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Byeong U. ParkView profile

Professor

• Nonparametric structured models
• Semiparametric inference
• Non-Euclidean data analysis
• +2 more

Byeong U. Park is a Professor in the Department of Statistics at Seoul National University, College of Natural Sciences. He has held this position since 1999 and previously served as Assistant Professor (1988–1992) and Associate Professor (1992–1999) at the same university. His research focuses on nonparametric structured models, semiparametric inference, and non-Euclidean data analysis. 1982: B.Sc., Department of Computer Science and Statistics, Seoul National University 1984: M.Sc., Department of Computer Science and Statistics, Seoul National University 1987: Ph.D., Department of Statistics, University of California at Berkeley (supervised by Peter J. Bickel) His research spans nonparametric regression, density estimation, and analysis of complex data on manifolds. Key methodologies include smooth backfitting, local likelihood estimation, and handling of errors-in-variables. He has developed techniques for additive models, varying coefficient regression, and high-dimensional data analysis. Notable scientific awards include the 2019 Incheon Award of Science and Technology, 2018 Carver Medal from IMS, 2017 Seoul National University Research Award, and fellowships from IMS, ASA, and KAST. He has served as Editor-in-Chief for multiple journals and held leadership roles in the Bernoulli Society and ISI. 2019–2023: Vice President, International Statistical Institute 2013–2017: Elected Council member, Bernoulli Society 2002–2004: Editor-in-Chief, Journal of Korean Statistical Society As head of the Nonparametric Inference Lab at Seoul National University, he leads research on infinite-dimensional statistical models and nonparametric methods, emphasizing applications to real-world problems in economics and biomedical data.

• Tectonics
• Geodynamics
• Seismology
• +3 more

Francisco Manuel Alonso Chaves is a Professor in the Department of Earth Sciences at the Faculty of Experimental Sciences, University of Huelva, Spain. He is affiliated with the Huelva Scientific and Technological Center and leads the research group RNM276 APPLIED GEOSCIENCES. His academic focus lies in Internal Geodynamics, contributing significantly to the understanding of tectonic processes in the Betic Cordillera and surrounding regions. Education: PhD in Geology, University of Granada (1995). Thesis: "Tectonic evolution of Sierra Tejeda and its relationship with processes of crustal thickening and thinning in the Betic mountain ranges", supervised by Dr. Miguel Orozco Fernández. His primary research interests encompass Tectonics, Geodynamics, Seismology, and Structural Geology. He investigates crustal deformation, seismic activity, and the evolution of mountain belts, with a regional focus on the Betic Cordillera and the Iberian Peninsula. His work integrates field studies, geophysical methods, and advanced data analysis to unravel complex tectonic histories and assess seismic hazards. Analysis of his recent publications reveals a strong emphasis on tectonic processes, particularly in the Guadalquivir Basin and the Betic Cordillera. His work utilizes seismic noise recording, kernel density estimation, and passive seismic techniques to study basin architecture, fault reactivation, and crustal structure. There is a consistent focus on Neogene extension, earthquake analysis (including the Türkiye-Syria events), and the application of geospatial tools like QGIS for tectonic interpretation. Scientific Awards: No scientific awards mentioned in available information. Advising and Grants: No details provided regarding students supervised or research grants secured. Labs and Teams: Dr. Alonso Chaves is a key member of the RNM276 APPLIED GEOSCIENCES research group and conducts his work at the Huelva Scientific and Technological Center. His team focuses on applied geological research, including seismic microzonation, tectonic modeling, and environmental geology, contributing to both academic knowledge and practical applications in the region.

Fabio NobileView profile

Professor

• Scientific Computing
• Uncertainty Quantification
• Numerical Analysis
• +5 more

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.

Pierre Lafaye de MicheauxView profile

Associate Professor

• Applied Statistics
• Biostatistics
• Statistical Theory
• +11 more

Associate Professor Pierre Lafaye de Micheaux is a statistician based at the School of Mathematics and Statistics, University of New South Wales , where he has worked since 2020. He previously held academic roles at Université Paul Valéry (2020, Associate Professor), ENSAI (2015–2017, Professor), Université de Montréal (2011–2016, Associate Professor), and Grenoble Alps University (2003–present, Assistant Professor). His research spans theoretical and applied statistics , focusing on complex random vectors , neuroimaging genetics , and data science for IoT . Education: PhD in Statistics (2003, Université de Montréal & Montpellier) MSc in Biostatistics (1998, Montpellier) BSc in Mathematics and Physics (1996, Montpellier) MSc in Cognitive Neuroscience (2007, Grenoble Institute of Technology) Research interests include: Dependence Measures : Leveraging complex analysis for big data dependence testing under 3V's (Volume, Variety, Velocity). Neuroimaging Genetics : Developing statistical tools for fMRI/EEG/DTI phenotyping of genetic variation with institutions like CHeBA and INSERM. IoT Data Science : Creating Raspberry Pi-based statistical computing tools for real-time sensor data streams. Complex-Valued Inference : Building a unified framework for complex random vectors in neuroimaging and nuclear engineering. Recent publications demonstrate expertise in circular data analysis , nonparametric testing , and central limit theorem counterexamples , with applications in medical imaging and finance. He has supervised numerous PhD, MSc, and honors students on topics ranging from deep learning to stochastic processes. Scientific achievements include: Université de Montréal Provost Honor List (2003) Editor of the Journal of Statistical Software (2017–present) Co-leader of three research groups: Dependence Measures , Neuroimaging Genetics , and Data Science & IoT He has secured grants from UNSW Research Infrastructure Scheme and NSERC , with industry collaborations including BNP Paribas (credit risk) and Olea Medical (stroke treatment analytics). Current teaching includes Statistical Inference (ZZSC5905) and Data Science (DATA3001) at UNSW.

Helen WoolleyView profile

Professor

• Children's Outdoor Environments
• Landscape Architecture
• Urban Green Spaces
• +9 more

Professor Helen Woolley is Head of the School of Architecture and Landscape at the University of Sheffield, where she is a leading academic in children's outdoor environments and landscape architecture. Her research spans inclusive play spaces, post-disaster landscapes, urban green space equity, and children's spatial agency. Research Interests: Her work focuses on the relationship between people and green spaces, especially children's access to and use of outdoor environments. She investigates policy, design, and societal influences on play, with a strong emphasis on inclusion, disaster recovery, and urban planning. Her research has been funded by ESRC, AHRC, JRF, JSPS, and the Big Lottery. Recent Publications Trends: Her recent articles (2020–2025) show a global reach, with studies in China, Brazil, and Jordan, using advanced spatial analysis (SOPARC, KDE), co-design methods, and comparative disaster research. Themes include water play, mobility safety, generational change in play, and multifunctional urban green spaces. Scientific Awards: Japanese Society for the Promotion of Science (JSPS) Visiting Fellowship (2014) Invited to Queen’s Garden Party at Buckingham Palace (2003) Landscape Institute Research Award (1997) Winner, Sunday Telegraph Town Garden Design Competition (1982) Advising and Grants: She actively supervises PhD students such as Melih Bozhurt, Junfang Xie, and Maryam Mani. Her grant portfolio includes major projects like Living with Nature (Big Lottery funded), Children’s Lost Landscapes in post-disaster Japan (Daiwa Foundation), and Knowledge Transfer Partnerships with Timberplay and Fairplay. She has led research for CABE Space, Natural England, and the National Audit Office. Labs and Teams: She is a member of the Centre for the Study of Childhood and Youth and the Sheffield Urban Institute. She co-led the Living with Nature partnership with Sheffield City Council and Wildlife Trusts, and contributed to interdisciplinary initiatives like the Green Space Forum and Sheffield Botanical Gardens regeneration.

Martin Skovgaard AndersenView profile

Associate Professor

• Optimization
• Signal Processing
• Machine Learning
• +12 more

Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.

• Mechatronics
• Signal Processing
• Control Systems
• +7 more

Jonas Sjöberg is a Full Professor of Mechatronics at Chalmers University of Technology, where he leads the Mechatronic research group in the College of Engineering. His research spans multiple aspects of mechatronic systems with a strong focus on automotive applications. Sjöberg holds leadership roles in numerous research projects related to autonomous vehicles, vehicle control systems, and transportation safety. His research interests encompass a broad spectrum of mechatronics applications, with particular emphasis on model-based methods, signal processing, control systems, system identification, and optimization for design and product development of mechatronic systems. Sjöberg's work bridges theoretical control engineering with practical automotive applications, especially in the domains of Automotive Active Safety and Hybrid Electric Vehicles. Analysis of Sjöberg's recent publications reveals a strong research trajectory focused on autonomous vehicle technologies, with particular attention to vehicle dynamics control, intersection safety, road surface condition estimation, and optimization of vehicle maneuvers. His work demonstrates a consistent approach of applying advanced control theory to solve real-world transportation challenges, with increasing emphasis on machine learning techniques integrated with traditional control systems. Sjöberg actively supervises research and education at both undergraduate and graduate levels while leading multiple research projects funded by VINNOVA, the European Commission, and other organizations. His research group collaborates extensively with both academic institutions and industry partners in the automotive sector. His laboratory work focuses on mechatronic systems development, particularly for automotive applications including autonomous bicycles, bus docking systems, and vehicle control algorithms. The research group maintains strong connections with the automotive industry, particularly in Sweden's robust vehicle technology ecosystem.

Xue-Mei LiView profile

Professor

• Stochastic Analysis
• Geometric Stochastic Analysis
• Malliavin Calculus
• +5 more

Xue-Mei Li is a Professor of Mathematics at Imperial College London and École Polytechnique Fédérale de Lausanne (EPFL). She holds chairs in Probability and Stochastic Analysis at both institutions. Her research focuses on stochastic analysis, geometric stochastic processes, and multi-scale systems, with contributions to areas like Malliavin calculus, fractional dynamics, and coarse curvature. Li has held positions at the University of Warwick, University of Connecticut, and others, supported by fellowships from the Alexander von Humboldt Foundation, Royal Society, and MSRI. Her work addresses fundamental questions in stochastic differential equations, geometric analysis, and their applications to complex systems. Education and Career: PhD in Mathematics, University of Warwick EPSRC Research Associate Faculty positions at the University of Connecticut (tenured Associate Professor) Research Interests: Her research spans stochastic differential equations (SDEs), stochastic partial differential equations (SPDEs), geometric stochastic analysis, and fractional dynamics. Notable contributions include the BEL formula, strict local martingales, and solutions to longstanding problems in strong completeness on non-compact manifolds. She explores interactions between stochastic processes and geometric structures, including coarse Ricci curvature and homogenization theory. Awards and Grants: Supported by NSF, EPSRC/UKRI, and Swiss NSF grants Awarded fellowships from Alexander von Humboldt Foundation, Royal Society, and MSRI Advising and Teams: PhD students: Johann Gehringer, Rhys Steel, Julian Sieber, and others Leading working groups on stochastic analysis and geometric dynamics

Daniel KressnerView profile

Professor

• Numerical Linear Algebra
• High-Performance Computing
• Matrix Equations
• +4 more

Prof. Daniel Kressner is a Professor at the École Polytechnique Fédérale de Lausanne (EPFL), holding positions in the School of Basic Sciences (SB), Mathematics Institute (MATH), and the Numerical Algorithms and High-Performance Computing (ANCHP) group. He also leads the SMA-ENS unit within the SB-SMA division. His research focuses on numerical linear algebra, high-performance computing, and tensor approximation methods, with applications in scientific computing and data science. Education details are not explicitly listed, but his career at EPFL includes leadership in key research groups and doctoral programs. He supervises multiple doctoral students, including Alice Cortinovis, Peter Effenberger, and others. Research interests emphasize low-rank methods, matrix equations, and efficient algorithms for large-scale problems. Recent work includes advancements in randomized algorithms, tensor networks, and preconditioning techniques for eigenvalue problems. His publications span high-impact journals like Siam Journal on Matrix Analysis and Applications and Numerical Linear Algebra with Applications, addressing topics such as compressed sensing, multigrid methods, and distributed signal processing. Prof. Kressner advises doctoral candidates and contributes to the Program doctoral Mathématiques (EDMA-GE) committee. His lab, ANCHP, develops software tools for hierarchical matrices and tensor computations, such as the hm-toolbox for HODLR and HSS matrices.

Xiuyuan ChengView profile

Associate Professor

• Applied Mathematics
• High-Dimensional Data Analysis
• Signal Processing
• +4 more

Xiuyuan Cheng is an Associate Professor of Mathematics at Duke University, affiliated with the Trinity College of Arts & Sciences. Her expertise lies in applied analysis, focusing on developing theoretical and computational techniques for high-dimensional data analysis, signal processing, and machine learning. She holds a Ph.D. from Princeton University (2013). Her research emphasizes graph-based methods, kernel techniques, and deep learning applications in manifold data analysis. Notable contributions include work on graph Laplacian convergence, optimal transport for single-cell data, and rotation-equivariant neural networks. She has received grants from the National Science Foundation, including a CAREER award (2023–2028) for learning graph diffusion from high-dimensional data. Recent publications explore topics such as bi-stochastic graph normalization, neural tangent kernels, and adversarial defense using basis transformations. Teaching includes advanced courses like Measure and Integration and High-Dimensional Data Analysis. She collaborates extensively in interdisciplinary projects, integrating mathematical theory with computational tools for real-world applications.

Boris LandaView profile

Assistant Professor

• Statistical Signal Processing
• Geometric Data Analysis
• Manifold Learning
• +5 more

Boris Landa is an Assistant Professor in the Department of Electrical & Computer Engineering at Yale University. His research focuses on statistical signal processing and geometric data analysis, developing theoretical and computational tools for analyzing large, complex datasets. He holds a Ph.D. and M.S. from Tel Aviv University, Israel, and a B.S. from the Technion - Israel Institute of Technology. His work bridges computational methods with applications in molecular biology, cryo-electron microscopy, and high-dimensional data analysis. Notable contributions include robust inference of manifold geometry, doubly stochastic scaling techniques, and multi-reference factor analysis for alignment problems. Recent research explores optimal transport metrics for single-cell data and noise stabilization in signal recovery. Landa's publications span prestigious journals such as SIAM Journal on Mathematics of Data Science and Information and Inference. His methodologies address challenges in manifold learning, graph Laplacian normalization, and biological dataset integration. Active areas include developing adaptive algorithms for low-rank signal detection and robust statistical techniques for heterogeneous data. His educational background includes advanced studies in applied mathematics and engineering, with a strong emphasis on interdisciplinary applications. Ongoing projects involve geometric approaches to omics data analysis and scalable solutions for large scientific imaging datasets.

Leslie GreengardView profile

Professor

• Integral Equation Methods
• Electromagnetics
• Acoustics
• +5 more

Leslie Greengard is a Silver Professor of Mathematics and Computer Science at New York University's Courant Institute, part of the Faculty of Arts and Science. His research focuses on integral equation methods for electromagnetics, acoustics, plasma physics, and fluid dynamics, with recent work extending to kernel-based methods in statistical inference. He leads the Courant Mathematics and Computing Laboratory and contributes to novel electromagnetic simulation techniques. Education: Ph.D. and M.D. in Computer Science and Medicine from Yale University (1987) B.A. in Mathematics from Wesleyan University (1979) Research Interests: Greengard's group develops fast solvers for complex geometries in electromagnetics and fluid dynamics. Notable contributions include the Fast Multipole Method (FMM) for particle simulations and Quadrature by Expansion (QBX) for layer potential evaluations. His work bridges computational mathematics and applications in physics, engineering, and biomedicine. Recent Article Trends: Recent publications emphasize fast algorithms (e.g., FMM, adaptive Gauss transforms), biomedical applications (e.g., deep brain stimulation modeling), and cosmological simulations. His methods address challenges in high-dimensional data, multiscale problems, and real-time computation. Awards & Recognition: While no specific prizes are listed, his foundational work on FMM has had a transformative impact on computational science. Advising & Collaborations: Collaborations span academia and industry, with co-authors including V. Rokhlin, M. O’Neil, and others. No formal advisees are listed here, though his research involves postdoctoral fellows and graduate students. Labs & Teams: Active in the Courant Mathematics and Computing Laboratory, focusing on electromagnetic simulation and design methodologies.

Didong LiView profile

Assistant Professor

• Biostatistics
• Machine Learning
• Geometric Data Analysis
• +7 more

Dr. Didong Li is an Assistant Professor in the Department of Biostatistics at the Gillings School of Global Public Health , University of North Carolina at Chapel Hill. His research focuses on statistical methods for complex data, including manifold learning, nonparametric Bayesian inference, and spatial statistics, with applications in healthcare, genomics, and environmental health. He holds a PhD in Mathematics from Duke University (2020), and degrees from Beijing Institute of Technology (BS 2012, MS 2015). His research interests include dimension reduction, Gaussian processes, geometric data analysis, and network analysis. He has developed novel methods for analyzing electronic health records, single-cell RNA sequencing data, and spatial omics datasets. Notable contributions include the spherelet manifold approximation framework and the STimage-1K4M dataset. Dr. Li has received the Inaugural IMS Lawrence D. Brown PhD Student Award (2019) . He serves on the editorial board of the Journal of Machine Learning Research and organized sessions at JSM 2021-2022 on geometric Bayesian methods. His lab supports interdisciplinary collaborations, with ongoing projects in spatial transcriptomics, healthcare access analysis, and AI-driven medical informatics. Advising highlights include guiding students through successful PhD defenses (e.g., Dr. Sam Hawke, Dr. Jiawen Chen) and postdoctoral placements. His team secured a NVIDIA Academic Grant (2024) for multi-modal AI in spatial transcriptomics. The Li Lab maintains active partnerships with institutions worldwide, advancing both statistical theory and real-world applications.

Danica M OmmenView profile

Associate Professor

• Forensic Statistics
• Computational Statistics
• Handwriting Analysis
• +3 more

Danica M Ommen is an Associate Professor at Iowa State University, specializing in forensic statistics and computational methodologies. Her research bridges machine learning, handwriting analysis, and source identification frameworks. Education: Ph.D. in Computational Science and Statistics (2017), M.S. in Mathematics (2014), B.S. in Mathematics (2012), all from South Dakota State University. Affiliations: Chair of the OSAC Statistics Task Group; Vice-Chair of the ASA Advisory Committee on Forensic Science. Her work focuses on statistical modeling for forensic evidence , particularly in handwriting identification, aluminum powder analysis, and digital device forensics. Recent publications explore interpretable deep learning, synthetic data anchoring, and ensemble methods for likelihood ratios. The 15 most recent articles (2023–2025) span forensic machine learning, handwriting kinematics, multi-camera smartphone identification, and Bayesian frameworks. Keywords include Forensic Science , Machine Learning , and Computational Statistics , with subfields like Score-Based Likelihood Ratios and Smartphone Forensics .

Tesfaye KidaneView profile

Professor

• Structural Geology
• Tectonics
• Paleomagnetism
• +6 more

Dr. Tesfaye Kidane is a tenured Professor in the Department of Environmental Science and Geology at Wayne State University (WSU), College of Liberal Arts and Sciences. Previously, he held academic leadership roles at Addis Ababa University (AAU) and the University of KwaZulu Natal, South Africa. His career includes postdoctoral research fellowships in Hawaii, Japan, Germany, and the United States, along with visiting professorships in Japan. He serves on the American Geophysical Union (AGU) publication committee and as an associate editor for Tectonics since 2017. **Education**: Ph.D. in Internal Geophysics, Institut de Physique du Globe de Paris, France (1999) M.Sc. in Structural Geology, Addis Ababa University (1993) B.Sc. in Geology, Addis Ababa University (1989) **Research Interests**: Dr. Kidane’s work focuses on structural geology, tectonics, and paleomagnetism. He uses paleomagnetic and rock-magnetic techniques to study continental rifting (e.g., the Afar Depression and East African Rift), Pangea/Gondwana reconstructions, and sedimentary processes in extreme environments. His recent projects include the Afar Dallol Drilling (ADD-ON) initiative to explore rift basin dynamics and hydrothermal systems. **Grants & Awards**: Notable awards include the African Fulbright Scholarship (2012–2013), Alexander von Humboldt Fellowship (2012–2013), and JSPS Fellowship (2005–2008). He has secured over $1.4 million USD in grants for equipment and field research, including projects on geothermal exploration and continental rifting mechanisms. **Teaching & Mentoring**: Current courses include Structural Geology , Sedimentology and Stratigraphy , and Geomorphology . While no named advisees are listed, his research collaborations and grant leadership imply extensive mentorship of students and junior researchers. **Labs & Collaborations**: Involved in international initiatives such as the AGU committees and the ADD-ON drilling project. His work integrates field studies, laboratory analyses, and numerical modeling to address tectonic and environmental questions in Africa’s major rift systems.