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Bei WangView profile

Lecturer

• Scientific Computing
• Numerical Methods
• Topological Data Analysis
• +3 more

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.

Blair D. SullivanView profile

Professor

• parameterized algorithms
• structural graph theory
• applied discrete mathematics
• +4 more

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 SvenssonView profile

Associate Professor

• Approximation Algorithms
• Combinatorial Optimization
• Computational Complexity
• +3 more

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
• +3 more

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 LeeView profile

Assistant Professor

• Optimization
• Risk Modeling
• Stochastic Optimization
• +17 more

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
• +5 more

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 BianconiView profile

Professor

• Statistical Mechanics
• Network Theory
• Mathematical Physics
• +7 more

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.

Corinna CoupetteView profile

Assistant Professor

• Legal Network Science
• Computational Legal Theory
• Graph Algorithms
• +7 more

Corinna Coupette is an Assistant Professor of Computer Science at Aalto University's School of Science, where she leads the Telos Lab at the intersection of law, computer science, and complex systems. She serves as Program Director for the interdisciplinary Master’s Program in Information Networks and holds affiliations as a Guest Researcher at Max Planck Institute for Informatics, Research Affiliate at Max Planck Institute for Tax Law and Public Finance, and member of ELLIS . Ph.D. in Computer Science (Dr. rer. nat., Saarland University, 2023) Ph.D. in Law (Dr. iur., Bucerius Law School, 2018) Her research focuses on computational legal theory and legal network science , using computational methods to model legal systems as networks and graphs. Key themes include responsible AI regulation, algorithmic fairness, and hypergraph formalization of legal data. Recent work spans quantum computing limits, graph-learning evaluation frameworks, and latent-space topological analysis. She has received accolades such as the Digital Futures Postdoctoral Fellowship (2023), Caroline von Humboldt Prize (2022), and Otto Hahn Medal (2020). Her publications in venues like ICML, KDD, and ICLR emphasize scalable algorithms for legal and social systems. Scientific Awards Outstanding Reviewer (multiple conferences, 2022–2025) Digital Futures Fellowship (2023) Caroline von Humboldt Prize (2022) Otto Hahn Medal (2020) Bucerius Best Dissertation Award (2018) Corinna actively mentors and collaborates with students and researchers globally, emphasizing interdisciplinary approaches to data-driven legal analysis and complex systems modeling.

• Random Matrix Theory
• Sparse Data Analysis
• Community Detection
• +3 more

Yizhe Zhu is an Assistant Professor of Mathematics at the University of Southern California , specializing in theoretical and applied aspects of high-dimensional data analysis. His research bridges mathematics, computer science, and statistics, with a focus on random matrix theory, sparse data structures, and algorithmic analysis for machine learning and privacy-preserving data methods. Research Interests Yizhe Zhu’s work addresses fundamental questions in: Random Matrix Theory : Spectra of sparse and structured matrices, including outlier detection and universality. Graph and Hypergraph Analysis : Community detection, spectral properties, and non-backtracking algorithms for complex networks. Privacy and Data Synthesis : Theoretical frameworks for differentially private synthetic data generation. Tensor Completion : Efficient algorithms for recovering low-rank tensors from sparse observations. Publications Trends His recent research (2024–2025) emphasizes spectral analysis of random structures, optimization in non-convex settings, and privacy-preserving machine learning. Key themes include the interplay between sparsity, spectral theory, and algorithmic robustness in high-dimensional regimes.

• Machine Learning
• Data Visualization
• Computer Vision
• +4 more

Dr. Patrick Li is a Postdoctoral Research Fellow at the Biological Data Science Institute within the Australian National University's Research School of Finance, Actuarial Studies & Statistics. He holds a PhD in Statistics (Monash University, 2025), an Honours degree in Econometrics (Monash University, 2021), and a Bachelor of Commerce in Business Analytics. His research bridges machine learning, data visualization, and political science, with a focus on automating data plot analysis and understanding human interpretation of graphics. His recent work analyzes judicial behavior, parliamentary dynamics, and legislative complexity, as evidenced by his publications on High Court docket management, Australian MPs' connections, and cross-national party ideology. While no awards are explicitly mentioned, his interdisciplinary approach spans law, political systems, and behavioral science. He contributes to the Biological Data Science Institute and has engaged in policy-related studies, including gambling harm assessments. Patrick’s academic trajectory reflects a blend of statistical rigor and applied political analysis, with publications addressing both technical and societal challenges. His work integrates computational methods into legal and policy contexts, offering novel insights into governance and judicial processes.

Christopher L. BarrettView profile

Professor

• Biocomplexity
• Computational Science
• Dynamical Networks
• +7 more

Christopher L. Barrett is the Executive Director of the Biocomplexity Institute and Distinguished Professor of Computer Science at the University of Virginia. He holds a joint appointment in the School of Engineering and Applied Science. His interdisciplinary work spans computational science, dynamical networks, and AI-driven modeling. He leads large-scale research initiatives for federal agencies and has advised organizations like the Department of Defense and the European Commission. Education: Ph.D. in Bioinformation Systems (Caltech, 1985), Post-Ph.D. in Aerospace Experimental Psychology (U.S. Navy, 1986) Awards: 2021 Virginia Academy of Science Award, Jubilee Professorship (Chalmers University, 2012–2013), Distinguished International Professor (Royal Institute of Technology, 1997–1998) Research focuses on multi-scale systems, including RNA evolution, pandemic modeling, and sociotechnical infrastructure resilience. His work bridges computational methods with real-world problems like disaster preparedness and vaccine distribution strategies. He has published over 100 articles and holds seven patents. Recent publications emphasize agent-based frameworks for migration modeling, genomic surveillance, and HPC-driven epidemic analysis. His interdisciplinary approach unifies mathematical, biological, and social science methods to address complex global challenges.

Joshua A. GrochowView profile

Associate Professor

• Theoretical Computer Science
• Computational Complexity
• Algebraic Complexity
• +16 more

Joshua A. Grochow is an Associate Professor in the Department of Computer Science and, by courtesy, in the Department of Mathematics at the University of Colorado at Boulder. He is a member of the CS Theory Group and the Complex Systems Group. Previously, he was an Omidyar Fellow at the Santa Fe Institute and a postdoctoral researcher at the University of Toronto. He completed his Ph.D. in Computer Science at the University of Chicago under Lance Fortnow and Ketan Mulmuley. His research bridges theoretical computer science and mathematics, focusing on algebraic complexity, group and tensor isomorphism, proof systems, and the theory of complex systems. His work explores deep connections between algebra, computation, and complexity, with applications in quantum information, ecology, and thermodynamics. He has developed new frameworks for matrix multiplication using infinite groups and advanced the theory of higher-order interactions in complex networks. His recent publications span top venues such as ITCS, CCC, FCT, and SIAM Journal on Computing, with notable results on tensor isomorphism completeness, parallel group isomorphism algorithms, and algebraic proof complexity. He has mentored several Ph.D. students to completion, including Michael Levet, Maya Ornstein, and Tzu-Chi Yen. Best Paper Award at FCT 2023 Outstanding Faculty Mentor Award, CU Boulder (2021–2022) Dean’s Faculty Fellowship (2021) He actively collaborates across disciplines and is involved in educational innovation, including standards-based grading in large algorithms courses. His recent work emphasizes the role of symmetry, structure, and algebra in understanding computational complexity and emergent phenomena.

Yury PolyanskiyView profile

Professor

• Information Theory
• Machine Learning
• Statistics
• +3 more

Yury Polyanskiy is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), affiliated with the Laboratory for Information and Decision Systems (LIDS), the Institute for Data, Systems, and Society (IDSS), and the MIT Statistics and Data Science Center. He holds a Ph.D. from Princeton University (2010) and an M.S. from the Moscow Institute of Physics and Technology (2005). His research focuses on information theory, machine learning, statistical inference, error-correcting codes, and wireless communication. He has contributed to fundamental limits of communication systems, finite-blocklength analysis, and applications of information theory to learning and signal processing. Notable awards include the 2020 IEEE Information Theory Society James Massey Award, the 2013 NSF CAREER Award, and the 2011 IEEE Information Theory Society Paper Award. His work spans theoretical advancements and practical applications, including the development of the SPECTRE toolbox for short-packet communication. He is also co-authoring a textbook on information theory. Recent research highlights include studies on quantization techniques for machine learning (e.g., NestQuant), transformer-based empirical Bayes methods, and novel approaches to massive random access in wireless networks (e.g., unsourced multiple access). His contributions bridge information theory and modern data science, addressing challenges in high-dimensional data representation, neural network dynamics, and efficient communication architectures.

Rob H. BisselingView profile

Professor

• Parallel Algorithms
• Sparse Matrix/Tensor Computations
• Graph and Hypergraph Algorithms
• +5 more

Rob H. Bisseling is a Full Professor in Scientific Computing at Utrecht University's Mathematical Institute and a visiting professor at ENS de Lyon's LIP laboratory (March–May 2024). He holds a BSc/MSc in Mathematics (cum laude) from the Catholic University of Nijmegen and a PhD in Theoretical Chemistry from the Hebrew University of Jerusalem. His research focuses on parallel algorithms, sparse matrix/tensor computations, and hypergraph partitioning, with applications in high-performance computing and numerical methods. He has authored a seminal textbook on parallel scientific computing and contributes to pedagogical resources like video lectures. During his visit to LIP, Bisseling collaborates with the ROMA team under Bora Uçar to advance parallel algorithms for large-scale irregular applications. His work includes developing tools like PMondriaan for sparse matrix partitioning and promoting knowledge transfer through lectures on BSP programming. He engages with researchers, PhD students, and engineers at LIP, extending collaborations to Lyon's Institut Camille Jordan and LabPhys for tomographic reconstruction and statistical physics modeling. His academic career includes roles at Royal Dutch Shell and as Utrecht University's Director of Education (2012–2015). He advocates interdisciplinary approaches, bridging computational methods with applied sciences and engineering challenges.

Monique LaurentView profile

Professor

• Discrete Mathematics
• Optimization
• Polynomial Optimization
• +5 more

Monique Laurent is a Tilburg University professor and senior researcher at CWI (Centrum Wiskunde & Informatica), focusing on discrete mathematics and optimization . Her work bridges algebra, geometry, and computer science to solve complex combinatorial and polynomial optimization problems. Part-time full professor at Tilburg University (since 2009) Group leader of Networks and Optimization at CWI (2005-2016) Current member of CWI Management Team Research Focus: Semidefinite programming hierarchies, noncommutative polynomial optimization, quantum information theory, and matrix factorization. Her recent work explores applications in quantum entanglement , graph parameters , and combinatorial data analysis . Key Publications: 15+ articles from 2017-2024 address topics like copositive matrices , sum-of-squares convergence , and hypergraph optimization . Collaborations span institutions in the Netherlands, France, Germany, and the U.S. Awards: 2023 Khachiyan Prize SIAM Fellow (2017) KNAW member (2018) EUROPT Fellow (2021) Grants & Projects: Leads EU-funded initiatives TENORS (2024) and POEMA (2019), with prior NWO and Marie Curie grants. Organizes international workshops on polynomial optimization and quantum information.