جستجوی استاد راهنمای دانشگاهی

• Risk Analytics
• Financial Econometrics
• Statistics
• +۴ مورد دیگر

Dr. Rui Ren serves as a Researcher at the Chair of Statistics and Data Science within the Faculty of Business and Economics at the University of Augsburg since January 2025, following prior research roles at IDA Institute Digital Assets (2024) and Humboldt University of Berlin where she held a Marie-Skłodowska-Curie Individual Fellowship (2021-2022). Her academic credentials include a Ph.D. in Management Science from the University of Chinese Academy of Sciences (2019) and a B.Sc. in Mathematics from Minzu University of China (2013), complemented by a Visiting Ph.D. stint in Statistics at Humboldt University of Berlin (2018-2019). Rui Ren's research centers on Risk Analytics and Financial Econometrics with specialized expertise in network-based risk modeling for cryptocurrency ecosystems, sentiment-driven market behavior analysis, and high-frequency portfolio risk assessment. Her methodological approach integrates advanced statistical techniques with machine learning to address complex financial system dynamics, particularly in quantifying tail risks and cross-asset correlations. Her publication trajectory reveals an evolution from traditional market prediction using news sentiment toward sophisticated network analyses of crypto-asset interdependencies, demonstrating consistent innovation in financial risk quantification frameworks like the Financial Risk Meter (FRM). Her scientific recognition includes the prestigious Marie-Skłodowska-Curie Individual Fellowship and a Best Paper Award at the 2017 IEEE Symposium on Analytics and Risk. She actively contributes to thesis supervision and research collaboration within Prof. Okhrin's Chair, participating in statistical consulting services and research seminars focused on advancing data science applications in financial systems.

• Signal Processing
• Machine Learning
• Statistical Signal Processing
• +۱۱ مورد دیگر

Saikat Chatterjee is a Professor in the Department of Information Science and Engineering at the School of Electrical Engineering and Computer Science, Royal Institute of Technology (KTH). He is also a Fellow of Digital Futures and maintains visiting researcher positions at Karolinska Institute, Karolinska Hospital (specializing in 'AI for Health Care'), and Oslo University Hospital in Norway. His primary research interests span Signal Processing and Machine Learning, with specific focus on signal modeling (sparsity, compressive sensing, dynamical systems), statistical signal processing, statistical machine learning, deep learning, speech/audio/image processing, medical data analytics, life science data analysis, perception for autonomous systems, distributed machine learning, and explainable AI (XAI). He particularly emphasizes explainable machine learning, having a strong background in signal processing and statistical machine learning, with growing passion for medical data analysis due to its societal importance. SSF - Swedish Foundation for Strategic Research Region Stockholm European Union Digital Futures Vinnova WASP Companies: Ericsson, Scania, Saab Professor Chatterjee is actively involved in teaching, serving as examiner and course responsible for various degree projects and courses including Machine Learning and Data Science, Pattern Recognition and Machine Learning, and Speech and Audio Processing. His research group has produced significant work across multiple domains, with notable publications in Bioinformatics and smart city applications, demonstrating the breadth of his research impact from healthcare to urban systems.

Anna Gusakovaمشاهده پروفایل

دانشگاهی

• Stochastic Geometry
• Probability Theory
• Random Polytopes
• +۷ مورد دیگر

Anna Gusakova is a Junior Professor at the Institute for Mathematical Stochastics, University of Münster, Germany. Her research lies at the intersection of stochastic geometry , probability theory , and high-dimensional analysis , with a focus on random polytopes, tessellations, and concentration phenomena. Education: She completed her Ph.D. under the supervision of Prof. Dr. Friedrich Götze, with a thesis titled Application of Probability Methods in Number Theory and Integral Geometry . Research Interests: Her work spans a broad range of topics including: Stochastic geometry of random polytopes and tessellations High-dimensional probability and concentration inequalities Poisson processes and their geometric applications Convex and integral geometry Number-theoretic aspects of random structures Publications Overview: Her recent publications demonstrate a deep engagement with theoretical foundations and asymptotic analysis in stochastic geometry. Notable contributions include studies on the β-Delaunay tessellation , spherical convex hulls , and concentration inequalities for Poisson functionals . These works often involve advanced tools from integral geometry, functional analysis, and probabilistic limit theory. Teaching and Mentorship: She teaches a variety of courses including master's seminars on stochastic geometry, probability theory, and high-dimensional probability. Her teaching emphasizes both theoretical depth and practical applications in modern probability and geometry. Collaborations and Grants: While specific grant details are not provided, her extensive collaboration network includes researchers like Christoph Thäle, Zakhar Kabluchko, and Florian Besau, indicating active participation in international research projects. Laboratory and Team: She is associated with the working group in Mathematical Stochastics at the University of Münster, contributing to a vibrant research environment in probability and geometry.

Adam Simon Levineمشاهده پروفایل

دانشیار

• Low-dimensional topology
• Knot theory
• Heegaard Floer homology
• +۴ مورد دیگر

Adam Simon Levine is an Associate Professor in the Department of Mathematics at Duke University, where he also serves as the Director of Undergraduate Studies for the mathematics department. His research focuses on the intricate structures of low-dimensional spaces and their applications to fundamental problems in topology. Levine received his A.B. from Harvard University in 2005 and his Ph.D. from Columbia University in 2010. His academic journey has positioned him at the forefront of modern topological research, particularly in the study of 3- and 4-dimensional manifolds. Levine's research interests center on low-dimensional topology, with particular emphasis on Heegaard Floer homology, Khovanov homology, and their applications to knot theory, concordance, and exotic 4-manifolds. His work explores how these powerful homological invariants reveal deep structures within seemingly simple geometric objects, particularly concerning the classification of smooth 4-dimensional manifolds—a problem that has challenged topologists for decades due to the unique complexities of four dimensions. He investigates connections between different invariants of knots in 3-space, especially the relationships between those arising from gauge theory, symplectic geometry, and representation theory. Levine's recent publications demonstrate a consistent focus on advancing our understanding of knot concordance, slice knots, and the intricate relationships between different homology theories. His 2024 paper with Hedden provides a strengthened surgery formula for knot Floer homology, while his 2023 work on rationally slice knots reveals surprising uniformity across different constructions. The 2022 papers with Hom and Lidman, and with Gujral, respectively address fundamental questions about knot concordance in homology cobordisms and the behavior of Khovanov homology under cobordisms between split links. Levine is actively involved in teaching and mentoring at Duke University. He has taught a wide range of courses from undergraduate linear algebra to advanced graduate topics in topology. In Fall 2025, he is scheduled to teach multiple sections of Linear Algebra. He has also led specialized minicourses on Knot Homologies, Link Homologies and Immersed Curves, and Introduction to 4-Manifolds, demonstrating his commitment to sharing cutting-edge research with students. Levine's research is partially supported by an NSF grant titled 'Four-Manifolds and Categorification' (DMS-2203860), running from 2022-2026. His work has gained recognition beyond academia, with news coverage in the Duke Research Blog about his crossword puzzle contributions to the New York Times. In 2021, he was among the newly tenured faculty at Duke University, reflecting the high regard for his scholarly contributions.

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 .

Shengrui Wangمشاهده پروفایل

استاد

• Data Mining
• Pattern Recognition
• Machine Learning
• +۱۰ مورد دیگر

Shengrui Wang is a Full Professor in the Department of Computer Science at the University of Sherbrooke, specializing in data mining, pattern recognition, and machine learning with applications spanning bioinformatics, business intelligence, and image analysis. His academic credentials include: D.E.A. from Université Joseph Fourier (1986) Ph.D. in Sciences from Institut National Polytechnique de Grenoble (1989) Professor Wang develops statistical and structural approaches for clustering/classification of high-dimensional and complex data (sequences, trees, graphs), addressing challenges in similarity measures, cluster number determination, outlier identification, and graph comparison. His work integrates fuzzy logic, neural networks, and statistical analysis for symbolic processing like hypothetical reasoning. Applied research includes the CLUSS server for global protein sequence analysis, web community identification systems, business intelligence through socio-economic data mining, radar target detection (boats/oil spills), road extraction in satellite imagery, and intelligent environment activity recognition.

Jianxuan Liuمشاهده پروفایل

استادیار

• Semiparametrics
• Causal Inference
• Measurement Error Models
• +۵ مورد دیگر

Jianxuan Liu is an Assistant Professor in the Department of Mathematics at Syracuse University, affiliated with the Applied Statistics program within the College of Arts and Sciences. His research focuses on semiparametric methods, causal inference, measurement error models, and high-dimensional data analysis. He holds a Ph.D. in Statistics from the University of South Carolina (2017), with prior degrees in Statistical Science (Indiana University, 2012), Economics (East Carolina University, 2010), and a B.S. in Economics from Capital University of Economics and Business (2007). Dr. Liu’s scholarly work emphasizes statistical methodologies for complex data structures, including recent contributions on beta regression for high-dimensional covariates, causal effects of violence exposure on juvenile crime, and longitudinal substance abuse analysis using machine learning. His publications appear in journals like Stat , Journal of Data Science , and Annals of Statistics . He has received grants for projects such as fusion of semiparametric methods with machine learning in causal inference and statistical analysis of error-prone data. Notable awards include an NSF Travel Grant (2022). Dr. Liu is actively involved in academic service, including supervising the MAT 222 course, chairing statistics hiring committees, and advising the Syracuse University ASA Chapter in Statistics and Data Science.

Amiremad Ghassamiمشاهده پروفایل

استادیار

• Causal Inference
• Causal Discovery
• Statistical Learning Theory
• +۳ مورد دیگر

Amiremad Ghassami is an Assistant Professor in the Department of Mathematics and Statistics at Boston University. His research focuses on causal inference and discovery, statistical learning theory, and semiparametric statistics. He develops methodologies to address challenges in causal effect estimation, policy optimization, and causal graph identification in complex real-world data scenarios such as unobserved confounders and measurement errors. His work integrates modern statistical techniques, machine learning, and information theory. Dr. Ghassami holds a PhD in Data Science and Communications from the University of Illinois at Urbana-Champaign (2020), advised by Prof. Negar Kiyavash, and completed postdoctoral research at Johns Hopkins University under Professors Ilya Shpitser and Eric Tchetgen Tchetgen. He co-organizes the Statistics and Probability Seminars at Boston University. Research Interests: Causal Inference and Discovery Statistical Learning Theory Semiparametric Statistics Probabilistic Graphical Models His recent publications emphasize causal data fusion, mediation analysis, and brain connectivity studies using fMRI. He addresses methodological challenges in handling unobserved variables and nonignorable missing data through innovative data fusion strategies and debiased estimation techniques. Dr. Ghassami has contributed to causal structure learning algorithms for cyclic and acyclic models, with applications to neuroscience and policy evaluation. His work bridges theoretical foundations and practical data-driven solutions for modern causal analysis problems.

Youngtak Sohnمشاهده پروفایل

استادیار

• Probability Theory
• High-Dimensional Statistics
• Random Graphs
• +۳ مورد دیگر

Youngtak Sohn is an Assistant Professor in the Division of Applied Mathematics at Brown University. His research bridges probability theory with statistical physics, machine learning, and theoretical computer science. Current investigations focus on high-dimensional statistical inference, random constraint satisfaction problems, and phase transitions in disordered systems. Previously, he was a postdoctoral researcher at MIT and earned his PhD in Statistics from Stanford University under Amir Dembo. Key research areas include: Phase transitions in random constraint satisfaction problems Statistical-computational gaps in high-dimensional inference Replica symmetry breaking in spin glass models Sharp thresholds in graph inference and community detection His publications demonstrate deep mathematical rigor, with recent work establishing fundamental limits in statistical estimation using low-degree polynomials and characterizing exact phase transitions in stochastic block models. The research consistently develops new mathematical frameworks for understanding computational thresholds in high-dimensional statistics and statistical physics. He mentors students through programs like MIT PRIMES, guiding projects on hypergraph coloring thresholds. His teaching portfolio includes graduate courses in probability theory and seminars on statistical learning theory.

Isabella Novikمشاهده پروفایل

استاد

• Algebraic Combinatorics
• Geometric Combinatorics
• Commutative Algebra
• +۴ مورد دیگر

Isabella Novik is a Professor of Mathematics at the University of Washington, affiliated with the Department of Mathematics within the College of Arts and Sciences. She holds editorial roles at Algebraic Combinatorics , Proceedings of the AMS , and SIAM Journal on Discrete Mathematics . Her research focuses on algebraic and geometric combinatorics, particularly the study of polytopes, manifolds, and their connections to commutative algebra and algebraic topology. She co-organizes the Combinatorics and Geometry Seminar at UW with Sara Billey, Gaku Liu, and Ricky Liu. Her work addresses foundational questions in combinatorial geometry, including face enumeration of simplicial complexes, central symmetry properties of polytopes, and topological invariants of manifolds. Recent contributions include resolving the neighborliness of centrally symmetric spheres, reconstructing polytopes via affine stresses, and exploring transversal numbers in simplicial structures. While no formal awards are listed, her extensive publication record reflects significant contributions to combinatorial geometry. She collaborates actively with researchers like Hailun Zheng and Satoshi Murai, advancing the field's theoretical underpinnings.

• Mathematical Physics
• Probability Theory
• Random Matrix Theory
• +۵ مورد دیگر

Gaultier Lambert is an Associate Professor at the Department of Probability, Mathematical Physics and Statistics, KTH Royal Institute of Technology. His research focuses on the intersection of probability theory, mathematical physics, and random matrix theory, with particular emphasis on determinantal processes, log-correlated fields, and quantum statistics. He holds a PhD and has an extensive publication record in high-impact journals. Key research areas include fluctuations in free fermionic systems, multiplicative chaos, and universality in random matrix ensembles. His work explores topics such as characteristic polynomials of random matrices, law of large numbers for Coulomb gas potentials, and quantum statistics transmutation via magnetic flux attachment. Recent publications (2021–2025) emphasize asymptotic analysis of eigenvalue fluctuations, universality in semiclassical systems, and stochastic processes in high-dimensional spaces. Lambert’s contributions bridge theoretical probability with applications in quantum physics and statistical mechanics. No scientific awards or grants are explicitly listed in the provided texts. He advises no listed students and is affiliated with the Lindstedtsvägen 25 research group at KTH.

Axel Munkمشاهده پروفایل

استاد

• Mathematical Statistics
• Biosciences
• Biophysics
• +۱۰ مورد دیگر

Prof. Axel Munk is a University Professor at the Institute for Mathematical Stochastics , Georg-August-University of Goettingen, and serves as the director of the Felix Bernstein Institute for Mathematical Statistics in the Life Sciences (FBMS) within the Faculty of Mathematics and Computer Science. His work bridges mathematical statistics with biosciences and biophysics, focusing on advanced statistical methodologies for complex data analysis. Research Interests: Shape analysis, stochastic models in biology, ion channel dynamics, optimal transport theory, and super-resolution microscopy. Awards: Institute of Mathematical Statistics (IMS) Fellow (2016). Collaborations: Involved in DFG Collaborative Research Centers (e.g., SFB 755, SFB 860) and Priority Programs (e.g., SPP 1590). Software Contributions: Developed packages like stepR , J-SMURF , and icmcstat for ion channel analysis and multiscale methods.

Fan Wangمشاهده پروفایل

پژوهشگر ارشد

• Change point analysis
• High-dimensional statistics
• Transfer learning
• +۱ مورد دیگر

Fan Wang is a Research Fellow at the Centre for Research in Statistical Methodology (CRiSM) at the University of Warwick . Previously, they completed a PhD in 2024 under Professor Yi Yu at Warwick, a Master's in Mathematical Statistics (Part III) at the University of Cambridge (2020) , and a Bachelor's in Advanced Mathematics from the University of Sydney (2018) . Research interests focus on change point analysis , high-dimensional statistics , transfer learning , and network analysis . Notable work includes contributions to transfer learning frameworks for dynamic pricing, penalized mean estimation, and change point detection in high-dimensional regression and network data. Service includes peer review for prestigious journals such as Bernoulli , Biometrika , and Journal of the Royal Statistical Society, Series B . Contact: fan.wang.6@warwick.ac.uk .

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.

Ramchandra Rimalمشاهده پروفایل

استادیار

• Machine Learning
• Statistical Network Models
• Classification and Clustering
• +۳ مورد دیگر

Dr. Ramchandra Rimal is an Assistant Professor in the Department of Mathematical Sciences at Middle Tennessee State University (MTSU), specializing in Data Science. He holds a PhD from the University of Central Florida (2020), an MS from UCF (2017), and a BS from Tribhuvan University (2009). His research focuses on Machine Learning, Statistical Network Models, and Classification/Clustering, with applications across diverse fields such as healthcare, finance, and biological sciences. He develops novel methodologies to solve real-world problems, including predictive modeling for stock markets, neurocognitive analysis, and network topology estimation. Dr. Rimal has published in top journals like the Journal of the Royal Statistical Society and Journal of Machine Learning Research , with notable contributions to stochastic block models, density deconvolution, and deep learning frameworks. His work bridges theoretical advancements with practical applications in data-driven decision making. Awards: 2019 UCF Graduate Research Excellence Award, 2018 UCF Teaching Assistant Award Teaching: Courses include Probability & Statistics, Applied Predictive Modeling, and Data Science graduate programs at MTSU. He actively presents at conferences such as the Symposium on Data Science and the Southeastern International Conference on Combinatorics. His research group collaborates on interdisciplinary projects, emphasizing both computational innovation and domain-specific relevance.