جست‌وجوی استاد و پژوهشگر

• Constraint Satisfaction Problems
• Computational Complexity
• Universal Algebra
• +۳ مورد دیگر

Michał Wrona is a Professor in the Department of Algorithmics at the Faculty of Mathematics and Computer Science, Jagiellonian University. His research focuses on constraint satisfaction problems (CSPs), computational complexity, universal algebra, and model theory, particularly concerning finite and omega-categorical structures. He holds a habilitation and has held postdoctoral positions at École Polytechnique (France) and Linköping University (Sweden). His current grant (2021–2026) investigates constraint satisfaction problems for infinite homogeneous structures. Education: M.Sc. in Computer Science (2004, University of Wrocław), Ph.D. in Mathematics (2009, University of Wrocław). Key Roles: Principal Investigator of the National Science Centre grant on infinite-domain CSPs. His work bridges theoretical computer science and mathematical logic, addressing foundational questions in algorithmic complexity and constraint satisfaction. Notable contributions include advances in quantified CSPs, tractability frontiers, and symmetries in infinite-domain structures. Research has been published in top venues like SIAM Journal on Computing, IEEE LICS, and ACM Transactions on Computational Logic. He actively contributes to the algorithmics research group and collaborates internationally on topics like universal algebra and computational complexity.

Stefan Kratschمشاهده پروفایل

استاد

• Parameterized complexity
• Efficient preprocessing
• Computational complexity

Stefan Kratsch is a Professor of Theoretical Computer Science at the Institute of Computer Science, Humboldt University of Berlin. He holds this position since September 2017 and has previously held academic roles at the University of Bonn (2015–2017) and Technical University Berlin (2012–2014). His research focuses on parameterized complexity, efficient preprocessing, and computational complexity, with a strong emphasis on theoretical foundations and algorithm design. University: Humboldt University of Berlin Department: Institute of Computer Science (Algorithm Engineering group) Academic Rank: Professor Email: stefan.kratsch@hu-berlin.de, kratsch@informatik.hu-berlin.de Kratsch’s recent publications highlight his work on advanced algorithmic techniques for graph problems. Key areas include kernelization methods, flow-augmentation for connectivity problems, and tight complexity bounds for classical problems parameterized by structural measures like clique-width and cutwidth. His theoretical contributions aim to bridge preprocessing efficiency with computational hardness. Stefan actively engages in academic service, including organizing workshops and serving on program committees for leading conferences such as IPEC, LATIN, and SWAT. He has reviewed for numerous journals and grant panels, including the European Research Council and German Research Foundation. Advising: He supervised PhD candidate Michael Piechotta, whose defense is scheduled for September 2025.

• Theoretical Computer Science
• Constraint Satisfaction Problems
• Algorithms and Complexity Theory
• +۳ مورد دیگر

Silvia Butti is a Senior Research Associate at the University of Oxford's Department of Computer Science and holds a Junior Research Fellowship at Lady Margaret Hall. Her research focuses on theoretical computer science, particularly constraint satisfaction problems (CSPs), algorithms, and complexity theory. She collaborates with Standa Živný on projects funded by the UKRI-ERC grant NAASP. She earned a PhD from Universitat Pompeu Fabra, an MSc in Mathematics and Foundations of Computer Science from Oxford, and a BSc from University College London. Her work bridges algebraic methods and computational complexity, exploring topics like promise CSPs, hierarchies (e.g., Sherali-Adams, Weisfeiler-Leman), and approximation algorithms. She has been awarded the INPhINIT fellowship and Marie Skłodowska-Curie COFUND funding. She has taught courses on Combinatorial Optimization, Computational Complexity, and Probability and Computing at Oxford, and Discrete Mathematics at UPF. Butti actively participates in academic outreach, including Maths Fest and Royal Institution Masterclasses, and has presented her research globally at venues like ICALP, LICS, and STACS. Her contributions span theoretical advancements and collaborative projects, with a focus on solving foundational challenges in computational complexity.

David G. Mitchellمشاهده پروفایل

دانشیار

• Propositional Satisfiability
• Constraint Satisfaction Problems (CSP)
• Formal Verification
• +۳ مورد دیگر

David G. Mitchell is an Associate Professor in the Department of Computing Science at Simon Fraser University (SFU). He is part of the School of Computing Science and affiliated with the Computational Logic Laboratory. His research focuses on propositional satisfiability (SAT) and finite domain constraint satisfaction (CSP), exploring their complexity and practical algorithm design for applications in formal verification and related tasks. Education : Ph.D., Computer Science, University of Toronto, 2002 M.Sc., Computing Science, Simon Fraser University, 1993 B.Sc., Cognitive Science and Artificial Intelligence, University of Toronto, 1989 Research Interests : Mitchell's work bridges mathematical logic and computer science, emphasizing SAT-based problem solving, constraint modeling languages, and automated reasoning. His contributions include advancements in clause learning, resolution complexity, and declarative programming frameworks for solving NP problems. He has developed tools like MXC and Enfragmo, which apply SAT techniques to real-world applications. Students : Mitchell has advised numerous graduate students, including current PhD candidates Mona Mehdizadeh and Sima Jamali, and past advisees such as Sahba Etezad, Heng Liu, and Faraz Hach. His students' work spans algorithm optimization, constraint satisfaction, and formal verification. Labs/Teams : He is a member of the Computational Logic Laboratory at SFU, contributing to research in logic-based problem-solving and algorithm development.

Libor Bartoمشاهده پروفایل

استاد

• Universal Algebra
• Computational Complexity
• Constraint Satisfaction Problems
• +۶ مورد دیگر

Libor Barto is a full professor at the Department of Algebra, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic. He is a leading researcher in universal algebra and computational complexity, with a strong focus on constraint satisfaction problems (CSPs) and their algebraic foundations. He leads the ERC Synergy Grant POCOCOP and previously led the ERC Consolidator Grant CoCoSym, and is deeply involved in advancing the algebraic theory of promise constraint satisfaction. Research Interests: His primary research areas include universal algebra, computational complexity, constraint satisfaction problems (CSP), promise constraint satisfaction problems (PCSP), clone theory, and the algebraic approach to logic and computation. He investigates the interplay between algebraic structures and computational tractability, particularly through polymorphisms, minions, and Taylor algebras. The recent articles reflect a strong trend in unifying algebraic approaches to CSP, exploring promise variants, approximation through plurimorphisms, symmetries in structures, and the role of Weisfeiler-Leman hierarchies. His work often appears in top-tier journals and conferences such as the Journal of the ACM, SIAM Journal on Computing, and LICS. Fellow of the Learned Society of the Czech Republic (since 2024) ERC Synergy Grant (POCOCOP, 2023–2029) ERC Consolidator Grant (CoCoSym, 2018–2023) Charles University Research Center (UNCE) Grant (PI, 2024–2029) Libor Barto advises PhD students and leads research teams under major grants like POCOCOP and CoCoSym. He has secured substantial funding from the European Research Council and the Czech Science Foundation (GACR). He is actively involved in the academic community as an editor of Algebra Universalis and Acta Scientiarum Mathematicarum , and has served on program committees for LICS, ICALP, and STACS. He has organized major workshops, including at the Fields Institute and multiple AAA and SSAOS conferences. He is involved in several research labs and collaborative teams, particularly through the Department of Algebra at Charles University, the POCOCOP project (with M. Bodirsky and M. Pinsker), and the CoCoSym ERC team. His work fosters international collaboration with researchers in France, Germany, Austria, Poland, Canada, and the USA.

• Theoretical Computer Science
• Computational Complexity
• Approximation Algorithms
• +۶ مورد دیگر

Per Austrin is a Lecturer at the Royal Institute of Technology (KTH) in the Department of Theoretical Computer Science. He has been actively involved in teaching courses such as Algorithms and Complexity (DD2352) , Advanced Algorithms (DD2440) , and Problem Solving and Programming Under Pressure (DD2458) , serving as examiner and course coordinator for several programs. His research focuses on the foundations of computational complexity, approximation algorithms, and cryptographic barriers. Key areas include hardness of approximation for constraint satisfaction problems (CSPs), combinatorial optimization, and theoretical limits in cryptography and coding theory. His work often intersects with probabilistic methods and quantum computing challenges. Recent publications highlight his contributions to understanding lower bounds in optimization problems, inapproximability thresholds, and complexity in structured combinatorial problems. Detailed information about his work can be found on his personal website: Per Austrin's Website .

• Quantum Computing
• Computational Complexity
• Theoretical Computer Science
• +۳ مورد دیگر

Demian Banakh is affiliated with the Department of Algorithmics at the Faculty of Mathematics and Computer Science, Jagiellonian University. His research focuses on quantum computing and computational complexity, as evidenced by his work on quantum CSP strategies and hardness conditions for promise constraint satisfaction problems (PCSPs). He completed a Master's thesis titled "Dichotomies for Boolean minions of weighted-sum functions with threshold." Banakh is part of the Algorithmics Research Group and collaborates with researchers like Marcin Kozik. His publications appear in prestigious conferences such as IEEE Symposium on Logic in Computer Science (LICS). He is involved in academic administration, contributing to technical infrastructure (e.g., university WiFi access management) and maintaining departmental resources. Contact details include room 3148 at the department, though an explicit email address is not provided in the text.

Harold Connamacherمشاهده پروفایل

دانشیار

• Random Constraint Satisfaction Problems
• Algorithms
• Artificial Intelligence
• +۴ مورد دیگر

Harold Connamacher is an Associate Professor in the Department of Computer and Data Sciences at Case School of Engineering, Case Western Reserve University . He holds the Robert J. Herbold Professor of Transformative Teaching title and serves as Associate Chair in his department. University: Case Western Reserve University School: Case School of Engineering Department: Computer and Data Sciences Academic Rank: Associate Professor Research Interests Harold's research focuses on random constraint satisfaction problems , algorithms , and artificial intelligence . He applies theoretical computer science techniques to analyze problem structures and enhance algorithm performance, particularly in combinatorial optimization and computational complexity. Teaching Interests : Programming languages, discrete mathematics, graph theory, algorithms, data structures, computer science theory, and database programming. His work in computer science education has been recognized with multiple awards, including the Carl F. Wittke Award for Excellence in Undergraduate Teaching (2019) and the Delta Upsilon Srinivasa P. Gutti Engineering Teaching Award (2017). Scientific Awards Carl F. Wittke Award for Excellence in Undergraduate Teaching 2019 Guy Savastano Outstanding Educator Award 2019 Delta Upsilon Srinivasa P. Gutti Engineering Teaching Award 2017 Tau Beta Pi Publications span topics in theoretical computer science , machine learning , and mathematical combinatorics , including works on satisfiability thresholds, spanning tree optimization, and educational methodologies in programming instruction.

Patrick LESERFمشاهده پروفایل

دانشیار

• Embedded Systems Engineering
• Model-Based Systems Engineering (MBSE)
• Automotive Software Architecture
• +۴ مورد دیگر

Patrick LESERF is an Associate Professor at ESTACA, a French engineering school specializing in transport systems, where he serves as a Teacher-researcher in ESTACA'Lab. Since joining in 2005, he has founded the embedded systems laboratory, developed academic programs including a specialized master's degree in Embedded Lighting Systems (with IOGS, Renault, and PSA), and led research collaborations with CEA-LIST on transport systems reliability using model-based approaches. His research centers on Model-Based Systems Engineering (MBSE) for embedded automotive systems, with expertise in SysML modeling, safety-security co-optimization, and AUTOSAR methodology. Key contributions include architecture optimization for critical systems, real-time communication networks (CAN/LIN/FlexRay), and embedded OS design. Recent work expands into intelligent transportation systems and electric vehicle powertrain optimization. Analysis of his 2023-2024 publications reveals a strategic shift toward AI-integrated solutions: neural architecture search for efficient deep learning models, predictive analytics for urban traffic optimization using Eclipse MOSAIC, and multi-objective optimization of battery/motor systems in electric vehicles. This evolution demonstrates integration of traditional embedded systems engineering with data-driven approaches. Scientific Awards: No major awards documented in source materials. Advising and Grants: Co-supervises PhD candidate Léo Pouy on MBSE for autonomous decision-making systems. Led the European Matelo project (statistical testing for embedded software) during industrial tenure and established ESTACA's joint research team with CEA-LIST. Directs curriculum development for embedded systems education across multiple specialized master's tracks. Labs and Teams: Founded ESTACA'Lab's embedded systems research group in 2005, scaling it to four teacher-researchers. The lab focuses on model engineering, transport communication networks, and software architecture optimization, supported by a 350-hour teaching program covering embedded networks, OS, and microcontrollers. Current activities include steer-by-wire systems and IoV applications.

Pedro Barahonaمشاهده پروفایل

استاد مهمان

• Constraint Satisfaction Problems (CSP)
• Satisfiability (SAT) Solving
• Artificial Intelligence
• +۳ مورد دیگر

Prof. Pedro Barahona is a former Visiting Professor at the International Center for Computational Logic (ICCL) , affiliated with the Faculty of Computer Science at TU Dresden. His research focuses on translating Constraint Satisfaction Problems (CSP) into Satisfiability (SAT) frameworks, with contributions to encoding techniques and algorithmic optimization. His work bridges formal logic, artificial intelligence, and computational complexity. Key publications (2013-2014) address SAT-based approaches for CSP constraints, hierarchical hybrid encodings, and efficient logical translations. Though listed as a former member, his contributions remain part of the ICCL's research portfolio. No awards or grants are explicitly noted in the provided materials.

Florent Madelaineمشاهده پروفایل

استاد

• Theoretical Computer Science
• Finite Model Theory
• Complexity Theory
• +۴ مورد دیگر

Florent Madelaine is a Professor at the Laboratoire d'Algorithmique Complexe et Logique (LACL) and a member of the IT department at the IUT Sénart-Fontainebleau, University of Paris-Est Créteil (UPEC) since September 2018. He leads the LCP (Logic, Computation, and Programming) research team since 2020 and is the head of the IT department at IUT. His research focuses on theoretical computer science, particularly finite model theory, complexity theory, and constraint satisfaction problems (CSP/QCSP/MMSNP). Key contributions include work on the algebraic tractability conjecture for MMSNP and complexity dichotomies for quantified constraints. His research is funded by projects like ANR Difference (of which he is UPEC PI). Research Interests: Quantified Constraint Satisfaction Problems (QCSP) Finite Model Theory and Descriptive Complexity MMSNP and its algebraic properties Algorithmic Model Theory Computational Complexity Dichotomies Recent work explores model-checking for fragments of first-order logic, guarded extensions of MMSNP, and the interplay between algebraic methods and computational complexity. He collaborates widely, notably with Barnaby Martin and Manuel Bodirsky. Notable Awards: None explicitly listed, but contributions recognized in major conferences (LICS, ICALP, CSR). Advising: Supervised PhD students including Alexey Barsukov (postdoc at Charles University, Prague) and co-supervises Fatemeh Ghasemi. Active in mentoring within the LCP team. Labs/Teams: Core member of LACL, involved in CNRS GdR IM national research group.

• Evolutionary Algorithms
• Swarm Robotics
• Optimization
• +۳ مورد دیگر

Prof. Ben Paechter is a leading academic at the School of Computing Engineering and the Built Environment , Edinburgh Napier University, specializing in evolutionary algorithms and swarm robotics. As a Professor , he has shaped research directions in computational optimization and adaptive systems for over two decades. Research Themes : Evolutionary Swarm Robotics, Multi-Objective Optimization, Hyper-heuristics, Neural Architecture Search Key Projects : FOCAS (European Commission), KTP: Intelligent Agents (Innovate UK), PerAda (Self-Aware Systems) Collaborations : CAVES Research Group, Centre for Algorithms, Visualisation and Evolving Systems His article trends demonstrate expertise in cross-domain hyper-heuristics, swarm behavior tree evolution, and neural network architecture optimization. Recent works (2023-2024) focus on GPU-accelerated evolutionary algorithms and hierarchical swarm control systems. Scientific Awards : Fellow of the British Computer Society (FBCS) Chartered IT Professional (CITP) GECCO Best Paper Nomination (2018) Advising & Grants : Supervised 5 PhD students including Paul Lapok (planar mechanisms) and Andreas Steyven (swarm diversity). Secured £2.2M+ in funding across 13 projects, including EU FOCAS (£639,999) and Innovate UK grants.

• Robotics
• Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Thomas Henderson is a Professor of Computer Science at the University of Utah, specializing in robotics, artificial intelligence, and computer vision. His work emphasizes multisensor data fusion, autonomous navigation, and industrial automation. He has contributed to foundational algorithms in constraint satisfaction and robotic manipulation. Research interests include 3D object representation, sensor integration, and system architecture design for industrial applications. Notable contributions span wireless sensor networks, neural controller evolution for mobile robots, and safety-critical simulations of fire/explosion scenarios. Collaborations include projects with Nanyang Technological University and industry partners in manufacturing automation. His publications (over 200 cited works) demonstrate interdisciplinary impact across robotics, computer vision, and AI. Current focus areas include strategic conflict management for unmanned aircraft systems (UAS) and resilient networked robotics solutions.

Youngtak Sohnمشاهده پروفایل

استادیار

• Probability Theory
• High-Dimensional Statistics
• Random Graphs
• +۳ مورد دیگر

Youngtak Sohn is an Assistant Professor in the Division of Applied Mathematics at Brown University. His research bridges probability theory with statistical physics, machine learning, and theoretical computer science. Current investigations focus on high-dimensional statistical inference, random constraint satisfaction problems, and phase transitions in disordered systems. Previously, he was a postdoctoral researcher at MIT and earned his PhD in Statistics from Stanford University under Amir Dembo. Key research areas include: Phase transitions in random constraint satisfaction problems Statistical-computational gaps in high-dimensional inference Replica symmetry breaking in spin glass models Sharp thresholds in graph inference and community detection His publications demonstrate deep mathematical rigor, with recent work establishing fundamental limits in statistical estimation using low-degree polynomials and characterizing exact phase transitions in stochastic block models. The research consistently develops new mathematical frameworks for understanding computational thresholds in high-dimensional statistics and statistical physics. He mentors students through programs like MIT PRIMES, guiding projects on hypergraph coloring thresholds. His teaching portfolio includes graduate courses in probability theory and seminars on statistical learning theory.

Vladimir Kolmogorovمشاهده پروفایل

استاد

• Discrete Optimization
• Combinatorial Optimization
• Global Optimization
• +۳ مورد دیگر

Vladimir Kolmogorov is a Professor at the Institute of Science and Technology Austria (IST Austria), leading the Kolmogorov Group focused on Discrete Optimization. He previously held positions as Assistant Professor at IST Austria (2011–2014), Lecturer at University College London (2005–2011), and Associate Researcher at Microsoft Research (2003–2005). His research spans algorithm development for graphical models, combinatorial optimization, and applications in computer vision. Education: M.S. in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology, and Ph.D. in Computer Science from Cornell University (2003). Research interests include complexity classifications, algorithm design for discrete optimization problems (e.g., max-flow, min-cost matching), and applications in computer vision. Notable contributions include the 'Boykov-Kolmogorov' max-flow algorithm and 'Blossom V' for minimum cost perfect matching. Honors include the ERC Consolidator Grant (2014–2019), Koenderink Prize (2012), and best paper awards at CVPR and ECCV. His work on optimization algorithms and theoretical complexity has significantly impacted computer vision and combinatorial optimization fields. Advising and grants: Supervised multiple PhD students and postdocs, with current advisees including Martin Dvořák and Pavel Arkhipov. His team explores inference in graphical models, combinatorial optimization, and discrete optimization theory.