جست‌وجوی استاد و پژوهشگر

Tom Bohmanمشاهده پروفایل

استاد

• Extremal Combinatorics
• Probabilistic Combinatorics
• Discrete Mathematics
• +۳ مورد دیگر

Tom Bohman is a Professor of Mathematical Sciences at Carnegie Mellon University, affiliated with the Mellon College of Science. His research focuses on extremal and probabilistic combinatorics, exploring discrete structures inspired by mathematics, information theory, statistical physics, and computer science. He holds a Ph.D. from Rutgers University and has held postdoctoral positions at MIT and the Mathematical Sciences Research Institute (MSRI). Bohman's work includes studies on random graph processes, such as the triangle-free process and Hamilton cycles in random graphs, as well as hypergraph coloring and Ramsey numbers. His publications span over three decades, addressing topics like coprime matchings, lonely runner conjectures, and dynamic concentration phenomena in combinatorial systems. He teaches advanced courses in combinatorics and discrete mathematics, including Graph Theory and Random Structures & Algorithms. His research has contributed to understanding phase transitions in random processes and the interplay between combinatorial structures and probabilistic methods. While no awards are explicitly listed, his extensive publication record and editorial roles (e.g., with Random Structures & Algorithms ) highlight his academic impact. Bohman's work often bridges theoretical foundations with algorithmic applications, influencing both pure and applied combinatorics.

Boris Bukhمشاهده پروفایل

استاد

• Combinatorics
• Discrete Geometry
• Extremal Graph Theory
• +۷ مورد دیگر

Boris Bukh is a Professor of Mathematics at Carnegie Mellon University (CMU), affiliated with the Department of Mathematical Sciences within the Mellon College of Science. He holds a Ph.D. from Princeton University and has held postdoctoral positions at the University of Cambridge and Churchill College. His research focuses on combinatorics, discrete geometry, extremal graph theory, and geometric selection theorems. He has received prestigious awards such as the Sloan Research Fellowship and the NSF CAREER Award. His work spans topics like Turán problems, geometric configurations, and algebraic methods in combinatorics. Recent publications explore extremal graph structures, convex polytopes in restricted point sets, and applications of random algebraic constructions to computational complexity. Bukh organizes events like the Math Kangaroo competition, fostering mathematics engagement among students. Key contributions include advancements in Ramsey theory, coding theory, and the intersection of combinatorics with geometry. His research often bridges theoretical insights with computational techniques, addressing problems in graph density, geometric incidences, and discrete optimization.

Theis Erik Jendalمشاهده پروفایل

پژوهشگر

• Recommender Systems
• Knowledge Graphs
• Graph Neural Networks
• +۴ مورد دیگر

Theis Erik Jendal is a Researcher at the Department of Computer Science, Aalborg University, affiliated with the Technical Faculty of IT and Design. He specializes in Recommender Systems and Knowledge Graphs, focusing on areas like explainable AI, graph neural networks, and inductive recommendation architectures. He participates in the Poul Due Jensen Professorate in Big Data and Artificial Intelligence (2019–2025), addressing challenges in query processing, knowledge graphs, semantic web, and open data. Key research interests include hypergraph models for explainable recommendation, gated architectures in knowledge graphs, and addressing challenges in knowledge graph embeddings. His work emphasizes interpretability, similarity search, and practical use cases in AI systems. Contributions include 5 peer-reviewed publications since 2020, spanning conferences like ECIR and CIKM, and a dataset contribution to the Yelp Collaborative Knowledge Graph (Zenodo, 2023). His research bridges theory and application, particularly in improving recommendation systems through advanced graph-based methodologies.

• Cyber-Physical Systems
• Internet of Things (IoT)
• Edge Computing
• +۴ مورد دیگر

Prof Jiong Jin is a Full Professor and Academic Director (Research Training) at Swinburne University of Technology's School of Science, Computing and Engineering Technologies. He holds a B.Eng (First Class Honours, Nanyang Technological University) and a Ph.D. (University of Melbourne). His research focuses on networked systems, IoT, edge computing, and robotics automation, with applications in smart manufacturing, transportation, and cities. Research Interests: Control and optimization of networked/embedded systems Edge/fog computing architectures Cyber-physical systems and IoT applications Autonomous robotic systems Real-time distributed algorithms Recent Publications: Recent work includes groundbreaking contributions in edge-IoT frameworks (Farm-LightSeek), cloud-fog automation paradigms, and adversarial machine learning defenses. His research bridges theory and practice through collaborations with industry partners like KDDI Research and IMCRC. Awards & Recognition: 2020 School Research Excellence Award 2019 Faculty Mid-Career Researcher Award Commendation Top 2% global cited scientists (Stanford, 2019) AI 2000 Most Influential Scholars (2021/2022) Grants & Collaborations: Australian Research Council (ARC) Linkage Projects CSIRO Data61 collaborations Industry partnerships with YSTC Energy, IMCRC, and iMOVE Australia Lab & Teams: Leads a multidisciplinary team advancing industrial IoT and smart systems through projects like the Factory-in-a-Box platform and virtual manufacturing solutions. His lab emphasizes practical industry applications of theoretical advancements.

Bei Wangمشاهده پروفایل

مدرس

• Scientific Computing
• Numerical Methods
• Topological Data Analysis
• +۳ مورد دیگر

Bei Wang is an instructor in the Department of Computer Science at the University of Utah, School of Computing. She teaches CS 6210: Advanced Scientific Computing I, focusing on numerical methods and algorithms for scientific computing. Her research interests span topological data analysis, scientific visualization, and algorithm design for data compression and analysis. She contributes to the Computational Engineering and Science (CES) program, supporting interdisciplinary scientific computing education. Her work emphasizes integrating topology into computational frameworks and advancing visualization techniques for complex scientific data. Education: Not explicitly stated in the provided materials. Research Focus: Numerical algorithms, topological methods, and visualization tools for scientific computing. Her recent articles explore topics such as lossy compression with topological guarantees, harmonic chain analysis, and uncertainty-driven visualization. These contributions highlight her expertise in merging computational efficiency with topological rigor. She actively participates in academic initiatives like the CES program, fostering collaboration in scientific computing education and research. Bei Wang’s academic contributions include developing algorithms for topological data structures and enhancing visualization techniques for fluid dynamics and astronomical data. Her work bridges theoretical foundations and practical applications, advancing the analysis of complex datasets in multiple domains.

Yilun Shangمشاهده پروفایل

دانشیار

• Complex Systems
• Network Science
• Nonlinear Dynamics
• +۷ مورد دیگر

Yilun Shang is an Associate Professor in the Department of Computer and Information Sciences at Northumbria University. Previously, he held an Associate Professor position at Tongji University's School of Mathematical Sciences (2014–2018). His research interests span complex systems, network science, and nonlinear dynamics. He earned a PhD in Applied Mathematics from Shanghai Jiao Tong University in 2010, followed by postdoctoral appointments at institutions including the University of Texas at San Antonio and the Hebrew University of Jerusalem. Shang's work focuses on three core areas: (1) properties of complex networks (e.g., robustness, percolation, epidemic models), (2) mathematical properties of random graph models (e.g., degree distribution, connectivity, algebraic indices), and (3) nonlinear dynamics in multi-agent systems (e.g., synchronization, consensus algorithms). He has organized international conferences and delivered invited talks on topics like Estrada indices in random graphs. His academic achievements include the 2016 Dimitrie Pompeiu Prize and a presentation at the 2018 International Congress of Mathematicians. He advises postgraduate students in complex networks and systems and investigates resilient consensus protocols, graph metrics, and topological indices with applications in chemistry and engineering.

Laura Sanitàمشاهده پروفایل

دانشیار

• Combinatorial Optimization
• Approximation Algorithms
• Network Design
• +۳ مورد دیگر

Laura Sanità is an Associate Professor in the Department of Computing Sciences at Bocconi University, Milan, Italy. Previously, she held positions at TU Eindhoven (2020–2022) and the University of Waterloo, Canada, where she was an Assistant Professor (2012–2017) and later Associate Professor (2017–2020). She earned a Bachelor’s and Master’s in Management Engineering from Università di Roma Tor Vergata (2003–2005), followed by a PhD in Operations Research from Università Sapienza di Roma (2009). Her postdoctoral work (2009–2011) was at EPFL’s Discrete Optimization Group. Her research focuses on Combinatorial Optimization , Approximation Algorithms , Network Design , and Algorithmic Game Theory . Key contributions include advancements in node connectivity augmentation, graph stabilization, and polytope diameter analysis. She has received prestigious awards such as the NWO-VIDI Award (Netherlands), NSERC Discovery Accelerator Supplements, and the Early Researcher Award (Ontario). Laura co-organizes the Bocconi Theory Day (May 2024) and serves on program committees for conferences like SODA, ESA, and IPCO. She is an Associate Editor for Mathematical Programming , Mathematics of Operations Research , and Operations Research Letters . Current advisees include PhD students Sean Kafer, Dylan Hyatt-Denesik, and Lucy Verbeck. Her work bridges theoretical foundations and practical applications, with notable publications in Mathematical Programming , SIAM Journal on Optimization , and Operations Research . Recent projects explore stabilization of capacitated matching games and iterative randomized rounding techniques for combinatorial problems.

Blair D. Sullivanمشاهده پروفایل

استاد

• parameterized algorithms
• structural graph theory
• applied discrete mathematics
• +۴ مورد دیگر

Dr. Blair D. Sullivan is a Professor in the School of Computing at the University of Utah. She holds a Ph.D. in Mathematics from Princeton University and a B.S. in Computer Science and Applied Mathematics from Georgia Tech. Prior to Utah, she was an Associate Professor at NC State University (2013–2019) and a Research Scientist at Oak Ridge National Laboratory (2008–2013). Research Interests: Parameterized algorithms, structural graph theory, applied discrete mathematics, random graphs, and combinatorial scientific computing. Current work focuses on integrating structural graph theory into scalable network analysis tools, with applications in robotics, fairness in algorithms, and biomedical networks. Awards & Honors: Fulbright U.S. Scholar to France (AY2025–26) Collegium de Lyon Fellow (2025–2026) Moore Investigator in Data-Driven Discovery (2014–2024) Inaugural Pathway of Progress Alumnae Honoree (Georgia Tech) Key Projects: Developed software tools like CONCUSS , spacegraphcats , and BEAVr for graph analytics and network analysis. Explores edge augmentation for fairness in social networks and robotic motion planning using parameterized algorithms. Grants: NSF-funded projects on fairness in network information flow ($1.5M). NIH grant for network-based drug repositioning algorithms. Labs & Collaborations: Active in the Laboratoire de l'Informatique du Parallélisme (LIP) at ENS de Lyon during her 2025–2026 sabbatical. Collaborates with SIAM and ICERM on workshops and conferences.

Ola Svenssonمشاهده پروفایل

دانشیار

• Approximation Algorithms
• Combinatorial Optimization
• Computational Complexity
• +۳ مورد دیگر

Ola Svensson is an Associate Professor at the School of Computer and Communication Sciences, EPFL. His research focuses on approximation algorithms, combinatorial optimization, computational complexity, and scheduling. He has been supported by grants including the ERC Starting Grant "OptApprox" (2014-2019), SNF grants, and the ERC Consolidator Grant "POTCO" (2023-). He teaches courses such as Advanced Algorithms and Approximation Algorithms and Hardness of Approximation. Education: PhD from IDSIA - Universita della Svizzera italiana (2009) and Master's from Uppsala University (2005). Research Interests: Design and analysis of approximation algorithms for NP-hard problems, scheduling, and computational complexity. He explores limitations of approximation techniques through hardness results and contributes to theoretical computer science. Publications span clustering, scheduling, and graph problems like the Traveling Salesman Problem. Recent work includes learning-augmented algorithms and robust optimization. Awards: I&C teaching award and best paper awards at FOCS (2017) and STOC (2018). Over a dozen PhD students advised, many entering postdocs or industry roles. Labs/Teams: Part of the theory group at EPFL, collaborating on academic projects and course development.

• Machine Learning
• Data Science
• Statistical Inference
• +۳ مورد دیگر

Ilia Nouretdinov is a Teaching Fellow in the Department of Computer Science at Royal Holloway, University of London. His research focuses on advanced statistical methodologies, particularly conformal prediction frameworks, and their applications in machine learning and data science. He contributes to the UN Sustainable Development Goals through interdisciplinary work. Research interests include conformal prediction, statistical testing, error detection in data, and probabilistic modeling. His recent work explores hypergraphical models, change-point detection, and Venn-Abers predictive distributions. These studies emphasize robust statistical methods for real-world data challenges. Key publications from 2023–2025 highlight innovations like Conformal Association Rule Mining (CARM) for error detection and Venn-ABERS testing for time-series analysis. His work is often peer-reviewed and open-access. No scientific awards are listed. He supervised one project, 'Support of collaborative research with Professor A.E. Guterman' (2013), and has no documented grants or labs. His collaborative networks span global institutions, reflecting his international research impact.

Jinwook Leeمشاهده پروفایل

استادیار

• Optimization
• Risk Modeling
• Stochastic Optimization
• +۱۷ مورد دیگر

Jinwook Lee is an Assistant Professor in the Decision Sciences and MIS department at LeBow College of Business, Drexel University. His research focuses on optimization, risk modeling, and artificial intelligence systems, particularly applied to blockchain, supply chain, and financial decision-making. He is actively involved in funded research, including an NSF SBIR grant on blockchain-based supply chain systems. Research Interests: His expertise spans Stochastic Optimization , Mathematical Programming , Blockchain , Operations Research , and Data Privacy . He develops models for uncertainty quantification, risk assessment, and fairness in AI systems using advanced mathematical tools such as pseudo-Boolean functions and multivariate risk measures (e.g., MVaR, MCVaR). His work bridges theoretical rigor with practical applications in finance, supply chains, and forecasting. The recent articles reflect a strong trend toward integrating blockchain and AI with privacy-preserving forecasting , equity metrics , and secure supply chains . His publications span top journals in operations research and management science, indicating a consistent and impactful research trajectory in quantitative decision sciences. Scientific Awards: NSF SBIR Grant (Award #2112078), Co-PI, Mar 2022 Advising and Grants: While direct student names are not listed, his collaborative work with Matthew J. Schneider and András Prékopa suggests active mentorship and research collaboration. He leads a funded project on blockchain-based tracking and distribution with $255,786 from the National Science Foundation, indicating strong grant-writing capability and research leadership. Labs and Research Teams: Though no formal lab name is provided, his research activities suggest leadership in a group focused on blockchain, AI, and risk modeling, likely involving interdisciplinary collaboration across business analytics, computer science, and operations research.

• Machine Learning
• Hypergraph Models
• Social Network Analysis
• +۵ مورد دیگر

Malik Khizar Hayat is a Researcher at Macquarie University's School of Computing within the Faculty of Science and Engineering. He holds a Doctor of Philosophy (PhD) and is associated with research in machine learning, hypergraph models, and dynamic network analysis. His work emphasizes supervised learning techniques applied to social networks, anomaly detection, and graph-level classification. Hayat has collaborated internationally on projects involving heterogeneous hypergraphs and self-supervised learning frameworks. His research outputs span computer science, data mining, and social systems analysis, with a focus on practical applications like recommendation systems and academic ranking methodologies. Education: Doctor of Philosophy (PhD), Macquarie University Research Interests: Development of hypergraph frameworks for session-based recommendations Self-supervised learning techniques in graph embedding Anomaly detection in dynamic networks using co-evolutionary patterns Supervised learning applications for identifying academic rising stars Bibliometric analysis of research collaboration impacts Key Research Trends: Recent work (2023-2025) focuses on advancing hypergraph models for complex network analysis, integrating time-aware mechanisms, and applying machine learning to real-world systems like social recommendations and academic ranking. His methodologies often combine deep learning with graph theory innovations. Grants & Collaborations: Research funded through institutional grants at Macquarie University, with collaboration networks extending to institutions in China (via co-authors like Yang, Jie and Xue, Shuqiang) and other international partners. No specific grant details are disclosed in the provided texts. Labs/Teams: Affiliated with Macquarie University's computational research groups focusing on machine learning and network analysis, though specific lab names aren't mentioned. Active participation in IEEE conferences and journal publications indicates strong ties to computational research communities.

Ginestra Bianconiمشاهده پروفایل

استاد

• Statistical Mechanics
• Network Theory
• Mathematical Physics
• +۷ مورد دیگر

Professor Ginestra Bianconi holds the position of Professor of Applied Mathematics at Queen Mary University of London (QMUL) within the School of Mathematical Sciences. She is also an Alan Turing Fellow at the Alan Turing Institute. Her research focuses on statistical mechanics, network theory, and their applications to complex systems. Notably, she formulated the Bianconi-Barabási model describing Bose-Einstein condensation in networks and pioneered studies on higher-order networks, including simplicial complexes and multilayer networks. Her work bridges statistical mechanics with topology, geometry, and dynamical systems. Education and Career: Ginestra Bianconi earned her PhD in Physics from the University of Notre Dame under Albert-László Barabási. She has held postdoctoral positions at the International Center for Theoretical Physics (ICTP) and the University of Fribourg. She joined QMUL in 2013, progressing from Lecturer to Reader before becoming a full Professor in 2019. Research Interests: Her research spans statistical mechanics of networks, higher-order networks (simplicial complexes), multilayer networks, topological signals, and quantum phenomena on networks. She explores applications in biological networks, neuroscience, and condensed matter systems. Awards and Recognition: Awards include the APS Fellow distinction, Network Science Society Fellowship, and the 2023 Franqui Chair. She is Chief Editor of JPhys Complexity and serves on editorial boards of Scientific Reports and PLOS ONE . Grants and Funding: She has secured funding from EPSRC, the Royal Society, the Simons Foundation, and the Alan Turing Institute for projects on network science applications and theoretical challenges in complex systems. Group and Alumni: Her research group includes current PhD students and alumni who have contributed to fields like simplicial synchronization, network geometry, and multilayer dynamics. Notable alumni include Dr. Arda Halu (Harvard Medical School) and Dr. Jacopo Iacovacci (Imperial College). Labs and Collaborations: She leads research at QMUL’s School of Mathematical Sciences and collaborates with institutions like the Alan Turing Institute and the Francis Crick Institute on interdisciplinary projects.

Oscar Defrainمشاهده پروفایل

دانشیار

• Algorithms
• Combinatorics
• Graph Theory
• +۴ مورد دیگر

Oscar Defrain is an Associate Professor at Aix-Marseille University, affiliated with the Laboratoire d’Informatique et Systèmes (LIS UMR CNRS 7020) and the ACRO team. He holds a Ph.D. from Université Clermont Auvergne (2020) and completed a postdoc at the University of Warsaw. His research focuses on algorithms and combinatorics in graphs, hypergraphs, and lattice structures, with expertise in minimal dominating sets, maximal independent sets, and Boolean function dualization. Education : Ph.D. in Computer Science, Université Clermont Auvergne (2020) Research Interests : Defrain specializes in structural graph theory, hypergraph dualization, and algorithmic enumeration. His work spans combinatorial optimization, parameterized complexity, and lattice theory, with recent contributions to geometric graph certification, metric graph problems, and XOR-CNF signature enumeration. He coordinates the ANR JCJC PARADUAL project and participates in ERC Cutacombs and ANR DISTANCIA. Recent Publications (2024-2025) examine quasi-optimal bounds for induced paths, polynomial-delay isomorphism generation, hypergraph dualization with FPT-delay, and geometric graph certification. These works intersect graph algorithms, combinatorics, and theoretical computer science. Teaching : Defrain teaches graph theory, algorithmic enumeration, and programming (Java/Python) at Aix-Marseille University (M1, L1, L2) and Université Clermont Auvergne (M1, L3). He uses platforms like Moodle and AMeTICE for course materials.

• Collective Decision Making
• Mean-Field Theory
• Network Dynamics
• +۳ مورد دیگر

Thierry-Sainclair Njougouo is a researcher specializing in collective decision-making processes, network dynamics, and complex systems. Their work focuses on applying heterogeneous mean-field theory to analyze decision-making in networks with zealots, leveraging interdisciplinary approaches from computer science, physics, and mathematics. Research Interests : Collective decision-making in agent populations Chimera states in nonlinear dynamical networks Mean-field modeling of heterogeneous systems Interdisciplinary applications to hypergraphs and social dynamics Research Output : Active in computational modeling and theoretical analysis, their recent work explores accuracy-speed trade-offs in best-of-n decisions and phase chimera states on non-local hyperrings. Publications include peer-reviewed journal articles and conference proceedings from 2022–2025. Collaborations : Organized international workshops such as the 18th Italian Workshop on Artificial Life and Evolutionary Computation (WIVACE 2024) and Cameroon’s first school on nonlinear dynamics and hypergraphs.