جست‌وجوی استاد و پژوهشگر

Henry Cohnمشاهده پروفایل

استاد

• Discrete Mathematics
• Sphere Packing
• Conformal Field Theory
• +۸ مورد دیگر

Henry Cohn is a Professor of Mathematics at the Massachusetts Institute of Technology (MIT), specializing in discrete mathematics, sphere packing, conformal field theory, and fast matrix multiplication. His work bridges pure and applied mathematics, emphasizing symmetry in exceptional structures and applications to physics, cryptography, and information theory. He supervises graduate students and research interns, particularly at Microsoft Research and MIT. Education: Ph.D., Mathematics, Harvard University (2000) B.S., Mathematics, MIT (1995) Professional Background: Postdoc to long-term member, Microsoft Research Redmond (2000–2007) Head, Cryptography Group, Microsoft Research (2007–2008) Founding member, Microsoft Research New England (2008–2024) Professor, MIT Department of Mathematics (2025–present) Research Interests: Cohn explores symmetry in mathematics and physics, focusing on sphere packing, potential energy minimization, and applications of harmonic analysis and representation theory. His work spans discrete geometry, coding theory, cryptography, and theoretical computer science. Publications & Grants: He has published extensively on sphere packings, error-correcting codes, and matrix multiplication, often leveraging modular forms and semidefinite programming. His research is supported by institutions like the Simons Foundation. Teaching & Mentorship: Cohn teaches courses in number theory, discrete geometry, and mathematical logic at MIT and programs like PROMYS. He has advised numerous Ph.D. students and research interns, including Yichi Zhang, James Hirst, and Alex Cohen.

Stefan Sperlichمشاهده پروفایل

استاد

• Nonparametric Statistics
• Semiparametric Methods
• Small Area Statistics
• +۷ مورد دیگر

Stefan Sperlich is a Full Professor and Director of the Research Institute for Statistics and Information Science at the University of Geneva's Geneva School of Economics and Management. He holds dual appointments in the Department of Econometrics and Statistics, with affiliations in both the Research Institute for Statistics and Information Science and the Institute of Economics and Econometrics. Dr. Sperlich earned his diploma in mathematics from the University of Göttingen and completed his PhD in economics at Humboldt University of Berlin. His academic career includes professorships at University Carlos III de Madrid (1998-2006) and the University of Göttingen (2006-2010), before joining the University of Geneva in 2010. Professor Sperlich's research spans nonparametric and semiparametric statistics , small area estimation , and impact evaluation methods . His work bridges theoretical econometrics with practical applications in development economics, policy evaluation, and poverty measurement. He has made significant contributions to specification testing, causal inference methodologies, and the development of robust statistical techniques for small area estimation. His research often addresses real-world problems through collaborations with international institutions and development programs. His recent publications reveal a strong focus on advancing methodological frameworks for small area statistics, causal inference, and nonparametric estimation. Key themes include developing robust inference techniques for linear mixed models, improving bandwidth selection methods, and creating model-free approaches to difference-in-differences estimation. His work increasingly integrates computational statistics with traditional econometric methods to address challenges in big data analysis and distributed data environments. Professor Sperlich has received numerous accolades including: Tjalling C. Koopmans Econometric Theory Prize (2000-2002) Augusto Gonzalez Linares award (2014) for attracting international talent Elected member of the International Statistical Institute (since 2025) Special rewards from the Economics Department at University Carlos III de Madrid (2004-2005) Grants from the Institute Flores de Lemus (2001-2003) As an advisor and researcher, Professor Sperlich has supervised numerous graduate students and led significant research initiatives. He co-founded the research center 'Poverty, Equity and Growth in Developing Countries' at the University of Göttingen and serves as a research fellow at the Center for Evaluation and Development in Mannheim, Germany. His consultancy work spans regional, national, and international institutions, with participation in development programs like EUROSOCIAL and UN assessment reports. He has secured multiple research grants supporting his work in statistical methodology and economic applications. Professor Sperlich leads research teams focused on nonparametric methods, small area statistics, and impact evaluation. His research group develops innovative statistical approaches for poverty mapping, causal inference, and composite indicator construction. The team maintains strong connections with statistical offices and international organizations, ensuring their methodological advances have practical applications in policy development and evaluation.

Alice Suozziمشاهده پروفایل

پژوهشگر ارشد

• Linguistics
• Syntax
• Semantics
• +۶ مورد دیگر

Alice Suozzi is a Research Fellow at the Department of Comparative Linguistic and Cultural Studies, Ca' Foscari University of Venice. She earned her cum laude PhD in Modern Languages, Cultures, and Societies and Language Sciences (Language Sciences curriculum) in 2024 after completing her cum laude Master’s in Italian Studies and Linguistic Sciences at the University of Bologna in 2019. Her research focuses on the syntax-semantics interface, Italian verb argument structures, and language acquisition, particularly instrument realization in typologically diverse languages. Education PhD in Lingue, culture e società moderne e scienze del linguaggio (Curriculum: Scienze del Linguaggio), Ca' Foscari University of Venice (2024) Master’s in Italianistica, Culture Letterarie Europee e Scienze Linguistiche (LM-14 - MODERN PHILOLOGY), University of Bologna (2019) Certificat de Spécialisation en Linguistique, Université de Genève (2024) Research Interests center on Italian syntactic optionality, instrumental argument structures, computational linguistics benchmarks, and comparative studies between spoken and sign languages. Her work integrates experimental methods, corpus analysis, and pre-trained language models to investigate how children acquire complex morphosyntactic structures and how instruments function in Italian dialects and cross-modal communication. Scientific Activities include organizing seminar series like Inclubembo and contributing to conferences such as Zadar Linguistic Forum, LSRL-53, and NOCroDeP 2023. Her publications address topics ranging from Italian clitic pronouns to syntactic bootstrapping hypotheses, with a 2025 article introducing the BAMBI baby language model for Italian.

Vasilije Perovicمشاهده پروفایل

دانشیار

• Matrix Theory
• Numerical Linear Algebra
• Nonlinear Eigenvalue Problems
• +۵ مورد دیگر

Vasilije Perovic is an Associate Professor in the Department of Mathematics and Applied Mathematical Sciences at the University of Rhode Island's College of Arts and Sciences. He joined the university as an Assistant Professor in 2015 and was promoted to Associate Professor in 2023. Dr. Perovic's primary research focuses on matrix theory and numerical linear algebra, with special emphasis on nonlinear eigenvalue problems. His work extends to applications of linear algebra in data science and the development of scientific algorithms for shared and distributed memory systems. His research bridges theoretical mathematics with practical computational applications, particularly in the areas of matrix polynomials, singular value decomposition, and parallel computing. His publication record demonstrates a consistent focus on numerical methods for matrix computations, with recent work centered on hybrid algorithms for singular value decomposition and properties of matrix polynomials. His research shows a progression from theoretical aspects of matrix polynomials to practical computational algorithms with applications in data science. Dr. Perovic has collaborated extensively with James Baglama and other researchers at the University of Rhode Island. His work on software development includes MATLAB implementations of advanced singular value decomposition algorithms that are publicly available. He earned his Ph.D. in Mathematics from Western Michigan University in 2015, with a dissertation titled 'Spectrally Equivalent Matrix Polynomials: Non-standard Representations and Preservation of Structure,' advised by Professor D. Steven Mackey. He also completed his B.Sc. in Applied and Computational Mathematics at Western Michigan University, graduating Summa cum laude and as a member of Phi Beta Kappa and Pi Mu Epsilon honor societies.

• Econometric Time Series Analysis
• Macro-Econometrics
• Co-Integration
• +۳ مورد دیگر

Heino Bohn Nielsen is a Professor at the Department of Economics , University of Copenhagen , specializing in Econometric Time Series Analysis , Co-Integration , Financial Econometrics , and Bootstrap Methods . He holds a PhD in Economics from the University of Copenhagen (2004). His research focuses on econometric theory and methodology, particularly in volatility modeling and simulation-based inference. He has led major research projects such as " Econometric Modeling of Instabilities in Financial Time Series " (2011–2013) and " Theory of the Bootstrap in Econometric Models with Time Varying Volatility " (2017–2021). His recent publications highlight advancements in unit root testing , GARCH-X models , and penalized quasi-likelihood estimation , reflecting his expertise in handling boundary parameters and instability in econometric models. He serves as an Associate Editor for the Oxford Bulletin of Economics and Statistics and has refereed for top journals like the Journal of Econometrics and Journal of Applied Econometrics . His teaching has been recognized with multiple Invisible Hand Awards and a Department of Economics Teaching Award .

Andrew Woodمشاهده پروفایل

استاد

• Non-Euclidean Statistics
• Directional Statistics
• Statistical Shape Analysis
• +۵ مورد دیگر

Professor Andrew Wood is a faculty member at the Research School of Finance, Actuarial Studies and Statistics, Australian National University. His research spans non-Euclidean statistics, theoretical statistics, computational methods, and applied statistics in sciences and medicine. Research Interests : Non-Euclidean statistics, directional statistics, statistical shape analysis, asymptotic theory, computational statistics, stochastic differential equations, and applications in science/medicine. Grants : Funded by Engineering and Physical Sciences Research Council (UK), Biotechnology and Biological Sciences Research Council (UK), and Australian Research Council Discovery Projects. Editorial Roles : Former Joint Editor of Journal of the Royal Statistical Society, Series B and current Associate Editor of Biometrika . Research Trends : Recent publications focus on robust statistical methods for non-Euclidean data, computational approaches for complex distributions, principal component analysis for high-dimensional datasets, and geometric inference on manifolds. Applications include microbiome analysis, surface fractal dimension estimation, and spherical regression models. Supervision & Collaboration : Registered as a supervisor at ANU, with collaborations across disciplines including molecular biology, environmental science, and computational mathematics.

• Computational Statistics
• Nonparametric Regression
• Survival Analysis
• +۴ مورد دیگر

Nora M. Villanueva is an Assistant Professor at the Department of Statistics and Operations Research, University of Vigo. She works at the intersection of computational statistics, nonparametric regression, and survival analysis, with applications to cybersecurity and industry 4.0. Her academic journey includes a PhD in Statistics and Operations Research from the University of Vigo (2021) and prior roles as a Data Scientist at Optare Solutions and Ecomanagement Technology. Education: PhD in Statistics and Operations Research (2021), University of Vigo Her research focuses on predictive modeling, nonparametric curve estimation, and software development for statistical analysis. She has contributed to the creation of four R packages and participated in 17 research projects and 39 conferences. Her most recent publications explore group determination in survival curves, explainable AI methods, and genomic sequence analysis. Her work combines theoretical statistics with practical applications in telecommunications, energy, and biomedical domains. Scientific Awards: Research Award (Science and Technology), Pontevedra Provincial Council (Spain) Premio a la mejor solución del reto IN1: 'Clasificación de ataques a honeypots SSH' Villanueva has extensive teaching experience at the University of Vigo, covering courses in statistics, regression models, and data analytics. She also contributed to industry workshops on predictive modeling and R package development.

Kirsten Schorningمشاهده پروفایل

استادیار

• Design of experiments
• Biostatistics
• Model selection
• +۳ مورد دیگر

Kirsten Schorning is a Junior Professor of Mathematical Statistics at the Department of Statistics, Faculty of Statistics, Technical University of Dortmund. She maintains her office in the Mathematics Building, Room 725, and leads a research group focused on statistical methodology development with applications in biostatistics and toxicology. Education: Bachelor's and Master's degree in Mathematics, Ruhr-University Bochum (2007-2012) Doctorate in Mathematics (Dr. rer. nat.), Ruhr-University Bochum (2012-2016) Dr. Schorning's research primarily centers on Design of Experiments , Biostatistics , and Model Selection and Averaging . Her work develops innovative statistical methodologies for optimal experimental design, particularly in drug combination studies, toxicological assessments, and dose-response modeling. She has made significant contributions to addressing day-to-day variability in toxicity assessments and developing optimal concentration designs for in vitro experiments. Analysis of her recent publications (2019-2025) reveals a strong focus on optimal experimental design methodologies with applications in pharmaceutical research and toxicology. Her work bridges theoretical statistics with practical applications, particularly in determining effective drug doses, analyzing mixture toxicity, and developing robust statistical approaches for concentration-response relationships. A notable trend is her increasing focus on addressing real-world complexities such as day-to-day variability in toxicity assessments and developing guidance frameworks for toxicological dose-response experiments. Dr. Schorning leads a research group at TU Dortmund that includes team members Sabine Bell (secretary), Pauline Baur, Julia Eichhorn, Justin Lamberti, and Onur Gül. Her research has significant implications for pharmaceutical development, environmental toxicology, and clinical trial design, providing statisticians and researchers with improved methodological tools for experimental planning and analysis.

• Cosmology
• Quantum Field Theory
• String Theory
• +۴ مورد دیگر

Enrico Pajer is a Lecturer at the University of Cambridge , affiliated with the Department of Applied Mathematics and Theoretical Physics and part of the General Relativity research group . His academic career includes previous roles as Assistant Professor at Utrecht University, Research Associate at Princeton University, and Postdoc at Cornell and Princeton. Education : PhD from Ludwig Maximilian University of Munich (2005-2008), Bachelor and Master from Scuola Normale Superiore and University of Pisa (2000-2005) Research Interests span theoretical cosmology, quantum field theory, and string theory. Key areas include: Inflationary cosmology Large-scale structure formation Cosmic microwave background Quantum field theory in curved spacetime String theory applications General relativity Publications focus on topics like inflationary unitarity bounds, open effective field theories, cosmological cutting rules, and graviton interactions, with a strong emphasis on quantum gravity and early universe dynamics. Contact : Email: enrico.pajer@gmail.com Room: B1.02, University of Cambridge Phone: 01223 766918 Personal Homepage: http://www.damtp.cam.ac.uk/user/ep551/index.html

Matúš Maciakمشاهده پروفایل

دانشیار

• nonparametric regression
• change-points
• structural breaks
• +۵ مورد دیگر

Matúš Maciak is an Associate Professor in the Department of Probability and Mathematical Statistics at the Faculty of Mathematics and Physics, Charles University . His research spans nonparametric regression , change-point detection , and statistical modeling in financial and biological contexts . Key research areas: Nonparametric smoothing and robustness Structural break analysis Shape-constrained inference Sparsity and LASSO-based methods Bootstrapping dependent data Recent publications focus on exogenous market changes , longitudinal data modeling , and biological applications like diel movement patterns in fish. Technical reports include contributions to ecological assessment methods and intercalibration processes for water quality. Contact: Email: Matus.Maciak@mff.cuni.cz , maciak@karlin.mff.cuni.cz Office: K151, Sokolovská 83, Prague 8

• Enumerative Geometry
• Quantum Algebra
• Quantum Topology
• +۷ مورد دیگر

Professor Gaëtan Borot is a faculty member at the Institute of Mathematics, Faculty of Mathematics and Natural Sciences, Humboldt-Universität zu Berlin. He leads a research group in Mathematical Physics with connections to quantum field theory, string theory, and geometric analysis. His work is supported by multiple Research Training Groups including Rethinking QFT (2019-2029), From geometry to numbers (2024-2029), and the College of Mathematics and Physics Berlin (2020-2029). Professor Borot's research centers around topological recursion and its applications across mathematical physics. His work connects enumerative geometry, quantum algebra, moduli spaces, and random matrix theory through a unifying framework that reveals deep connections between seemingly disparate fields. He investigates how topological recursion provides methods to study quantization of geometric objects and field theories, with applications ranging from Gromov-Witten theory to statistical physics models on random surfaces. Analysis of his recent publications (2021-2024) shows a strong focus on the mathematical structures underlying conformal field theories, particularly Liouville CFT, where he explores Virasoro representations, scattering matrices, and equations of motion. His work on topological recursion continues to expand into new domains including cohomological field theories, Hurwitz theory, and spectral curves, demonstrating the versatility of this algebraic structure across mathematical physics. Professor Borot actively supervises PhD students including Giacomo Umer (who successfully defended in May 2025), Davide Scazzuso, and Niklas Martensen, along with numerous master's and bachelor's students. He organizes the Mathematical Physics Seminar held weekly in Adlershof and has taught courses on integrable systems, random matrix theory, and differential geometry. Despite currently being on leave until September 2025, he continues to supervise thesis projects with availability starting October 2025.

• Bayesian Statistics
• Biostatistics
• Epidemiology
• +۵ مورد دیگر

Stefano Cabras is a Full Professor in the Department of Statistics at University Carlos III de Madrid (UC3M), where he serves as Director of the Flores de Lemus Institute and Deputy Director of the Master's Degree in Statistics for Data Science. His office is located at 10.1.10 - Campomanes (Getafe). Professor Cabras maintains broad interdisciplinary research interests spanning: Bayesian statistics and computational methods Biostatistical applications in nutrition, epidemiology, and medicine Causal inference and policy impact evaluation Machine learning applications in transportation and sports science Statistical modeling of complex biological systems His publication trends show strong emphasis on Bayesian methodologies applied to healthcare, public policy, and urban systems. Recent work demonstrates increasing integration of deep learning with traditional statistical approaches for problems in causal inference, pandemic forecasting, and transportation optimization. Professor Cabras leads several major research initiatives: Principal researcher for 'Statistical modelling for smart cities' (Beijing Metro Group, 2020-2023) Director of 'Contrato marco para servicios de consultoría estadística' (KROLL Advisory, 2022-2024) Co-researcher for 'Predicción de edad biológica mediante IA' (Fundación Mutualidad Abogacía, 2020-2021) He has supervised graduate research including theses on Bayesian metro occupancy modeling and maintains active collaborations through the Flores de Lemus Institute. His research has been applied in policy analysis, healthcare diagnostics, and urban planning internationally.

Pierre Dutilleulمشاهده پروفایل

استاد

• Environment
• Ecology
• Agriculture
• +۷ مورد دیگر

Pierre Dutilleul is a Professor at McGill University's Macdonald Campus, associated with the McGill School of Environment and the Department of Mathematics and Statistics. His research focuses on spatio-temporal heterogeneity, plant and soil imaging, and fractal analysis of natural structures. Current affiliations: Department of Plant Science, McGill School of Environment Specializations: CT scanning for root and wood imaging, multifractal analysis, soil aggregate dynamics Research interests span environmental sustainability , soil science , and plant production , with recent work on 3D root phenotyping, biochar characterization, and space-time modeling of agricultural and seismic events. Articles demonstrate expertise in applied statistics and computational methods for ecological and agricultural systems. Key methodological contributions include: Development of R packages for spatio-temporal analysis Innovative CT scanning applications in plant and soil research Statistical tests for separable variance-covariance structures

Nezamoddini-Kachouie Nezamoddinمشاهده پروفایل

دانشیار

• Statistical Modeling
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Nezamoddini-Kachouie Nezamoddin is an Associate Professor in the Department of Mathematics and Systems Engineering at Florida Institute of Technology , with an affiliate appointment in the Department of Electrical Engineering and Computer Science , both within the College of Engineering and Science . Since joining Florida Tech in 2012, he has built an interdisciplinary research program bridging engineering, medicine, and biology. Education & Training BASc – Electrical & Computer Engineering MASc – Systems Design Engineering, University of Waterloo, 2008 PhD – Biomedical Engineering Postdoctoral Fellow – Harvard Medical School & Harvard School of Public Health, 2010-2012 Research Interests Dr. Kachouie’s work sits at the confluence of statistical modeling , machine learning , artificial intelligence , image processing , pattern recognition , and biostatistics . He leverages these tools to tackle grand challenges in cancer research , public health , and climate change , producing actionable insights from complex, high-dimensional data. Publication Trends With more than 120 peer-reviewed papers spanning 2003-2025, his recent output emphasizes deep learning (U-Net, CNNs), Bayesian spatiotemporal models , and generalized additive models applied to glacier recession , wildlife population monitoring , sea-level rise , and cancer genomics . These works collectively advance both methodological innovation and high-impact societal applications. Student Mentorship & Grants He has mentored 80+ undergraduates who presented at venues such as NCUR, JSM, and AMS. Seven PhD students have graduated under his supervision and now hold academic positions. He has served as PI of the NSF REU Site “Statistical Models with Applications to Geoscience” (2020-2024) and as mentor for the NSF Biomath REU Site (2015-2017). Currently, his group comprises 2 post-docs, 6 PhD, 2 Master’s, and 7 undergraduate researchers. Contact & Resources Email: nezamoddin@fit.edu Office: Crawford 335 (inside 328) Phone: (321) 674-7485 Profiles: Google Scholar , Research Website

Charles L. Bérubéمشاهده پروفایل

استادیار

• Applied geophysics
• Mineral physics
• Mathematical modeling
• +۷ مورد دیگر

Charles L. Bérubé is an Assistant Professor in the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal since 2021. His research couples applied geophysics, mineral physics and machine learning to advance mineral exploration and resource characterisation. Education: B.Sc. in Physics, Université de Montréal Ph.D. in Mineral Engineering, Polytechnique Montréal (2018) Research Interests: Dr. Bérubé focuses on electromagnetic geophysics , induced polarization and machine-learning-assisted inversion . Key themes include: Spectral Induced Polarization (SIP) tomography and modelling Neural-network-based rock-physics inversion Mineral prospectivity mapping with variational autoencoders Quantitative mineralogy and computer vision for outcrop analysis Uncertainty quantification in geophysical imaging Publications Overview: His recent work (2022-2024) demonstrates a clear pivot toward data-driven and AI-enhanced geoscience , integrating Bayesian inversion, generative models and deep learning with traditional exploration geophysics. Earlier studies (2017-2020) concentrated on petrophysical characterisation of the world-class Canadian Malartic gold deposit and non-destructive evaluation of concrete degradation. Graduate Supervision & Funding: Dr. Bérubé has supervised one completed M.Sc. thesis (Benzetta, 2024) on graphite geoelectrics. In May 2023 he received a research grant from the Fonds de recherche du Québec as part of a campus-wide allocation exceeding CAD 1.57 million. Contact: Local B-632, Department of Civil, Geological and Mining Engineering Tel: +1 (514) 340-4711 ext. 4991 | Email: charles.berube@polymtl.ca