 2020 Program Committee Member, CPM 2020 Program Committee Member, IPEC 2019 Program Committee Member, STOC 2019 Program Committee Member, FOCS 2018 Program Committee Member, CPM 2016 Advising and Grants: While the text does not list any formal students or advisees, Dr. Abboud leads significant research in theoretical computer science and collaborates with top researchers worldwide. His publication record and program committee roles indicate a well-funded and influential research trajectory, likely supported by institutional and national research grants, though specific grants are not mentioned. Labs and Research Groups: Dr. Abboud is affiliated with the theoretical computer science group at the Weizmann Institute of Science. He also organizes a Course on Fine-Grained Complexity and is part of a research group focused on algorithms and complexity, as indicated by the website structure.

Enric Boixمشاهده پروفایل

استادیار

• AI Theory
• Neural Networks
• Machine Learning Foundations
• +۷ مورد دیگر

Enric Boix is an Assistant Professor at the University of Pennsylvania , affiliated with The Wharton School and the Statistics and Data Science Department . His research focuses on the mathematical foundations of deep learning, including neural network training dynamics, inductive bias, adversarial robustness, and AI safety. Undergraduate: Princeton University (advised by Emmanuel Abbe) PhD: MIT EECS (advised by Guy Bresler and Philippe Rigollet) Postdoctoral: MIT Mathematics & Harvard CMSA Current work investigates theoretical aspects of AI, such as: Feature learning in neural networks Model distillation and fine-grained expert architectures Chain-of-thought reasoning in LLMs Inductive bias in ResNets and Transformers NTK approximation validity and optimization dynamics Adversarial prompt vulnerabilities His publications span top conferences like NeurIPS, ICLR, and EMNLP, emphasizing mathematical rigor in understanding deep learning phenomena. Awards include: NSF Graduate Research Fellowship Siebel Fellowship Apple AI/ML Fellowship He has collaborated extensively with researchers including Mikhail Belkin, Emmanuel Abbe, and Philippe Rigollet, focusing on theoretical advancements to improve AI efficiency and trustworthiness.

Michele Tomaiuoloمشاهده پروفایل

دانشیار

• Social Media Analysis
• Peer-to-Peer Networks
• Security and Trust Management
• +۵ مورد دیگر

Michele Tomaiuolo is an Associate Professor at the Department of Engineering and Architecture, University of Parma, Italy. He has been actively teaching undergraduate and graduate courses in computer science and engineering since at least 2014, with current teaching assignments for the 2025/2026 academic year. Dr. Tomaiuolo holds a Master's degree in Computer Engineering and a PhD in Information Technology, both from the University of Parma. His educational background has provided the foundation for his extensive career in academic research and teaching in the field of computer science. Professor Tomaiuolo's research spans several interconnected domains within computer science and information systems. His primary focus is on social media analysis and peer-to-peer social networks, with particular attention to security and trust management. His work also encompasses multi-agent systems, semantic web technologies, rule-based systems, and peer-to-peer networking architectures. More recently, his research has expanded into sentiment analysis applications for digital libraries and educational contexts, as well as fine-grained agent-based modeling for epidemiological simulations. His interdisciplinary approach bridges theoretical computer science with practical applications in information systems and social computing. An analysis of Professor Tomaiuolo's recent publications (2020-2024) reveals several key trends in his research. There is a strong emphasis on social network analysis, particularly in the context of sentiment analysis and user behavior. His work frequently employs agent-based modeling techniques to address complex problems ranging from pandemic spread prediction to digital library user experience. The integration of artificial intelligence methods with traditional computer science approaches is evident across his publications, with applications spanning digital libraries, educational technology, and social media platforms. His research demonstrates a consistent focus on practical implementations of theoretical concepts, often addressing real-world challenges through computational solutions. Professor Tomaiuolo has participated in various research projects, including the EU-funded @lis TechNet, Agentcities, Collaborator, and Comma projects, and the nationally funded Anemone project. These collaborative efforts have provided the foundation for much of his published research. His teaching portfolio is extensive, covering fundamental computer science concepts, object-oriented programming, software engineering, computer networks, mobile code, and security. Current courses for the 2025/2026 academic year include Fundamentals of Computer Science, Computer Science and Programming Laboratory, and Paradigms and Languages for Data Analysis across various engineering programs. Professor Tomaiuolo is actively involved in the academic community, serving as a reference professor and tutor for undergraduate engineering programs at the University of Parma. His work with the CSBNO Consortium demonstrates his commitment to applying computational methods to enhance library services and user experiences.

• Computational Geometry
• Hyperbolic Space
• Data Structures
• +۶ مورد دیگر

Geert van Wordragen is a doctoral researcher at the Department of Computer Science, School of Science, Aalto University. His work focuses on computational geometry, hyperbolic space, and algorithmic complexity. University: Aalto University School: School of Science Department: Department of Computer Science Rank: Researcher Geert’s research explores hyperbolic space, data structures, and fine-grained complexity. He has contributed to discrete Voronoi games and nearest neighbor algorithms, with publications from 2023 to 2025. His recent work includes: 2025: Structure and Independence in Hyperbolic Uniform Disk Graphs 2024: A Quadtree, a Steiner Spanner, and Approximate Nearest Neighbours in Hyperbolic Space 2024: Fine-Grained Complexity of Earth Mover’s Distance Under Translation 2023: Improved Bounds for Discrete Voronoi Games Conference Activities Member of the Helsinki Algorithms & Theory Days committee (2024)

• Fine-Grained Complexity
• Theory of Computation
• Graph Theory and Algorithms
• +۶ مورد دیگر

Amir Abboud is a Senior Scientist (equivalent to Associate Professor) in the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science, where his research centers on theoretical computer science with primary focus on fine-grained complexity. This field investigates exact computational complexity of fundamental problems to establish tight upper and lower bounds. His educational credentials include: Ph.D. from Stanford University M.Sc. from the Technion B.Sc. from the University of Haifa (via the "Etgar" program) Abboud's research spans graph theory and algorithms, dynamic data structures, pattern matching, sequence alignment, exact algorithms, parameterized complexity, distributed computing, and circuit complexity. His work bridges theoretical insights with practical algorithmic advancements, particularly in understanding computational limits within polynomial time. Analysis of his publications (2014-2022) reveals consistent focus on fine-grained complexity applications to graph algorithms and dynamic problems. Key contributions include breakthroughs in Gomory-Hu tree computation, hardness of approximation frameworks, and conditional lower bounds for dynamic data structures. His research demonstrates how conjectures about fundamental problems (e.g., SETH, 3SUM) propagate to diverse computational domains. Scientific awards: No specific prizes, fellowships, or medals are documented in the source material. Advising and grants: The provided text contains no information regarding graduate students supervised, postdoctoral mentoring, or research funding received. Labs and teams: No details about laboratory leadership, research groups, or collaborative teams are mentioned in the available documentation.