جست‌وجوی استاد و پژوهشگر

• 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.

• Answer Set Programming
• Logic Programming
• Knowledge Representation
• +۷ مورد دیگر

Mario Alviano is a Professor at the University of Calabria's Department of Computer Engineering, Modeling, Electronics and Systems (DIMES) in Rende, Italy. With a prolific publication record spanning from 2008 to 2025, he has established himself as a leading researcher in Answer Set Programming (ASP), logic programming, and knowledge representation. His work bridges theoretical foundations with practical applications across diverse domains including cybersecurity, environmental monitoring, and explainable AI. Alviano's research focuses on advancing the theoretical understanding and practical applications of Answer Set Programming. His key contributions include developing ASP Chef, a visualization and development framework for ASP; advancing theoretical foundations of conditional reasoning and typicality in weighted knowledge bases; and creating bridges between neural networks and symbolic AI through preferential interpretations. His recent work explores temporal extensions to conditional logics, applications of ASP to digital twin visualization, and tools for improving explainability in AI systems. Alviano's publications consistently appear in top-tier venues including ICLP, LPNMR, JELIA, and Theory and Practice of Logic Programming. His research demonstrates strong trends toward practical applications of theoretical advances, with increasing focus on explainability, visualization, and real-world problem solving. The recent surge in publications (26 in 2024 alone) indicates significant ongoing research activity and leadership in the field. His work increasingly integrates ASP with other AI paradigms, particularly neural networks, to create more interpretable and robust AI systems. Alviano has made substantial contributions to the development of tools and frameworks that lower barriers to entry for ASP, most notably ASP Chef which provides visualization capabilities for complex logic programs. His work on Hashcash Tree demonstrates applications to security problems, while his research on marketplace logistics shows practical business applications of declarative programming. His collaborative network is extensive, with frequent co-authorship with researchers including Wolfgang Faber, Nicola Leone, Francesco Ricca, Carmine Dodaro, Laura Giordano, and Daniele Theseider Dupré, reflecting strong connections across European AI research institutions. Alviano has also organized workshops including multiple editions of Datalog 2.0, demonstrating leadership in the logic programming community.

• 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.

Grammati Pantziouمشاهده پروفایل

استاد

• Algorithmic Techniques
• Parallel and Distributed Computing
• Mobile Ad-Hoc and Sensor Networks
• +۵ مورد دیگر

Grammati Pantziou is a Professor at the Department of Informatics and Computer Engineering, University of West Attica (since 2018). She previously held roles as Vice Rector for Administrative Affairs (2019–2023), member of the Governing Committee, and president of the Research Committee at the same institution. Earlier positions include Assistant Professorships at Dartmouth College (1992–1994) and University of Central Florida (1994–1995), and Professor at the Technological Educational Institute of Athens (1998–2018). She served on the Greek Data Protection Authority (2003–2013). Education: Ph.D. in Computer Science, University of Patras, Greece (1991) Research Interests: Focuses on algorithmic optimization, parallel/distributed computing, mobile networks, and cybersecurity. Specializes in sensor networks, pervasive computing security, and smart city applications. Her work bridges theoretical algorithms with real-world systems like tourism route planning, vehicular networks, and IoT. Publications & Impact: Authored/co-authored 11 books, 165+ papers, and edited 5 books. Her research has 5,600+ citations (h-index:34, i10-index:80). Leads 29 funded projects on cybersecurity, transportation optimization, and smart systems. Editorial Roles: Editor-in-Chief of Algorithms Journal's 'Parallel and Distributed Algorithms' section, and editor for journals like Cybersecurity & Privacy (MDPI) and Security and Communication Networks (Wiley). Contributions: Developed frameworks like eCOMPASS for tourist route planning and ETHACA for cybersecurity training. Pioneered algorithms for vehicle relocation in sharing systems and energy-efficient data aggregation in sensor networks.

Alexandr Andoniمشاهده پروفایل

دانشیار

• Algorithmic Foundations of Massive Data
• Sublinear Algorithms
• High-Dimensional Computational Geometry
• +۴ مورد دیگر

Alexandr Andoni is an Associate Professor in the Department of Computer Science at Columbia University and a member of the Data Science Institute. His research focuses on the algorithmic foundations of massive data, including sublinear algorithms, high-dimensional computational geometry, and machine learning. He holds a PhD from MIT (2009) under Piotr Indyk, with postdoctoral work at Princeton University and visits at NYU and the Institute for Advanced Study (IAS). He previously worked at Microsoft Research Silicon Valley and the Simons Institute for the Theory of Computing at UC Berkeley. His research interests span sublinear algorithms (streaming, property testing), metric embeddings, and Locality-Sensitive Hashing (LSH). He maintains an active LSH page and the FALCONN software package. He teaches graduate courses on algorithms for massive data and advanced algorithms. Andoni advises a cohort of brilliant researchers, including current students Negev Shekel Nosatzki and Shunhua Jiang, and has mentored former students and postdocs like Ilya Razenshteyn and Peilin Zhong. His work bridges theoretical computer science with practical applications in data analysis and machine learning.

• Algorithms
• Graph Theory
• Computational Complexity
• +۲ مورد دیگر

Manuel Sorge is a Research Fellow in the Faculty of Informatics at Vienna University of Technology, affiliated with the Algorithms and Complexity group (E192-01). His research focuses on parameterized algorithms, graph theory, and computational complexity, with particular emphasis on graph modification problems, clustering algorithms, and combinatorial optimization. His work spans several areas including graph drawing, algorithmic game theory, and machine learning. Research interests include: Parameterized complexity and kernelization Graph algorithms and combinatorial optimization Algorithmic graph theory and discrete mathematics Computational geometry and graph drawing Supervises master's students in algorithm design and complexity theory. Current projects include Parameterized Graph Drawing (2023–2027).

Christian Freudeمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Rendering
• Monte Carlo Methods
• +۳ مورد دیگر

Christian Freude is a PreDoc Researcher at the Department of Computer Graphics , Faculty of Informatics , Vienna University of Technology (TU Wien) , focusing on Visual Computing and Human-Centered Technology . His work bridges photorealistic rendering , Monte Carlo methods , and real-time simulation . Active in projects: Toward Optimal Path Guiding (2023–2027, WWTF-funded), ACD (2020–2028) Supervised PhD/Master's students : Lukas Lipp, Christian Jauernik, Johannes Lurf Research spans Monte Carlo denoising , radiative thermal transport , subsurface scattering , and point cloud rendering . His 2025 work on Inverse Simulation of Radiative Thermal Transport introduces novel methods for efficient heat distribution modeling. Key scientific awards include the OCG Förderpreis 2016 . Publications from 2015–2025 highlight advancements in rendering algorithms , stereoscopic film , and uncertainty visualization .

• Algorithms
• Data Structures
• String Algorithms
• +۳ مورد دیگر

Panagiotis Charalampopoulos is a Lecturer (Assistant Professor) in the Department of Informatics at King's College London, part of the School of Computing and Mathematical Sciences. He holds a PhD in Computer Science from King's College London, an MSc in Advanced Computing from the same institution, and a BA in Mathematics from the University of Cambridge. Previously, he was a Lecturer at Birkbeck, University of London and a postdoctoral fellow at Reichman University and the University of Haifa. His research focuses on algorithms and data structures, particularly string algorithms (e.g., pattern matching, indexing) and planar graph algorithms (e.g., shortest path computations). Applications of his work include bioinformatics, text mining, and medical imagery analysis. He teaches modules such as 'Software and Programming I' and 'Problems in Mathematics.' His research has produced impactful contributions in both theoretical and applied domains, addressing challenges in efficient algorithm design and practical implementations. His work often bridges foundational computer science principles with real-world applications. Dr. Charalampopoulos' publications span topics like resilient pattern mining, efficient string processing, and planar graph optimization, reflecting his dual focus on algorithmic efficiency and practical utility. His academic journey includes research internships at the University of Warsaw and IDC Herzliya, highlighting his international collaboration and expertise in computational theory.

Martin Skutellaمشاهده پروفایل

استاد

• Combinatorial Optimization
• Network Flows
• Discrete Mathematics
• +۶ مورد دیگر

Prof. Martin Skutella holds the Einstein Professorship in Mathematics and Computer Science at Technische Universität Berlin (TU Berlin), where he is part of Faculty II – Mathematics and Natural Sciences and the Department of Mathematics. His research focuses on combinatorial optimization, network flows, scheduling, and algorithmic game theory. He leads major initiatives such as the Berlin Mathematics Research Center MATH+ and previously served as Chair of MATHEON. Skutella is renowned for contributions to dynamic network flows, evacuation planning, and scheduling under uncertainty. His work bridges theoretical advancements with practical applications, including transportation systems, robotic welding, and gas network optimization. He has been recognized with awards like the Einstein Professorship (2015) and the Best Teaching Award (2004). Skutella advises numerous PhD students and collaborates internationally on projects funded by DFG, EU, and industry grants. Key roles include organizing the 2012 International Symposium on Mathematical Programming and editing the Notices of the German Mathematical Society. His lab focuses on discrete optimization and graph algorithms, with applications in logistics, transportation, and energy systems.

Tomasz Kociumakaمشاهده پروفایل

استاد

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

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

• Analysis
• Applied mechanics
• Calculus of variations
• +۶ مورد دیگر

Marco Morandotti is an Associate Professor in Mathematics at the Department of Mathematical Sciences (DISMA) of Politecnico di Torino . He is also a member of the Interdepartmental Center IAM@PoliTo - Integrated Additive Manufacturing. Research Interests: Calculus of variations, Applied mechanics, Defects analysis, Multi-agent systems, Mathematical modeling. Teaching: Courses include Sobolev spaces, Gamma-convergence, Asymptotic methods for multi-scale problems, and Mathematical Analysis at both PhD and undergraduate levels. Scientific Contributions: His recent publications focus on structured deformations, microswimmer controllability, and Γ-convergence approaches for stochastic particle aggregation. He has co-edited a didactic book on hands-on mathematics. Scientific Awards: Learning to Teach (L2T) badge from Politecnico di Torino (2023) Conference Roles: Program chair for multiscale multi-agent systems, elasticity challenges, and nonlinear analysis workshops. Academic Collaborations: Has held research and teaching positions at Carnegie Mellon University, University of Lisbon, SISSA Trieste, and TU München.

Soumen Chakrabartiمشاهده پروفایل

استاد

• Graph Neural Networks
• Knowledge Graphs
• Natural Language Processing
• +۳ مورد دیگر

Soumen Chakrabarti is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. His work bridges graph theory, natural language processing, and knowledge representation, focusing on scalable solutions for complex information retrieval problems. He has held visiting positions at Carnegie-Mellon University and Google. Education: B.Tech from IIT Kharagpur, PhD from UC Berkeley Key research areas: Graph Neural Networks, Knowledge Graphs, Code-Switched Text Analysis, Temporal Reasoning His recent publications emphasize neural graph matching , code-switched NLP , and multi-modal QA systems , often integrating symbolic reasoning with deep learning. Awards include the WWW 1999 Best Paper Award and ECML/PKDD 2008 Best Student Paper . He leads projects like CRUSH4SQL for text-to-SQL parsing and CSAW for annotated web search. His lab collaborates with IBM and Google on graph representation learning and temporal knowledge graph completion.

Anthony PEREZمشاهده پروفایل

مدرس

• Parameterized Complexity
• Graph Algorithms
• Community Detection
• +۳ مورد دیگر

Anthony PEREZ is a Lecturer in Computer Science at the University of Orleans, affiliated with LIFO (Laboratory of Fundamental Computer Science of Orleans) and part of the Graphs, Algorithms, and Computational Models team. He previously held a full-time ATER position at the same university from 2011 to 2012 and completed his PhD at LIRMM (Montpellier II University) under the supervision of Stéphane Bessy and Christophe Paul. Research Focus: Parameterized complexity, graph modification problems, community detection in directed graphs, and sparse graph embeddings Software Development: Created SINr (sparse interpretable node representations), Directed Louvain (modularity optimization for directed networks), and GeST (graph embedding-based image segmentation) His research explores polynomial kernelization for graph editing problems, particularly in triangulated graph subclasses, and challenges the common practice of ignoring edge direction in community detection algorithms. He has co-supervised multiple PhD students including Joanne Dumont and Maël Dumas, and collaborates extensively on graph-theoretical problems with applications to social network analysis.

Darren A. Narayanمشاهده پروفایل

استاد

• Graph Theory
• Combinatorics
• Social Networks
• +۴ مورد دیگر

Darren A. Narayan is a Professor in the School of Mathematical Sciences within the College of Science at Rochester Institute of Technology (RIT), where he also serves as Director of Undergraduate Research. His academic foundation includes a BS from State University of New York at Binghamton and MS/PhD from Lehigh University. His educational background is: BS: State University of New York at Binghamton MS, PhD: Lehigh University Narayan's research spans Graph Theory, Combinatorics, and their real-world applications in Social Networks, Brain Connectivity, and Mathematical Modeling. His work bridges theoretical mathematics with practical problems in neuroscience, transportation systems, and internet infrastructure. He develops graph-theoretic metrics for analyzing functional MRI data, social network dynamics, and network efficiency in systems like Atlanta's subway and fiber-optic networks. His publication trends reveal consistent focus on graph labeling (k-rankings, bent ladders), network efficiency metrics, and interdisciplinary applications. Key themes include brain connectivity modeling using clustering coefficients, real-world network analysis (social, transportation, fiber-optic), and combinatorial solutions for access control and resource allocation. His research is supported by significant NSF funding, including renewal of RIT's longest-running REU site in extremal graph theory and dynamical systems. Narayan actively mentors undergraduate researchers and leads the STEM Real World Applications of Mathematics Project, which connects classroom concepts to real industry problems through modules with IBM, National LambdaRail, and Cisco Systems. His grant portfolio includes NSF-CCLI Award #1019532 and multiple REU site funding cycles. He directs research initiatives through the School of Mathematical Sciences, focusing on collaborative projects with industry partners to demonstrate practical applications of graph theory in network design, brain connectivity analysis, and optimization problems.

Kexin Peiمشاهده پروفایل

استادیار

• Security
• Software Engineering
• Machine Learning

Kexin Pei is a Neubauer Family Assistant Professor at the Department of Computer Science, University of Chicago. She received her PhD from the Department of Computer Science at Columbia University. Her academic career spans research and teaching in the fields of Security, Software Engineering, and Machine Learning. Her educational background includes: PhD in Computer Science from Columbia University Dr. Pei's research interests focus on developing data-driven program analysis to improve the security and reliability of both traditional and AI-based software systems. She is particularly interested in creating machine learning models that can reason about program structure and behavior to precisely and efficiently analyze, detect, and fix software bugs and vulnerabilities. Her work bridges the gap between theoretical computer science and practical security applications, with significant contributions to binary analysis, program understanding, and AI security. Her research output shows a strong trend toward integrating machine learning with traditional program analysis techniques. Many of her recent publications explore how neural networks and language models can enhance software security and reliability, particularly in binary analysis, vulnerability detection, and code understanding. Her work often involves developing novel frameworks that combine execution traces, code structure, and semantic understanding to create more robust analysis tools. Dr. Pei has received several prestigious awards for her research: Best Paper Award at MASEC@NeurIPS 2023 Best Paper Award Runner-Up in CSAW 2018 Top-10 Finalist of Applied Research Competition ACM SigMobile Research Highlight MLSec @NIPS'17 CACM research highlight Distinguished Artifact Award at FSE 2016 Dr. Pei actively mentors students and has advised several PhD and Master's students. Her group includes PhD students Jie Zhu, Weichen Li, and Jun Yang (with Shan Lu), as well as MS students Sam Huang and Yiming Cheng (with Junchen Jiang). She has also worked with numerous student collaborators and visiting students from institutions including MIT, Georgia Tech, and UChicago. Her research has been supported by grants from various sources, including collaborations with Google DeepMind and Microsoft Research where she completed internships. Dr. Pei is involved in several research labs and teams, including collaborations with the CUMLSec group (as seen in repositories like trex and XDA on GitHub). Her work often involves interdisciplinary teams combining expertise in security, machine learning, and software engineering to tackle complex problems in program analysis.