جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Constraint Programming
• +۴ مورد دیگر

Siegfried Nijssen is a Professor at the Department of Computer Science within the Faculty of Engineering Science at KU Leuven. He is a core member of the Declarative Languages and Artificial Intelligence (DTAI) research group at the Arenberg campus and affiliated with Leuven.AI, the university-wide Institute for Artificial Intelligence. His academic position is designated as 'professor BOF', reflecting a specialized research-focused appointment. Nijssen's research centers on the integration of declarative programming paradigms with machine learning, particularly through constraint programming frameworks. Key focus areas include interpretable rule learning, neural-symbolic integration, and constraint-based optimization for combinatorial problems. His work bridges theoretical computer science with practical applications in bioacoustics, pandemic response modeling, and network analysis, emphasizing transparency and reliability in AI systems. Analysis of his 2021-2024 publications reveals a strong trajectory toward interpretable AI, with significant contributions like RL-Net (combining neural networks with rule-based reasoning) and novel approaches to NP-hard optimization using structured perceptrons. His research consistently targets the intersection of symbolic reasoning and statistical learning, addressing critical challenges in constraint imposition, model explainability, and stochastic optimization. Nijssen currently leads two major research initiatives: 'Declarative Languages for Imposing Constraints on Machine Learning Models' (2025-2027) and the long-term 'Declarative Programming for Machine Learning (DeclaLearn)' project (2025-2035), demonstrating sustained leadership and funding in his specialized domain. These projects extend his foundational work on constraint-based machine learning frameworks. Based at the DTAI research group, Nijssen contributes to KU Leuven's AI ecosystem through collaborative research in logic programming, constraint solving, and data mining. His work with Leuven.AI positions him at the forefront of institutional efforts to advance trustworthy and constraint-aware artificial intelligence systems.

• Natural Language Processing
• Grapholinguistics
• Digital Typography
• +۴ مورد دیگر

Yannis Haralambous is a full professor at the Data Science Department of IMT Atlantique, Brest, France. He is a member of the DECIDE research group affiliated with the CNRS Lab-STICC laboratory. His career spans academia and entrepreneurship, with a focus on natural language processing, grapholinguistics, and knowledge representation since transitioning from algebraic topology and digital typography research in the 1990s. Research Interests Natural Language Processing with emphasis on explainable AI and speech analysis Grapholinguistics, particularly script reform and writing systems Explainable Representation of Complex Systems using automata and time series Fuzzy Logic applications in recruitment analytics Semantic Web extensions to pragmatic and social representation Teaching Graph theory, logic, knowledge representation, and computational linguistics Curriculum design for the Data Science Master of Science at IMT Atlantique Recent Publications 2024: 550-page Springer textbook on NLP with exercises and educator resources 2023: Frameworks for Pragmatic Web and fuzzy logic in recruitment (SN Computer Science, Future Internet) 2022: Conference papers on resume segmentation, job offer term extraction, and complex system modeling Projects 2024-2026: COGITAMUS project on spontaneous e-learning from open-source data Laboratory collaborations with Lab-STICC and La Mètis company Academic Background 1990 Ph.D. in Algebraic Topology (University of Lille I) 2020 HDR (University of Western Brittany) Professional Timeline 1990-2001: Digital typography R&D and Inalco lectureship 2001-present: IMT Atlantique professor 2010: Full DECIDE team member

Rediet Abebeمشاهده پروفایل

استادیار

• Algorithms
• Artificial Intelligence
• Inequality and Distributive Justice
• +۴ مورد دیگر

Rediet Abebe is an Assistant Professor of Computer Science at the University of California, Berkeley, and a Junior Fellow at the Harvard Society of Fellows. Her research examines the intersection of algorithms and inequality, focusing on building mathematical and computational foundations for equitable systems. PhD in Computer Science from Cornell University MA in Applied Mathematics and BA in Mathematics from Harvard University MSc in Part III of the Mathematical Tripos from University of Cambridge Her work spans algorithmic fairness, computational social change, and legal AI applications, with notable projects like "Algorithms on Trial: Interrogating Evidentiary Statistical Software" addressing misuse of software tools in criminal justice systems. Recent publications analyze legal AI, educational equity, and network segregation. Scientific Awards Andrew Carnegie Fellowship (2022-2024) She advises doctoral students Eve Fleisig, Angela Jin, and Ali Shirali, and leads research initiatives including the MD4SG working group and Black in AI. Her work combines technical rigor with societal impact through interdisciplinary collaboration.

Maximilian Wiesmannمشاهده پروفایل

پژوهشگر ارشد

• Algebraic Geometry
• Computational Geometry
• Combinatorics
• +۴ مورد دیگر

Maximilian Wiesmann is a mathematician working as an ELBE Postdoctoral Fellow at the Center for Systems Biology Dresden, Germany. Previously, he completed his PhD at the Max-Planck-Institute for Mathematics in the Sciences in Leipzig under the supervision of Eliana Duarte and Bernd Sturmfels. Mentored by Aida Maraj and Ivo Sbalzarini at CSBD, his work bridges pure mathematics with applications in computational biology and quantum information. His educational background includes: PhD in Mathematics, Max-Planck-Institute for Mathematics in the Sciences, Leipzig (2025) MASt in Pure Mathematics, University of Cambridge (2022) B.Sc. in Mathematics, University of Bonn (2021) Wiesmann's research focuses on the intersection of algebraic geometry and computational mathematics, particularly in nonlinear algebra applications. His work spans theoretical investigations in toric geometry and combinatorics to practical implementations in quantum information and machine learning. He has developed significant software tools including the EulerStratifications.jl Julia package, which accompanies his research on hypersurface families. His publications demonstrate a consistent trajectory from foundational mathematical structures to their applications in statistical modeling and computational science. Analysis of his 11 most recent publications reveals a strong focus on algebraic statistics and geometric approaches to statistical problems, with increasing attention to machine learning applications. His work connects pure mathematical structures with practical computational frameworks, particularly through the lens of nonlinear algebra. The development of specialized software like EulerStratifications.jl demonstrates his commitment to making theoretical advances accessible for practical computation. Wiesmann has served as a tutor in linear algebra and foundational mathematics at the University of Bonn during winter terms 2019/20 and 2020/21. He also completed a Quantum Science Summer Internship at Riverlane in Cambridge, UK in 2021, and was a visiting student at the Institute for Mathematical and Statistical Innovation at the University of Chicago in September and October 2023. At CSBD, Wiesmann is part of a vibrant research environment focused on systems biology, where he applies his expertise in algebraic geometry to biological modeling problems. His work with the Nonlinear Algebra Community on Zenodo demonstrates his commitment to open science and community building in his field.

Anton Kolotilinمشاهده پروفایل

استاد

• Microeconomic Theory
• Information Economics
• Economics
• +۳ مورد دیگر

Professor Anton Kolotilin is a faculty member at the University of New South Wales (UNSW), affiliated with the UNSW Business School. His research focuses on Microeconomic Theory and Information Economics, with particular emphasis on strategic communication, persuasion mechanisms, and game-theoretic models. He has contributed significantly to understanding optimal information disclosure strategies, the design of persuasive communication frameworks, and the economic implications of partisan gerrymandering. His work intersects with political economy, experimental design, and network formation models. Professor Kolotilin has an extensive publication record with over 13 journal articles, 3 working papers, and 13 preprints. His recent research explores topics such as the duality of persuasion, censorship as an optimal strategy, and the application of linear programming to information design. He frequently collaborates on interdisciplinary projects, bridging theoretical economics with practical policy analysis. Key research interests include mechanism design, Bayesian persuasion, strategic information transmission, and the analysis of social and economic networks. While no specific grants or awards are listed, his prolific output underscores his contributions to academic discourse in economics and related fields.

• Applied mathematics
• Pure mathematics
• Theory of computation
• +۷ مورد دیگر

Dr. Ban Pin Tan is a Mathematics Lecturer at the National University of Singapore (NUS), holding a PhD from NUS awarded in 1997. His research focuses on graph theory and digraph properties, particularly in kings in multipartite digraphs, eccentric digraphs, Roman domination, bondage numbers, and optimal orientations. He has contributed to over 8 peer-reviewed publications since 1995, addressing structural properties of tournaments and multipartite graphs. His work includes foundational studies on dominance hierarchies, 3/4-kings enumeration, and digraph orientation optimizations. Dr. Tan emphasizes teaching methodologies that foster deep conceptual understanding through systematic lecture notes, 'Pause and Think' sessions, and problem-solving exercises. He believes in inspiring students to apply mathematical principles to real-world challenges, aligning with Singapore's knowledge-based economy goals. His pedagogical approach combines rigorous problem sets with frequent quizzes to enhance critical thinking and engagement. His long-term research program includes: (1) studying kings-of-kings in semicomplete multipartite digraphs, (2) analyzing eccentric digraphs of various graph classes, (3) determining optimal orientations for graph families, (4) evaluating Roman domination and bondage numbers, and (5) exploring magic graph properties. Current investigations focus on eccentric digraphs, Roman domination extensions to digraphs, and magic graph characteristics.

Danijel Kučakمشاهده پروفایل

مدرس ارشد

• Software Engineering
• Machine Learning
• Educational Technology
• +۴ مورد دیگر

Danijel Kučak is a Senior Lecturer at the University of Algebra since 2007. He holds a Master's in Mathematics from the Faculty of Science (2002) and a PhD in Information Sciences from the Faculty of Philosophy, University of Zagreb (2025). With 10 international programming certifications, his expertise spans web application development using .NET and Java platforms. He has authored/co-authored 5 books and over 20 scientific papers focusing on software engineering, machine learning, and educational technology. Beyond academia, he owns a programming business, contributing to systems like MedicalBit (for lung disease clinics) and CRIS (Croatian Road Inspection System). His research emphasizes gamification in education, blockchain applications, and AI-driven solutions. Research interests include machine learning applications, gamification strategies for programming education, and secure blockchain systems. His work bridges theoretical computer science with practical implementations in healthcare, transportation, and education. Notable contributions include empirical studies on gamification's long-term impact on student outcomes and innovative approaches to E-learning platforms. Awards: Multiple Best Lecturer Awards at the University of Algebra Business Experience: Developed custom software systems for healthcare and traffic management Publications: Over 20 peer-reviewed articles and 5 books on software engineering and educational technology Labs/Teams: Active in developing IoT architectures for environmental monitoring and AI models for educational outcomes prediction. His programming business integrates academic research with real-world system implementations.

Karl-Friedrich Israelمشاهده پروفایل

مدرس

• Macroeconomics
• History of Economic Thought
• Economic Methodology
• +۳ مورد دیگر

Karl-Friedrich Israel is a Lecturer in Economic Sciences (HDR) at Université Catholique de l’Ouest (UCO Angers), France. He holds a PhD in Economics from UCO Angers (2017) and an HDR from Université Paris 1 Panthéon-Sorbonne (2023). His research focuses on macroeconomics, history of economic thought, and Austrian economics, with a particular emphasis on monetary policy’s societal impacts. He is affiliated with the Mises Institute (USA) and the Liberales Institut (Switzerland). Education: PhD in Economics, UCO Angers, 2017 HDR, Université Paris 1 Panthéon-Sorbonne, 2023 MSc in Applied Statistics, Oxford University MSc in Economics, Humboldt-Universität zu Berlin Diploma in Economic Statistics, ENSAE ParisTech Research Interests: Austrian economics, monetary theory, econometrics, and the unintended consequences of central banking. Recent work critiques inflation measurement, explores deadweight loss models, and compares monetary theories of Menger/Wieser vs. Hayek/Mises. Awards: Gary G. Schlarbaum Prize (2021) for research and teaching excellence. Active in organizing international seminars on political economy and philosophy. Teaching: Taught at UCO Angers, University of the Saarland (Germany), and Berlin School of Economics and Law. Courses include Political Economy, Macroeconomics, and Monetary Policy Analysis. Labs/Teams: Member of EGEI (Équipe de Groupe de Recherche en Économie Internationale) and GRANEM (Groupe de Recherche en Analyse de la Structure Économique des Marchés).

Mark Uttingمشاهده پروفایل

دانشیار

• Software Verification
• Model-Based Testing
• Formal Methods
• +۵ مورد دیگر

Associate Professor Mark Utting is affiliated with the University of Queensland (UQ), specifically within the School of Electrical Engineering and Computer Science. He holds the rank of Associate Professor and focuses on software verification, model-based testing, and programming language design. His academic journey includes roles at multiple Queensland universities, Waikato University (NZ), and the University of Franche-Comte (France). He earned his PhD from UNSW on object-oriented language semantics. Utting has authored over 80 publications and the book 'Practical Model-Based Testing: A Tools Approach.' His current research emphasizes verifying compilers, blockchain smart contracts, ARM64 binaries, and AI-generated code. Key projects include the Algorand Centre for Sustainability Informatics and the BASIL project on secure information-flow logics. His research spans formal methods, theorem proving, and software engineering. Utting leads supervision in areas like compiler verification and smart contract analysis, with active involvement in grants such as the 'Directed and Incremental Analysis for DevSecOps' project. Utting collaborates on interdisciplinary projects, including energy grid simulations and cybersecurity strategies. His work bridges academia and industry, with past experience in genomics and manufacturing software development.

Miki Hermannمشاهده پروفایل

پژوهشگر

• Computational Complexity
• Constraint Satisfaction Problems
• Satisfiability
• +۴ مورد دیگر

Miki Hermann is a CNRS Researcher at the Laboratory of Computer Science (LIX) at École Polytechnique, France. He is affiliated with the Algorithms and Complexity research group and maintains an active research program in theoretical computer science and computational logic. His research interests span computational complexity, constraint satisfaction problems, satisfiability, and logic in computer science. Hermann's work focuses on the theoretical foundations of computational problems, particularly examining complexity classifications, counting problems, and algorithmic solutions for logical and combinatorial structures. His research bridges theoretical computer science with practical applications in artificial intelligence and data analysis. The analysis of his recent publications reveals a consistent focus on computational complexity across various logical frameworks. His work demonstrates expertise in classifying the complexity of constraint satisfaction problems, propositional logic systems, and graph-theoretic problems. Notable research directions include minimal inference problems, counting complexity, and applications of satisfiability to big data transformation through his MCP project. Hermann is part of the Algorithms and Complexity research group at LIX (CNRS, UMR 7161), where he contributes to theoretical computer science research. He has developed significant software projects including MCP (Multi-Classification Project) for transforming datasets into propositional formulas and GYT (Generalized Young Tableaux) for solving variadic polynomial equations over non-negative integers.

Leslie Trotterمشاهده پروفایل

استاد

• Discrete Optimization
• Combinatorial Optimization
• Graph Theory
• +۶ مورد دیگر

Leslie Trotter is a Professor in the School of Operations Research and Industrial Engineering within the College of Engineering at Cornell University, where he has served since 1975. He currently holds the position of Associate Dean for Undergraduate Programs and maintains active research and teaching roles. His educational background includes: A.B. in Mathematics, Princeton University (1965) M.S. in Industrial and Systems Engineering, Georgia Institute of Technology (1971) Ph.D. in Operations Research, Cornell University (1973) He completed a postdoctoral appointment at the University of Wisconsin and served on the Yale University faculty prior to joining Cornell. Professor Trotter's research centers on discrete and combinatorial optimization, with specific focus on graph stability numbers, abstract duality models, parallel computing implementations for integer programming, orthogonal projection algorithms for linear programming, and integer programming applications in number theory. His work integrates operations research, mathematics, and computer science to address strategic decision-making challenges through model development, mathematical analysis, and algorithmic implementation. His accolades include: Senior U.S. Scientist Award from the Humboldt Foundation Seven Cornell University teaching excellence awards 15 consecutive years of listing in Who's Who in America Professor Trotter has directed over 25 Ph.D. dissertations and sponsored 10 postdoctoral researchers. His administrative service includes multiple terms as Director and Associate Director of Undergraduate Studies, Director of Graduate Studies in OR&IE, Chair of the College of Engineering's International Programs Committee, and current Chair of the Common Curriculum Governing Board. At the university level, he significantly influenced faculty salary policies and teaching responsibilities through service on the Financial Policies Committee.

Sourya Royمشاهده پروفایل

استادیار

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

Sourya Roy is an Assistant Professor in the Department of Computer Science at the University of Iowa. Prior to his academic career, he worked as a Data Scientist at Foursquare. He earned his PhD in Computer Science from the University of California, Riverside in 2022, advised by Silas Richelson and Amey Bhangale. Education: PhD in Computer Science (University of California, Riverside, 2022) His research spans theoretical and applied domains, focusing on pseudorandomness, coding theory, and cryptography in theoretical computer science, while also contributing to computer vision and machine learning applications. Recent publications emphasize expander graph theory, coding algorithms, and spatio-temporal modeling. Dr. Roy is actively seeking PhD students to collaborate on theoretical computer science projects. His publications highlight expertise in pseudorandomness, expander graphs, and scalable unsupervised learning techniques. Key conferences include FOCS, ECCV, and IEEE Transactions on Information Theory.

• Energy Systems
• Smart Grids
• Electricity Markets
• +۶ مورد دیگر

Sergio Potenciano Menci is a Postdoctoral Researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg. His research focuses on smart grids, energy systems, and machine learning applications in energy management. He contributes to projects like the Energy Synchronization Platform, aiming to optimize industrial demand response and grid flexibility. His work addresses challenges in electricity markets, distributed energy resources, and privacy-preserving technologies like federated learning. Key areas of expertise include: Design of local flexibility markets for congestion management Short-term load forecasting with spatiotemporal models Privacy-preserving federated learning for energy data Optimization of battery storage in intraday markets Scalability analysis of smart grid functions Publications emphasize energy market structures, data imputation for smart meters, and regulatory frameworks for grid safety. His work intersects technical innovation with policy considerations, addressing both European energy systems and global climate risks. Active in collaborative projects like InteGrid and InterFlex, he explores grid management services and functional scalability in distribution networks. Research outcomes include frameworks for assessing vulnerability of national economies to physical climate risks and taxonomies for local flexibility markets.

Martin Peiمشاهده پروفایل

مدرس

• Graph Theory
• Combinatorics
• Optimization
• +۱ مورد دیگر

Martin Pei is a faculty member at the University of Waterloo's Department of Combinatorics and Optimization within the Faculty of Mathematics. He teaches courses such as CO 227 (Introduction to Optimization) and CO 370 (Deterministic OR Models). His research interests stem from his PhD thesis on 'List colouring hypergraphs and extremal results for acyclic graphs,' completed under Prof. Penny Haxell. Though his CV is noted as outdated, his academic focus aligns with combinatorial optimization and graph theory. Outside academia, Pei is an avid board gamer with a substantial collection, reflecting his engagement with strategic and social games. His personal interests also include photography, as evidenced by his 500px profile showcasing landscape and travel photos. He maintains a presence on platforms like BoardGameGeek since 2007, indicating long-term involvement in gaming communities.

• Parallel and Distributed Systems
• Graph Optimization
• Big Data
• +۷ مورد دیگر

Tomaž Dobravec is an Assistant Professor at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia. He is actively involved in research and teaching in computer science, with a focus on system software, algorithms, and distributed computing. His work is supported by national research programmes and structural funds projects. His research interests include: Parallel and Distributed Systems Graph Optimization and Big Data Combinatorial Scientific Computing Mobile and Industrial Operating Systems Interactive Educational Software Development The trend in his research projects indicates a strong emphasis on algorithmic efficiency, large-scale data processing, and practical software solutions for both industrial and educational contexts. His involvement in long-term ARRS programmes (e.g., P2-0095) highlights sustained contributions to core computing disciplines. He has participated in several research and development projects, including: P2-0095 - Parallel and distributed systems (2020–2026) N2-0171 - Graph Theory and Combinatorial Scientific Computing (2021–2023) PKP6 - Mobile Operating System for Industrial Use (MobiIOS) (2020) Graph Optimisation and Big Data (2016–2019) Development of the Visual Assistant project (2016–2017) Design and programming of interactive electronic textbooks (2017) He teaches key undergraduate courses such as System Software Programming 2 , Algorithms and Data Structures 2 , and The C Programming Language , indicating a strong foundation in core computer science education. While no formal advisees are listed, his role in research projects suggests mentorship of students and junior researchers. There is no mention of scientific awards in the provided text.