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Raissa M. D'SouzaView profile

Professor

• Complex Networks
• Percolation Theory
• Cascades
• +3 more

Raissa M. D'Souza is a Professor and Associate Dean of Research in the College of Engineering at the University of California, Davis. She holds joint appointments in the Department of Computer Science, Department of Mechanical and Aerospace Engineering, Graduate Group in Applied Mathematics, and Graduate Group in Physics. Her research focuses on complex networks, percolation theory, cascades, and interdependent systems, with applications to social, biological, and technological networks. She leads the Networks and Control research group and is a Founding Lead Editor of Physical Review Research . Her work spans interdisciplinary collaborations, including with the Santa Fe Institute and the Complexity Sciences Hub Vienna. Key awards include Fellowships from AAAS (2024), APS (2016), and the Network Science Society (2019), as well as the Euler Award for her contributions to network science. Dr. D'Souza has advised over 10 PhD students and has secured grants from agencies including the NSF. Her lab explores topics like explosive percolation, synchronization in oscillator networks, and network controllability. Notable recent work includes studies on exotic synchronization patterns and strategies for mitigating cascading failures in interdependent systems.

Konstantin (Kostia) M. ZuevView profile

Teaching Professor

• Network Science
• Applied Probability
• Computational Statistics
• +4 more

Dr. Konstantin (Kostia) M. Zuev serves as Teaching Professor in the Computing + Mathematical Sciences Department at California Institute of Technology , where he has made significant contributions to network science and computational statistics since 2016. His dual PhDs in Mathematics (Moscow State University, 2008) and Civil Engineering (HKUST, 2009) underpin his interdisciplinary research spanning differential geometry, stochastic simulation, and network dynamics. Education PhD in Mathematics, Lomonosov Moscow State University (2008) PhD in Civil Engineering, Hong Kong University of Science & Technology (2009) His research focuses on network science , particularly course-prerequisite networks and complex financial systems , with recent work extending to network navigability in cosmological models and rare event simulation. Over his career, he has developed innovative Bayesian inference methods and geometric preferential attachment theories while maintaining active collaborations across mathematics, physics, and biomedical domains. Recent publications highlight network analysis in education ( 2023 ), hyperbolic graph theory ( 2024 ), and pandemic-informed cancer mortality studies ( 2023 ). His 15 most recent articles demonstrate methodological innovations across disciplines including statistics, physics, finance, and cosmology. Scientific recognition includes Humboldt Research Fellowship (2021) Carver Mead Seed Fund Grant (2023) ASCIT Teaching Award (2018, 2023) Northrop Grumman Teaching Excellence Prize (2019) As Graduate Option Representative for Information and Data Sciences at Caltech and faculty advisor for multiple student organizations including the Caltech Karate Club and Caltech Chess Club , he actively bridges academic rigor with community engagement through outreach initiatives like the virtual math education channel and university math circles for K-12 students.

• Statistical Network Analysis
• Machine Learning
• Data Privacy
• +7 more

Tianxi Li is an Assistant Professor in the Department of Statistics at the University of Minnesota, Twin Cities, within the College of Science and Engineering. Their research integrates statistical methodology with applications in network science, data privacy, and biomedical data analysis. Their research interests lie at the intersection of statistics and network science, focusing on statistical modeling of complex networks , data privacy , network security , and biomedical applications such as neuroimaging and genomics. They develop adaptive and scalable methods for network estimation, community detection, and differential correlation analysis. The recent publications demonstrate a consistent focus on advancing statistical tools for network-structured data, with increasing applications in neuroscience and cancer genomics. The work spans theoretical development (e.g., network growth models) and practical applications (e.g., glioblastoma gene modules), reflecting a balance between methodology and real-world impact. Tianxi Li leads an active research program funded by the National Science Foundation, indicating recognition and support for their innovative work. Principal Investigator, Statistical tools for network security protection: from data privacy to threat detection , NSF (2024–2025) They advise graduate students in statistics and data science, though specific advisees are not listed. Their collaborative network includes researchers in biostatistics, computer science, and machine learning, as evidenced by co-authorships and interdisciplinary projects. Li's work contributes to the UN Sustainable Development Goals, particularly through advancements in data-driven solutions for secure and ethical data analysis.

Christina Lee YuView profile

Assistant Professor

• Algorithm Design and Analysis
• High Dimensional Statistics
• Inference over Networks
• +5 more

Christina Lee Yu is an Assistant Professor at Cornell University in the School of Operations Research and Information Engineering (ORIE). She holds a PhD and MS in Electrical Engineering and Computer Science from MIT (2017, 2013) and a BS in Computer Science from Caltech (2011). Her research focuses on algorithm design, high-dimensional statistics, causal inference in networks, and reinforcement learning. She is also an Amazon Scholar and has received prestigious awards, including the NSF CAREER Award and Intel Rising Stars Award. Her work is supported by grants from the NSF and Air Force Office of Scientific Research. Education: PhD in EECS, MIT (2017) MS in EECS, MIT (2013) BS in Computer Science, Caltech (2011) Research Interests: Algorithm design and analysis Inference over networks and causal inference Sequential decision making under uncertainty Online learning and reinforcement learning High-dimensional statistics Awards and Honors: NSF CAREER Award (2024) ACM SIGMETRICS Rising Stars Award (2024) Intel® Rising Stars Award (2021) JPMorgan Faculty Research Award (2021) Simons Institute Research Fellow (2020) INFORMS Dantzig Dissertation Award Honorable Mention (2018) Grants and Funding: National Science Foundation (NSF) CAREER Grant Air Force Office of Scientific Research Grant Advising: PhD Students: Sean Sinclair, Tyler Sam, Xumei Xi Collaborators: Mayleen Cortez, Matthew Eichhorn Labs and Collaborations: Member of ORIE, Statistics, CAM, and CS graduate fields at Cornell Amazon Visiting Academic in Fulfillment Optimization (2025)

• Stochastic Processes
• Random Environments
• Percolation Theory
• +2 more

Noam Berger Steiger is a Professor of Stochastic Processes at the Technical University of Munich (TUM), within the School of Computation, Information and Technology and the Department of Mathematics. His office is located at Parkring 11, Garching bei München, and he can be contacted at noam.berger@tum.de. His research focuses on stochastic processes in random environments, percolation theory, and random walks. Key contributions include asymptotic analysis of preferential attachment graphs, quenched invariance principles for non-elliptic random walks, and slowdown phenomena in ballistic random motion. His work bridges theoretical probability with applications in complex systems. Analysis of his 2012-2014 publications reveals consistent focus on random walk dynamics in disordered media, with significant results on ballisticity conditions, trail detection in random scenery, and distributional limits. His research employs advanced probabilistic techniques published in top-tier journals including Annals of Probability and Probability Theory and Related Fields . Professor Berger has supervised 11 theses: 5 bachelor's theses at TUM covering Brownian motion properties and investment strategies for risk-averse investors, and 6 master's theses (3 at TUM, 3 at Hebrew University) on topics including return times for random walks, mass transport principles, and spin-glass percolation. His current teaching includes Markov Chains, Probability on Graphs, and Brownian Motion seminars. He is an active member of TUM's Probability Theory research group, which participates in the TUM-ICL Mathematical Sciences Hub and Exzellenzcluster MCQST. The group collaborates on quantum science initiatives while maintaining strong foundations in classical probability theory and stochastic analysis.

• Branching processes
• Random trees
• Reinforcement processes
• +9 more

Cécile Mailler is a Reader in Probability at the University of Bath, where she is a member of the probability group Prob-L@B. She has held significant research positions including an EPSRC postdoctoral fellowship (2018-2021) titled "Random trees: analysis and applications" and previously worked as a postdoc at Prob-L@B (2013-2016) as part of Peter Mörters' EPSRC project "Emergence of Condensation in Stochastic Networks". She earned her PhD under the supervision of Brigitte Chauvin and Danièle Gardy at the Laboratoire de Mathématiques de Versailles. Her research focuses on probability theory with emphasis on branching processes, random trees, reinforcement mechanisms, Pólya urns, stochastic approximation, random networks, and statistical physics. She has made significant contributions to understanding preferential attachment models, zero-range processes, and random Boolean trees. Her work bridges theoretical probability with applications in statistical physics and combinatorics. Analysis of her recent publications shows a strong focus on random tree structures, branching processes, and reinforcement learning algorithms, with applications spanning from network theory to statistical mechanics. Her research demonstrates sophisticated mathematical techniques applied to complex stochastic systems, particularly those with reinforcement mechanisms and memory effects. Associate Editor of the Applied Probability Trust (since October 2020) Associate Editor of Stochastic Processes and Their Applications (since March 2022) Author of a general introduction to Pólya urns for the LMS Newsletter (November 2020) Co-organizer of the "Random Walks: Applications and Interactions" conference at CIRM (January 2026) She actively supervises PhD students working on topics including the multi-city ants process, Pólya urns with growing initial composition, large deviations for the Monkey walk, and competing growth processes. She has secured research funding through EPSRC fellowships and has been involved in multiple collaborative projects with prominent researchers in probability theory. Mailler regularly teaches mini-courses on advanced probability topics at international summer schools and workshops, demonstrating her commitment to knowledge dissemination in the field.

Ulrik BrandesView profile

Professor

• Social Network Analysis
• Graph Theory
• Network Science
• +3 more

Ulrik Brandes serves as Full Professor and Head of the Department of Humanities, Social and Political Sciences at ETH Zurich, holding the Professorship for Social Networks. He actively teaches courses including Network Analysis and Applied Network Science: Sports Networks for Fall semester 2025, with office location at WEP J 14, Weinbergstr. 109, Zurich. His research spans Social Network Analysis, Graph Theory, and Network Science, with significant applications in sociology, sports analytics (particularly soccer and Australian Football League), and archaeological networks. As a longstanding member of the International Network for Social Network Analysis (since 2001) and the Academy of Sociology (since 2017), he bridges theoretical graph algorithms with practical interdisciplinary applications, recently expanding into sports analytics through the Football Scouting Association (2023). Analysis of his recent publications reveals strong thematic continuity in network centrality, temporal dynamics, and robustness, with increasing application diversity across sports analytics, archaeological trade networks, and decentralized social media platforms. His work consistently emphasizes efficient computational methods for real-world network problems. Honors: Simmel Award (2024) Prof. Brandes maintains active research leadership through departmental oversight and course development, though specific grant details and student advisement records are not publicly documented in available sources. His departmental role indicates substantial administrative responsibilities alongside research and teaching commitments.

Giulio CiminiView profile

Associate Professor

• Statistical Physics
• Complex Networks
• Economic Networks
• +12 more

Giulio Cimini is Associate Professor of Theoretical Physics in the Department of Physics at the University of Rome Tor Vergata and a Research Associate at the 'Enrico Fermi' Research Center. He is a statistical physicist with a strong interdisciplinary focus on complex networks and their applications in socio-economic systems. His research interests include: Statistical Physics of Complex Networks Reconstruction and Validation of Economic Networks Social Network Interactions and Financial Markets Systemic Risk and Financial Contagion Scientific Success, Fitness, and Complexity Adaptive Social Recommendation Codon Usage Bias and Protein Interaction Networks His recent publications reveal a strong trend in applying statistical physics to real-world networks, particularly in finance and social systems. Key themes include the modeling of systemic risk in supply chains and financial networks, the dynamics of collective action on platforms like Reddit (e.g., the GameStop short squeeze), and the development of network reconstruction methods using maximum entropy and optimal transport frameworks. His work often combines empirical analysis with theoretical modeling. Scientific awards and recognitions include: Associate Editor, Frontiers in Physics – Interdisciplinary Physics Board Member, Network Science Society Member, Council of the Complex Systems Society Steering Committee, CCS/Italy He has advised or collaborated with numerous researchers, particularly in projects related to economic networks and complex systems. His work has been supported by Italian national grants such as PRIN and PNRR. He leads or co-leads research projects including RENet and C2T. His research is conducted within interdisciplinary teams involving physicists, economists, and computer scientists, often in collaboration with institutions like ISC-CNR, IMT Lucca, and the Network Science community.

• Applied Probability
• Operations Research
• Computational Biology
• +4 more

Hiroshi Toyoizumi is a Professor at Waseda University's Faculty of Commerce and Graduate School of Accountancy, with a Ph.D. in Mathematical Physics from Waseda University. His work bridges Applied Probability , Operations Research , and Computational Biology , focusing on stochastic modeling across biological and network systems. Research Interests Stochastic Models in Biology : Analyzing social queues in hover wasps and meiotic DSB regulation using quasi-birth-death processes and survival analysis. Network Security : Modeling computer virus spread on scale-free networks and proposing active defense strategies. Queueing Theory : Developing analytical frameworks for on-demand streaming, quantum merging, and blockchain-based systems. Article Trends Recent work (2020-2024) explores nonlocal diffusion , quantum queueing , and swarm behavior in financial markets . Mid-2010s studies focus on cooperative breeding and DNA recombination dynamics . Early papers (pre-2010) examine computer virus ecology , secure group communication , and deterministic queue analysis . Scientific Awards Biotechno 2013 Best Paper Award IEICE Switching Workshop SSE Research Award (1999) IEICE Young Research Engineer Award (1997) Teaching Activities Graduate-level courses in Operations Research and Financial Engineering . Undergraduate and graduate seminars in Applied Mathematics and Probabilistic Models .

Aric OpdahlView profile

Professor

• Surface Chemistry
• Analytical Chemistry
• DNA Hybridization
• +3 more

Aric Opdahl is a Professor in the Department of Chemistry & Biochemistry at the University of Wisconsin-La Crosse. He holds a B.S. in Chemistry and Anthropology from the University of Wisconsin-Madison and a Ph.D. in Physical Chemistry from the University of California-Berkeley. His research focuses on surface and analytical chemistry, particularly exploring DNA immobilization strategies and their thermal stability on surfaces. Prior to his academic role, he served as a National Research Council Postdoctoral Fellow at the National Institute of Standards and Technology (NIST) from 2003 to 2005. His recent work emphasizes the impact of DNA-surface interactions on hybridization stability and efficiency. Notable projects include studies on gold surface attachment strategies and substrate-preferential DNA probe immobilization. While no formal grants or awards are listed, his publications frequently involve undergraduate researchers, as indicated by asterisks in co-author listings. Opdahl’s research group bridges analytical and surface chemistry, contributing to advancements in biomolecular analysis and material science applications. No specific lab affiliations or team collaborations beyond co-authorships are detailed in the provided text.

• Algorithms for Big Data
• Parallel Computing
• External-Memory Algorithms
• +5 more

Ulrich Meyer is a Professor at the Institute for Computer Science at Goethe University Frankfurt. He serves as a prominent researcher in algorithms for big data, with extensive contributions to parallel and external-memory graph algorithms. His work spans theoretical foundations and practical implementations for processing large-scale data sets. Spokesperson of the DFG priority program (SPP 1736) on Algorithms for Big Data in Germany SEA23 Symposium on Experimental Algorithms, Steering Committee Chair ALENEX23 Algorithm Engineering and Experiments, Program Committee Member Professor Meyer's research interests focus on the theoretical and experimental aspects of processing large data sets on advanced computational models. His work particularly emphasizes parallel and external-memory graph algorithms, with recent focus on efficient large-scale network generation according to various stochastic models. His research has produced significant contributions including the parallel Delta-Stepping algorithm (which received the ESA Test of Time Award in 2019) and the first BFS approach with sublinear I/O. He has also explored more specialized topics like energy-efficient sorting (with records in the JouleSort competition 2009/10 and the Germany Land of Ideas Award) and fragile computing (which earned him a best-paper award at ESA 2019). His recent publications demonstrate a strong focus on graph algorithms, network generation, and parallel computing techniques. The research trends show consistent advancement in scalable algorithms for massive graphs, with particular emphasis on efficient sampling methods, shortcutting techniques, and communication-free distributed approaches. His work bridges theoretical computer science with practical engineering considerations for real-world big data applications. ESA Test of Time Award 2019 for Parallel Delta-Stepping algorithm Records in the JouleSort competition 2009/10 Germany Land of Ideas Award Best-paper award at ESA 2019 for fragile computing research Professor Meyer has made substantial contributions to the academic community through his leadership in the DFG priority program on Algorithms for Big Data, which has fostered significant research collaborations across Germany. His extensive publication record in top venues demonstrates sustained research productivity and impact in the algorithms community. While specific grant details aren't provided in the text, his role as spokesperson for a major DFG priority program indicates substantial research funding and leadership responsibilities. His work appears to be conducted within collaborative research environments focused on algorithm engineering and experimental evaluation. His research appears to be conducted within the Institute for Computer Science at Goethe University Frankfurt, likely involving collaborations with other researchers in the Algorithms for Big Data priority program. The extensive list of co-authored publications suggests active participation in research teams focused on parallel algorithms, graph processing, and network generation.

Fariba KarimiView profile

Professor

• Algorithmic Fairness
• Computational Social Science
• Network Science
• +7 more

Fariba Karimi is a Professor of Social Data Science at Graz University of Technology and leads the Algorithmic Fairness research group at the Complexity Science Hub (CSH) in Vienna. Her work sits at the intersection of computational social science, network science, and AI ethics, with a strong focus on fairness, inequality, and bias in algorithmic systems. She holds a PhD in Physics and Computational Science from Umeå University, Sweden (2015), and was a Postdoctoral Researcher at GESIS – Leibniz Institute for Social Sciences, Germany. Her research combines large-scale data analysis, agent-based modeling, and network science to study how social structures and algorithms interact to shape disparities, particularly for underrepresented groups. Computational Social Science Algorithmic Fairness and Bias Network Science and Homophily Digital Humanism Urban Inequality and Implicit Bias Gender and Age Equity in Academia Her recent publications reveal a consistent focus on understanding and mitigating algorithmic and structural inequities. Themes include the visibility of minorities in networks, the impact of segregation on health and cognition, gender citation gaps in physics, and the development of fair ranking and recommendation systems. She employs interdisciplinary methods to analyze both digital and real-world social systems. Fariba Karimi has received several prestigious recognitions, including: Young Scientist Award from the German Physical Society (2023) ERC Starting Grant (2024) Nomination for the Hedy Lamarr Award (2021) She leads the 'Humanized Algorithms' project at CSH, funded by the ERC, and co-leads an EU Horizon project (MAMMOth) on multi-criteria fairness in AI. She advises PhD researchers such as Lisette Espín-Noboa and collaborates with institutions like the University of Mannheim and the Centre for Social Sciences. Her work contributes to both academic knowledge and practical solutions for fairer AI systems and more equitable societies.

• Actuarial Science
• Mathematics Education
• Bibliometrics
• +4 more

Larry Smolinsky is the Roy Paul Daniels Professor of Mathematics at Louisiana State University (LSU), a position he has held since 2013. He has been a Professor at LSU since 2000, serving as Chair of the Department of Mathematics from 2004 to 2010. His academic journey includes roles as Associate Professor (1992–2000), Assistant Professor (1987–1992), and Josiah Willard Gibbs Instructor at Yale University (1985–1987). Smolinsky's research focuses on actuarial science , bibliometrics/informetrics , and mathematics education . He co-leads LSU's Actuarial Science program, providing resources like capstone credit guidance and exam preparation advice. His work in informetrics includes analyzing citation patterns, altmetric indicators, and collaboration dynamics. He also explores STEM education methodologies, such as online vs. handwritten homework effectiveness in calculus courses. His editorial roles include serving on the Journal of Informetrics board since 2022. He received the H.M. 'Hub' Cotton Award for Faculty Excellence (2012) for his contributions. Current teaching includes Long-Term Actuarial Mathematics courses (Math 4045/4046). Smolinsky's research bridges theoretical mathematics and applied fields like actuarial modeling, legal testimony, and educational innovation. His recent work critiques citation metrics and evaluates interdisciplinary programs such as Mathematics and Finance dual disciplines.

Fady AlajajiView profile

Professor

• Information Theory
• Communications
• Coding Theory
• +5 more

Fady Alajaji is a Professor of Mathematics and Engineering at Queen's University, with a cross-appointment in the Department of Electrical and Computer Engineering. He holds a B.E. from the American University of Beirut, and M.Sc. and Ph.D. from the University of Maryland, College Park. His research focuses on information theory, coding for communication networks, probability models (e.g., Polya urns, contagion processes), and applications of information theory to machine learning (e.g., generative adversarial networks, data privacy). He has served as Associate Editor for the IEEE Transactions on Information Theory and has received awards for research and teaching. His work spans theoretical foundations (e.g., Shannon limits) and practical coding techniques for wireless systems. Education: B.E. (1988), M.Sc. (1990), Ph.D. (1994) in Electrical Engineering from the University of Maryland. Roles: Professor of Mathematics and Engineering, Cross-appointment in Electrical and Computer Engineering. Research Interests: Information theory, coding for communication networks, stochastic processes (network epidemics, Polya urn models), machine learning applications (information bottleneck, GANs), and data privacy. Recent work includes optimal signaling schemes for sensor networks, privacy-aware estimation, and curing models for contagion networks. Publications: Over 100 journal/conference papers, including foundational work on joint source-channel coding, hybrid digital-analog coding, and theoretical bounds for communication systems. Recent trends focus on information-theoretic machine learning and network science. Awards: Premier's Research Excellence Award (2001), Golden Apple Teaching Award (2015). Grants/Advising: Supervised postdoctoral fellow Jian-Jia Weng. Active in conference organization and editorial roles. Labs/Teams: Member of the Mathematics and Engineering Communications and Information Theory Group at Queen's University.

Manuel PenschuckView profile

Research Fellow

• Algorithm Engineering
• Random Graphs
• Parallel and Distributed Computing
• +3 more

Manuel Penschuck is a Research Fellow at the Institute of Computer Science , Goethe University Frankfurt, Germany. His research focuses on algorithm engineering, graph theory, and scalable network generation, with emphasis on parallel computing, I/O-efficient algorithms, and random graph models. He actively contributes to conferences like ESA, SEA, and IPDPS, and has co-authored publications in top venues including LIPIcs , IEEE Transactions , and SIAM . His work includes engineering algorithms for non-linear preferential attachment , parallel shuffling , and hyperbolic graph generation . He has co-organized program committees for ESA, EuroPar, and SEA, and his collaborations span institutions such as MPI-INF, TU Darmstadt, and Australian National University. Recent publications highlight advances in uniform graph sampling, geometric network models, and distributed systems. His research integrates theoretical rigor with practical implementation, addressing challenges in big data and high-performance computing. He is a key contributor to the Networkit toolkit for large-scale network analysis.