جستجوی استاد راهنمای دانشگاهی

Göran Kauermannمشاهده پروفایل

استاد

• Nonparametric Models
• Semi- and nonparametric analysis
• Generalized Linear Models
• +۲ مورد دیگر

Prof. Dr. Göran Kauermann is a Full Professor of Statistics at the Ludwig-Maximilians-University Munich , holding the Chair of Applied Statistics in Social Sciences, Economics and Business . His research spans nonparametric models, generalized linear models, and network data analysis, with applications in economics, epidemiology, and data science. Education: Diplom in Economic Mathematics (1991, TU Berlin), PhD in Statistics (1994), Habilitation (Venia Legendi) in Statistics (2000) Kauermann’s research interests focus on penalized regression , network analysis , and statistical modeling in economics, social sciences, and public health. Recent work explores label uncertainty in machine learning , spatio-temporal conflict diffusion , and dynamic network models for economic and social data. Scientific trends in his publications include penalized splines for nonlinear modeling, network flow estimation in social and economic contexts, and label variation analysis in machine learning. His collaborations span climate zone classification , Covid-19 mortality modeling , and smart city parking analytics . Scientific Awards: Bruce Russett Award (2020) for political network research Leadership Roles: He served as Dean of the Faculty of Mathematics, Informatics and Statistics (2019–2021), Speaker of the Elite Master Program in Data Science (2016–2026), and Chair of the German Statistical Society (2005–2013). He also held editorial roles in journals like AStA Advances in Statistical Analysis and Statistical Modelling .

Georgi S. Medvedevمشاهده پروفایل

استاد

• Dynamical Systems
• Stochastic Analysis
• Mathematical Neuroscience
• +۳ مورد دیگر

Georgi S. Medvedev is a Professor in the Department of Mathematics at Drexel University. He earned his PhD from Boston University (1999) and was a Veblen Research Instructor at Princeton University and the Institute for Advanced Study before joining Drexel in 2002. His research investigates dynamical systems, stochastic analysis, and mathematical neuroscience. Research develops mathematical frameworks for understanding complex network dynamics. Primary areas include synchronization in coupled oscillator systems, metastability in stochastic networks, and continuum limits of large-scale dynamical systems. Applications span neuroscience, statistical physics, and engineering. Recent publications focus on the Kuramoto model, exploring bifurcations in networked oscillators, stability of twisted states, and continuum approximations for sparse graphs. The work bridges discrete network models with continuum descriptions using graph limit theory. He has developed interdisciplinary courses including Mathematical Neuroscience and Dynamical Networks. His research is supported by multiple NSF grants focusing on network metastability, mean-field analysis, and synchronization phenomena. Service includes editorial roles for Discrete and Continuous Dynamical Systems and Networks and Heterogeneous Media. He mentors undergraduate researchers in projects investigating metastability in oscillator networks.

Francesca Pariseمشاهده پروفایل

استادیار

• Multi-Agent Systems
• Network Theory
• Control Systems
• +۶ مورد دیگر

Francesca Parise is an Assistant Professor in the School of Electrical and Computer Engineering at Cornell University. Her research focuses on multi-agent systems, particularly analyzing large networks of autonomous decision-makers in transportation, finance, and social systems, employing tools from control theory, game theory, and optimization. She holds a PhD from ETH Zurich and was a Postdoctoral Research Fellow at MIT's Laboratory for Information and Decision Systems under Prof. Asuman Ozdaglar. Her work bridges theoretical foundations with practical applications in networked systems and interventions. Education: Bachelor's and Master's from University of Padua, Italy PhD in Control Engineering from ETH Zurich (2016) Her research interests span game-theoretic models for network analysis, learning dynamics in time-varying systems, and optimal intervention strategies. Key themes include synchronization in power grids, epidemic control via testing, and dynamic resource allocation. Recent work emphasizes graphon-based frameworks for large-scale network games and computational methods for equilibrium analysis. She explores both theoretical guarantees and real-world applications in transportation and energy systems. Publications highlight contributions to networked game theory, stochastic systems, and control applications, with a focus on scalable solutions for complex systems. Her work often intersects with interdisciplinary challenges in systems biology and socio-technical networks. She currently holds an office at Frank H. T. Rhodes Hall, Cornell University.

David S. Choiمشاهده پروفایل

دانشیار

• Statistics
• Machine Learning
• Network Analysis
• +۵ مورد دیگر

David S. Choi is an Associate Professor at Heinz College, Carnegie Mellon University, which houses the Schools of Public Policy and Information Systems. He maintains a courtesy appointment in the Department of Statistics at the same institution. His academic credentials include a Ph.D. in Electrical Engineering from Stanford University under Benjamin Van Roy. Professional experience encompasses roles as a technical staff member at MIT Lincoln Laboratory, postdoctoral research at Harvard University's School of Engineering, and a visiting postdoc at UC Berkeley's Department of Statistics. Choi's research centers on statistical and machine learning methodologies for network-structured data. His work pioneers community detection techniques, latent variable network modeling, unsupervised learning frameworks, and causal inference methods that account for interference in social networks. These innovations address fundamental challenges in analyzing interconnected systems across public policy, healthcare, and social sciences. Publication analysis reveals consistent contributions to causal inference in networked experiments, particularly in characterizing interference and spillover effects. His scholarship spans network clustering algorithms, biological network modeling, and policy evaluation methodologies, with impactful work published in Journal of the American Statistical Association, PNAS, and Annals of Statistics. No scientific awards were documented in the source material. The available information does not reference doctoral students supervised or research grants managed by Professor Choi. Specific research laboratories or collaborative teams were not mentioned in the provided documentation.

Cameron Freerمشاهده پروفایل

استاد پژوهش

• Probabilistic computing
• Foundations of probabilistic programming
• Random structures
• +۵ مورد دیگر

Cameron Freer is a Research Scientist in the MIT Probabilistic Computing Project , with prior roles including Instructor in Pure Mathematics at MIT, Postdoctoral Fellow at CSAIL, and Project Associate Professor at Keio University. His work bridges probabilistic computing, logic, and theoretical computer science. Education PhD in Mathematics, Harvard University, 2008 (Thesis: Models with High Scott Rank ) Research Interests Freer's research explores the deep interplay between randomness and computation , focusing on: Foundations of probabilistic programming languages and systems Efficient samplers for discrete and continuous distributions Mathematics of random structures like graphons and exchangeable processes Computability in measure theory and probabilistic inference Publications Overview His recent work (2020–2024) advances probabilistic programming systems (e.g., GenSQL), theoretical frameworks for random graphs via Markov categories, and computable approaches to PAC learning. Earlier contributions include exact sampling algorithms, computable exchangeability, and algorithmic barriers in conditional probability. Academic Service Steering Committee Member, LAFI (formerly PPS) workshop series (2017–2025) Program Committee Chair/Co-chair, PPS 2017–2018 Session Chair, POPL 2017 (PPS track) Industry & Visiting Roles Chief Scientist, Remine (2017–2018) Research Scientist, Gamalon Labs (2013–2016) Lyric Labs Visiting Fellow, Analog Devices (2013–2014) Project Associate Professor, Keio University (2021–2024) Labs & Collaborations Freer collaborates extensively with the MIT Probabilistic Computing Project, Harvard Logic Group, and international partners in Oxford, CMU, and Keio University. His work integrates theoretical insights with practical systems in AI and probabilistic inference.

• Stochastics
• Probability theory
• Stochastic processes
• +۷ مورد دیگر

Lasse Leskelä is an Associate Professor at the Department of Mathematics and Systems Analysis at Aalto University. His research focuses on stochastics, particularly statistical network models, random graphs, and stochastic processes acting on networks. He aims to derive mathematical laws for predicting macroscopic behavior in large random systems and improving statistical learning algorithms for high-dimensional data analysis. Probability theory Stochastic processes Random graphs Statistical network models Queueing Theory Applied probability Leskelä's work spans theoretical and applied domains, with applications in information networks, social networks, biological systems, and financial modeling. His research group actively explores models like random intersection graphs, stochastic block models, and graphons to understand complex systems. Leskelä's recent publications in journals such as the IEEE Transactions on Network Science and Engineering and PLoS Computational Biology highlight his contributions to community detection, hypergraph connectivity, and epidemic modeling. His work bridges mathematical theory with practical applications in data science and network analysis. Varma-Sampo MSc thesis prize , Finnish Mathematical Society McKinsey Prize for the best MSc degree completed in 1999 at the Helsinki University of Technology Leskelä has collaborated with leading researchers across disciplines. His research includes grants from Aalto University and international institutions, focusing on advancing stochastic modeling and data analysis techniques.

• Probability Theory
• Random Graphs
• Graph Limits
• +۳ مورد دیگر

Backhausz Ágnes is a Assistant Professor at Eötvös Loránd University's Faculty of Science , specifically in the Department of Probability Theory and Statistics . She also holds a part-time Researcher position at the Alfréd Rényi Institute of Mathematics . Her academic journey includes habilitation and a PhD in Mathematics, focusing on random graph models and their asymptotic properties. Research Group: Struktúrák limeszei (since 2013, part-time since 2015) Grants: ERC Grant on 'Limits of Discrete Structures' (2014–2019) Ágnes specializes in Probability Theory and Random Graphs , with emphasis on graph limits , factor of i.i.d. processes , and spectral theory . Recent publications analyze epidemic spread on multilayer networks, entropy inequalities, and action convergence in graph operators. Her work bridges theoretical mathematics with applications in network science and stochastic processes. Notable awards include the Grünwald Géza Memorial Medal (2014) from the Bolyai János Matematikai Társulat. She actively contributes to academic service as a Supervisor and Training Lead for the Beyond The Edge Marie Curie Doctoral Network (2024–2027) and serves on program committees for conferences like Eurocomb and the European Girls' Mathematical Olympiad . Her teaching portfolio spans Probability Theory, Stochastic Processes, and Mathematical Statistics at both undergraduate and graduate levels.

Backhausz Ágnesمشاهده پروفایل

استادیار

• Probability Theory
• Random Graphs
• Matrices and their Limits
• +۳ مورد دیگر

Ágnes Backhausz is a habilitated assistant professor at Eötvös Loránd University's Faculty of Science, Institute of Mathematics, Department of Probability Theory and Statistics, with a part-time research position at the Alfréd Rényi Institute of Mathematics. Her academic career spans theoretical probability and practical applications in network science. Dr. Backhausz's research focuses on probability theory with specializations in random graphs, matrices and their limits, spectral theory of random graphs, and factor of iid processes. Her work bridges pure mathematics with real-world applications, particularly in epidemiological modeling on complex networks. She has made significant contributions to understanding the eigenvectors of random regular graphs and the behavior of processes on infinite trees. Her recent publications demonstrate a growing emphasis on applying probabilistic methods to epidemic modeling on multilayer networks with overlapping communities. This research has important implications for public health policy and disease control strategies. The trend in her work shows increasing interdisciplinary collaboration, combining mathematical rigor with practical healthcare applications. Dr. Backhausz holds significant academic responsibilities including serving as Supervisor and training lead for the Beyond The Edge Marie Curie Doctoral Network (2024-2027) and as a researcher at the National Laboratory for Health Security, Hungary (2023-2026). She is an editor for Acta Mathematica Hungarica since March 2021 and has been organizing the Departmental seminar of the Department of Probability Theory and Statistics since 2010. She actively contributes to the mathematical community through program committee memberships for major conferences like Eurocomb and by organizing workshops on graph limits, groups, and stochastic processes. Her editorial work further demonstrates her standing in the mathematical research community. As an educator, Dr. Backhausz teaches mathematical statistics, probability theory, and related subjects across multiple programs at ELTE, including courses for mathematics students, earth science students, and informatics programs, showcasing the interdisciplinary nature of her expertise.

Soumik Palمشاهده پروفایل

استاد

• Probability
• Stochastic Processes
• Optimal Transport
• +۵ مورد دیگر

Soumik Pal is a Professor in the Department of Mathematics at the University of Washington, with adjunct appointments in Applied Mathematics and Statistics. His research focuses on probability theory, particularly interacting Brownian particle systems, random graphs, random matrices, and optimal transport. He leads the Kantorovich Initiative, an interdisciplinary research effort funded by PIMS and NSF, advancing applications of optimal transport in data science. NSF grants DMS-2052239 and DMS-2134012 (Scale Math of Deep Learning) NSF Infrastructure grant DMS-2133244 supporting the Kantorovich Initiative His recent work bridges optimal transport with gradient flows, Schrödinger bridges, and graphons. He advises PhD students in probability and stochastic models, with former advisees including Tobias Johnson and Andrey Sarantsev. He serves as Associate Editor for journals like Stochastic Models and Electronic Journal of Probability .

Qing Zhouمشاهده پروفایل

استاد

• Causal inference and graphical models
• High-dimensional statistics
• Monte Carlo methods
• +۵ مورد دیگر

Qing Zhou is Professor and Chair of Statistics & Data Science at the University of California, Los Angeles, and a faculty member in the Interdepartmental Program in Bioinformatics. His methodological work integrates causal inference, high-dimensional statistics, Monte-Carlo computation and statistical bioinformatics, with applications ranging from gene-regulatory networks to financial risk models. Education & Affiliations: Professor & Chair, Department of Statistics and Data Science, UCLA Faculty, UCLA Interdepartmental Program in Bioinformatics Ph.D. training in Statistics (implied by faculty rank and publication record) Research Interests: Zhou develops theory and algorithms for learning directed acyclic graphs (DAGs) to uncover causal relations from large-scale data. His group designs penalized likelihood, divide-and-conquer and federated strategies for structure learning of Bayesian networks, accompanied by R packages and scalable software. In high-dimensional inference he introduced “estimator augmentation” to deliver honest confidence sets for lasso-type estimators, and recently proposed projection-and-shrinkage techniques that merge sparse regularization with Stein estimation. Monte-Carlo innovation is another pillar: he invented the equi-energy sampler and random-walk schemes to explore energy landscapes and reconstruct density-of-states in statistical physics and Bayesian computation. In bioinformatics he builds model-based and sparse-regression tools to integrate RNA-seq, ChIP-seq, chromatin-interaction and sequence data, leading to biological discoveries on RNA editing, alternative splicing and embryonic-stem-cell regulatory circuits. Publication Trends: Recent articles focus on (i) causal discovery in nonlinear and high-dimensional DAGs, (ii) federated and privacy-preserving learning of causal networks, (iii) confidence-set construction post model-selection, and (iv) stochastic blockmodels and graphons for network data. A 2025 research monograph synthesizes advances in latent structure and causal graphical models. Scientific Awards & Recognition: Authored research monograph “Latent Structure and Causality: Inference from Data”, World Scientific 2025 Consistent federal and UCLA funding (implied by continuous publication output and chair position) Advising & Grants: While specific student names and grant numbers are not listed in the source text, the sustained publication stream in top-tier journals and the role as department chair indicate active supervision of doctoral students and postdocs and ongoing NIH/NSF support. Labs & Teams: Zhou leads the computational statistics and causal inference group within the UCLA Department of Statistics & Data Science, maintaining cross-departmental collaborations with the Bioinformatics program and experimental biology labs on campus.

• Systemic Risk
• Financial Networks
• Stochastic Analysis
• +۴ مورد دیگر

Andreea Minca is a Professor in the School of Operations Research and Information Engineering at Cornell University, holding a PhD in Applied Mathematics from Sorbonne University and a Diplome from Ecole Polytechnique. Her research focuses on mathematical modeling of financial systems under uncertainty, with emphasis on systemic risk and network-based contagion dynamics. Her educational background includes: PhD in Applied Mathematics, Sorbonne University Diplome de l'Ecole Polytechnique, Ecole Polytechnique Minca's research pioneered structural models for systemic risk using network theory to represent financial interconnections. She developed foundational work on contagion in large systems through asymptotic analysis of diffusions on random graphs and introduced optimization frameworks under network uncertainty. Current interests extend to decentralized finance, stablecoin design, reinforcement learning in graphon games, and pandemic-related risk mitigation for supply chains. Her methodology integrates stochastic analysis, game theory, and network science to derive stability-promoting policies. Recent publications reveal a strategic shift toward decentralized finance applications while maintaining core systemic risk research. Key trends include modeling stablecoin vulnerabilities, applying clustering algorithms to financial networks, and developing reinforcement learning solutions for mean-field games. This interdisciplinary work bridges operations research, finance, and computer science to address contemporary challenges in financial stability and emerging technologies. Her distinguished awards include: 2016 SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize NSF CAREER Award (2017) Research Fellow of GARP (2014) AXA Research Fund Awardee (2020) Minca has advised seven PhD students with placements at JPMorgan, Northwestern University, Citadel, AQR, Tower Research Capital, and Gyroscope Stablecoin (founded by her student). Her research is supported by competitive grants including the NSF CAREER award and AXA-funded project on pandemic risk mitigation. She serves on editorial boards of Mathematical Finance, SIAM Journal on Financial Mathematics, and European Journal of Operational Research. Her work operates within complex financial systems research, collaborating across disciplines to develop mathematical frameworks for crisis management and system resilience, particularly through network analytics and machine learning applications.

Benedikt Stuflerمشاهده پروفایل

استاد

• Combinatorics
• Probability Theory
• Random Structures
• +۵ مورد دیگر

Prof. Benedikt Stufler is a Professor of Mathematics at Technische Universität Wien's Institute of Discrete Mathematics and Geometry. His research focuses on the interplay between combinatorics and probability, particularly the analysis of random structures such as graphs, trees, and maps. He holds a Habilitation in Mathematics (2022) and a Doctorate from Ludwig-Maximilians-Universität München (2015). Education: Habilitation in Mathematics, TU Wien (2022) Doctorate in Mathematics, LMU Munich (2015) Master's (Diplom-Mathematiker), LMU Munich (2013) Study abroad at Universidad de Buenos Aires (2011) Research Interests: His work combines stochastic methods with combinatorial constructions to study models of random structures. Key areas include: Limits of random structures (Gromov-Hausdorff-Prokhorov convergence) Branching processes and random maps Enumerative combinatorics and Gibbs partition models Phase transitions in weighted graphs Algorithmic sampling of combinatorial structures Recent Grants: FWF Project 'Limits of random networks' (2024–2027, €435k) Principal Investigator in SFB 'Discrete Random Structures' (2024–2028, €3.9M total) Lab/Group: Current members include postdoc Dr. Colin Desmarais and PhD students Niccolò Bosio and Philipp Beltran. He supervises master's theses in probability theory.

Maryanthe Malliarisمشاهده پروفایل

استاد

• Model Theory
• Mathematical Logic
• Classification of Theories
• +۸ مورد دیگر

Maryanthe Malliaris is a Professor in the Department of Mathematics at the University of Chicago. Her research focuses on model theory , particularly the classification of theories and interactions between model theory, set theory, and combinatorics. University of Chicago, Department of Mathematics Research Interests: Malliaris works on model theory, ultrafilters, and their applications to problems in set theory, graph theory, and learning theory. She has advanced understanding of Keisler's order, regularity lemmas, and cofinality spectrum problems. Scientific Contributions: Her recent work connects model theory to machine learning, including PAC learning and online learning. She has contributed to the classification of simple theories, topological model theory, and Ramsey-type combinatorics. Awards and Grants: NSF CAREER Award DMS-1553653 Gödel Research Prize Fellowship Sloan Research Fellowship NSF-BSF 2051825 NSF Grants DMS-1300634, DMS-1001666 Teaching: Courses include Math 277 and Math 309. Contact: Department of Mathematics, University of Chicago, 5734 S. University Avenue, Chicago, IL 60637. Email: malliaris@math.uchicago.edu . Office: 406.

Jean François DELMASمشاهده پروفایل

استاد

• Probability Theory
• Random Trees
• Mathematical Biology
• +۵ مورد دیگر

Roles and Affiliations: Jean François DELMAS is a Professor at the École des Ponts ParisTech and a member of the CERMICS research center. His research focuses on probability theory, stochastic processes, and their applications in biology, computer science, and mathematical modeling. He has held administrative roles, including Director of CERMICS (2009–2017) and contributed to academic programs like the Master in Mathematics and Applications. Research Interests: DELMAS specializes in random trees (Galton-Watson, Lévy trees), branching processes, coalescent theory, and stochastic models in biology. His work bridges theoretical probability with applications in epidemiology, genetics, and statistical physics. Key areas include the study of metapopulation SIS models, vaccination strategies, and the analysis of large random networks using graphon theory. Teaching: DELMAS teaches advanced courses such as Stochastic Processes and Applications and Large Random Networks at the graduate level. He has authored textbooks on probability and statistics, including Random Models: Applications to Engineering and Life Sciences (with Benjamin Jourdain). Advising and Grants: DELMAS has supervised numerous PhD students, many of whom have contributed to groundbreaking work in probability and biology. His grants include ANR projects on continuum random trees and collaborations with institutions like EDF on risk management and stochastic modeling. Labs and Teams: Primary affiliation with CERMICS, part of the Université Gustave Eiffel . Active in interdisciplinary collaborations, including work with nuclear engineering and environmental science groups.

Olga Kloppمشاهده پروفایل

استاد

• Nonparametric Estimation
• High-Dimensional Inference
• Network Models
• +۴ مورد دیگر

Olga Klopp is a Professor of Statistics at ESSEC Business School and a permanent member of the CREST research center. Her work focuses on nonparametric estimation, high-dimensional inference, network models, and matrix completion. Research spans theoretical and applied statistics Specializes in sparse/high-dimensional data and network analysis Develops algorithms for matrix completion and graphon estimation Recent research includes tensor decomposition for economic networks, detection of change-points in dynamic networks, and handling missing data in epidemic modeling. She has received ERC and ANR grants for innovative projects. PhD Students: Solenne Gaucher, Mokhtar Alaya, Guillermo Martin Key methodological contributions in low-rank modeling and robust estimation