جست‌وجوی استاد و پژوهشگر

Abi Adamsمشاهده پروفایل

استاد

• Labor Economics
• Applied Microeconomics
• Gender-Based Violence
• +۳ مورد دیگر

Abi Adams is the Nuffield Professor of Economics at the University of Oxford and an affiliated Visiting Professor at Northwestern University (2024/25) and the Becker-Friedman Institute (Spring 2025). She specializes in labor and applied microeconomics, focusing on gender-based violence, job flexibility, and behavioral models. Her work bridges economic theory and policy impact, with awards including the Philip Leverhulme Prize (2023) and the UK ESRC Outstanding Impact Award (2018). Education: DPhil in Economics (Edgeworth Prize), University of Oxford (2011–2015) MPhil in Economics (Distinction), University of Oxford (2009–2011) BA in PPE (First Class), University of Oxford (2006–2009) Research Interests: Quantitative analysis of gender-based violence and its economic consequences Employer-provided job flexibility and labor market dynamics Behavioral models in observational data and survey design Grants & Impact: ERC Starting Grant (€1.5M, 2020): FEMPOWER project Nuffield Foundation Grant (£2.4M, 2024): Transforming Justice Policy advising on equality, labor rights, and pandemic responses Teaching: Graduate courses at Oxford, MIT, and Copenhagen Undergraduate statistics and econometrics instruction Supervised over 20 PhD/MPhil students Affiliations: Editorial Board, Review of Economic Studies Governing Council, Royal Economic Society IFS, CEP, CEPR, and UK Parliament advisory roles

M.B. Eichlerمشاهده پروفایل

دانشیار

• Time Series Analysis
• Econometrics
• Causal Inference
• +۳ مورد دیگر

Dr. M.B. Eichler is an Associate Professor of Econometrics at the QE Econometrics Department within the School of Business and Economics at Maastricht University. His research focuses on advancing methodologies for causal inference in time series analysis, particularly addressing limitations in Granger causality and developing algorithms for multivariate time series with latent variables. He collaborates with institutions like Raytheon BBN Technologies on projects involving heterogeneous data sources for predicting rare events. His expertise spans econometrics, functional time series analysis, and dynamic factor models. Notable projects include electricity spot price modeling, semi-parametric approaches for non-stationary processes, and applications in neuroscience and energy economics. He advises PhD students such as Carlos A. Moreno, Dennis Tuerk, and Anne van Delft. Research Themes: Causal inference, Granger causality, time series econometrics, dynamic factor models Labs/Teams: QE Econometrics Research Group Grants: Open Source Indicator (OSI) Program funded by IARPA Publications highlight contributions to spectral analysis, graphical modeling of time series, and applications in energy markets. His work bridges theoretical advancements with practical challenges in economics, neuroscience, and engineering.

Bo Wangمشاهده پروفایل

استادیار

• Artificial Intelligence
• Cancer
• Genomics
• +۴ مورد دیگر

Dr. Bo Wang is an Assistant Professor at the University of Toronto's Department of Laboratory Medicine & Pathobiology, with a joint appointment in the Department of Computer Science. He holds the Canada CIFAR Artificial Intelligence Chair and serves as Chief Artificial Intelligence Scientist at the University Health Network (UHN). Previously, he was affiliated with the Department of Medical Biophysics and Surgery. Dr. Wang leads the Temerty Centre for AI Research and Education in Medicine (T-CAIREM), focusing on integrating AI into biomedical research and education. He earned his PhD in Computer Science from Stanford University in 2017, specializing in computational biology, cancer subtype prediction, and single-cell analysis. His research interests bridge artificial intelligence, genetics, and healthcare, with a focus on causal inference, machine learning applications in biomedicine, and genomic data analysis. He has pioneered courses like Basic Principles of Machine Learning in Biomedical Research alongside Dr. Rahul G. Krishnan. His work includes developing algorithms for high-dimensional variable selection and instrumental variable methods in causal analysis. Dr. Wang's publications emphasize causal mediation analysis, genetic risk scoring, and longitudinal health outcomes. Notable contributions include studies on vaccine efficacy against long-COVID, schizophrenia treatment efficacy, and Alzheimer’s disease biomarker modeling. Awards include the Canada CIFAR AI Chair (Vector Institute). His research is supported by collaborations with leading institutions and spans computational biology, healthcare analytics, and precision medicine.

Elisabeth GASSIATمشاهده پروفایل

استاد

• Statistics
• Probability
• Hidden Markov Models
• +۷ مورد دیگر

Elisabeth GASSIAT is a Professor at the University of Paris-Saclay, affiliated with the Orsay Mathematics Institute and the Probability and Statistics Team. She is a Senior member of the Institut Universitaire de France (IUF) for the period 2020-2025, recognizing her significant contributions to academia. Her primary research interests include: Hidden Markov Models and their theoretical properties Mixture models and nonparametric statistics Deconvolution and inverse problems with unknown noise Machine learning theory with focus on privacy and security Order identification and model selection Professor GASSIAT's recent work shows a strong evolution from classical statistical theory toward modern machine learning challenges, particularly in understanding theoretical limits of learning in complex models and addressing privacy concerns in machine learning systems. Her research spans both theoretical foundations and practical applications across various domains, with a remarkable consistency in methodological rigor throughout her career. She has received notable recognition as a Senior member of the Institut Universitaire de France (2020-2025). Professor GASSIAT is actively involved in teaching, offering courses such as M1 "Statistique" and Mathématiques 2 (Polytech). She has also co-organized the MHC2021 International conference on Mixtures, Hidden Markov models and Clustering with H. Holzmann and P. Vandekerkhove, demonstrating her leadership in the international statistics community.

Christian Rohrbeckمشاهده پروفایل

مدرس ارشد

• Extreme Value Analysis
• Spatial Statistics
• Environmental Statistics
• +۳ مورد دیگر

Dr. Christian Rohrbeck is a Senior Lecturer in the Department of Mathematical Sciences at the University of Bath. He holds a PhD in Statistics and Operational Research from Lancaster University and leads projects in statistical climatology through the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics and Centre for Climate Adaptation & Environment Research. His research combines extreme value theory, spatial statistics, and Bayesian methods to analyze environmental and financial data. Current projects focus on flood risk modeling, climate extremes standardization, and monotonic regression techniques. Dr. Rohrbeck accepts doctoral students in extreme value analysis, flood risk, and climate adaptation statistics.

Sigbert Klinkeمشاهده پروفایل

استاد

• Statistics
• Data Visualization
• E-Learning
• +۳ مورد دیگر

Sigbert Klinke is a Professor in the Chair of Statistics at Humboldt University of Berlin's Faculty of Economics and Business Administration. He specializes in statistical methodology, computational statistics, and educational technology. His work focuses on developing interactive e-learning platforms for statistics, such as MM*Stat and wiki-based systems. Klinke leads research in statistical software development, including R packages and open-source tools like Yxilon. Education: PhD in Statistics (implied by academic role). Research interests include data visualization, exploratory analysis, and reproducible research. He has authored numerous publications in statistical methodology, software development, and pedagogical innovations. His contributions span over 20 years, with impactful work in both academic research and educational tool creation. Publications emphasize statistical software (e.g., R packages, XploRe), e-learning frameworks, and methodological advancements. His work bridges theoretical statistics with practical applications, particularly in large-scale educational settings. Klinke's projects often involve interdisciplinary collaboration, such as integrating satellite image analysis with statistical computing environments.

Fredrik Lindstenمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Machine Learning
• Approximate Bayesian Inference
• +۷ مورد دیگر

Fredrik Lindsten is a Senior Associate Professor in Machine Learning and Head of the Division of Statistics and Machine Learning at Linköping University's Department of Computer and Information Science (IDA). His research focuses on statistical machine learning, emphasizing probabilistic modeling and uncertainty quantification in methods such as approximate Bayesian inference, representation learning, and graph-based approaches. Applications span weather forecasting, materials science, biochemistry, and automotive industry challenges. Education: MSc (2008), PhD (2013) in Automatic Control from Linköping University; Postdoctoral roles at University of Cambridge, UC Berkeley, and University of Oxford. Affiliations: WASP (Wallenberg AI, Autonomous Systems and Software Program), ELLIIT (Lab for Information and Communication Technology). Research Interests: Lindsten’s work bridges statistical methodology and machine learning, particularly in quantifying uncertainty in predictions. His team explores method development across diverse applications, including spatio-temporal models and graph-based techniques. Recent projects include probabilistic weather forecasting with graph neural networks and cryo-EM reconstruction techniques. Publications: Over 25+ peer-reviewed articles, with recent highlights in Nature Methods , NeurIPS , and Physical Review Materials , focusing on Bayesian methods, generative models, and computational statistics. Awards: Ingvar Carlsson Award (Swedish Foundation for Strategic Research), Benzelius Award (Royal Society of Sciences in Uppsala). Grants & Teams: Supervises 7+ PhD students; active in collaborative projects funded by WASP, ELLIIT, and VR (Swedish Research Council). Labs/Teams: Leads the Division of Statistics and Machine Learning (STIMA) at IDA, fostering interdisciplinary research in probabilistic machine learning.

Katharina Prokschمشاهده پروفایل

پژوهشگر

• Statistics
• Biomedical Imaging
• Data Analysis
• +۴ مورد دیگر

Katharina Proksch is a researcher specializing in advanced statistical methodologies and their applications across diverse scientific domains. Her work focuses on regression models, simultaneous inference, and transforms, with notable contributions to nonparametric testing, inverse problems, and quantitative imaging techniques. She explores interdisciplinary applications in biomedical imaging, astrophysics, and biomechanical analysis, emphasizing rigorous statistical frameworks to ensure methodological robustness. Her research interests include developing statistical tools for super-resolution microscopy, fatigue detection through biomechanical data, and analyzing complex datasets from fluorescence microscopy and X-ray astronomy. Proksch's methodologies often address challenges in handling measurement errors, outliers, and multiscale data structures. Key contributions include a two-sample test using Wilcoxon rank sums, quantitative super-resolution microscopy techniques with statistical guarantees, and fatigue detection algorithms leveraging sequential testing. Her work bridges theoretical statistics with practical applications in health monitoring and astrophysical phenomena modeling.

• Statistical Inverse Problems
• Regularization Theory
• Uncertainty Quantification
• +۵ مورد دیگر

Frank Werner is a Professor of Mathematics at the Chair of Scientific Computing, University of Würzburg, specializing in statistical inverse problems and regularization theory. His research bridges statistics and inverse problems, with applications in biophysics, particularly fluorescence microscopy and non-Gaussian noise modeling. Education: Diplom in Mathematics (2009) and PhD (2012) from Georg-August-Universität Göttingen. Werner's research focuses on uncertainty quantification via minimax tests, nonlinear inverse problems, and photonic imaging. His recent work includes adaptive regularization methods and multiscale scanning techniques. He leads the Inverse Problems team and collaborates internationally, including at Fudan University, Technical University of Chemnitz, and the Max-Planck-Institut für biophysikalische Chemie (2014–2020). His publications span journals like Inverse Problems , Annals of Statistics , and SIAM Journal on Numerical Analysis , with trends emphasizing Poisson data, impulsive noise, and computational algorithms for microscopy. Scientific Awards: Diplomprüfung with Distinction (2009) Erskine Fellowship (2023) Werner is actively involved in academic service, organizing conferences (e.g., AIP2025 in Rio), and mentoring collaborations. He is married with two sons and maintains affiliations with societies like the EMS-TAG Inverse Problems and the Society for Inverse Problems.

Bodhisattva Senمشاهده پروفایل

استاد

• Nonparametric Statistics
• Large Sample Theory
• Shape-Constrained Estimation
• +۴ مورد دیگر

Bodhisattva Sen is a Professor of Statistics at Columbia University, New York. His research focuses on nonparametric statistics, large sample theory, optimal transportation, and statistical applications in astronomy. He completed his Ph.D. in Statistics at the University of Michigan (2008) and holds degrees from the Indian Statistical Institute, Kolkata (B.Stat., M.Stat.). His work spans shape-constrained estimation, bootstrap inference, and interdisciplinary projects in astronomy. Sen’s research emphasizes distribution-free testing, high-dimensional models, and computational methods. Education: Ph.D. in Statistics, University of Michigan, Ann Arbor (2008) M.Stat., Indian Statistical Institute, Kolkata B.Stat., Indian Statistical Institute, Kolkata Research Interests: Nonparametric function estimation Optimal transport applications in statistics Empirical Bayes and multiple testing High-dimensional statistical inference Statistical methods in astronomy Key Contributions: Developed multivariate distribution-free tests using optimal transport Advanced convex regression methods in multidimensions Contributed to nonparametric maximum likelihood estimation in mixture models Explored statistical applications in stellar abundance clustering His work bridges theoretical statistics with practical applications, emphasizing robust and computationally efficient methods. Sen has also contributed to methodological advancements in astronomy through statistical modeling of stellar data.

Jakob Zechمشاهده پروفایل

استاد

• Uncertainty Quantification
• High-Dimensional Approximation
• Sparse-Grid Techniques
• +۴ مورد دیگر

Jakob Zech is a Professor at the Interdisciplinary Center for Scientific Computing (IWR) at Heidelberg University since April 2020. Before this, he held postdoctoral positions at MIT (2019-2020) and ETH Zürich (2018-2019). He earned his PhD in Mathematics from ETH Zürich in 2018, focusing on Sparse-Grid Approximation of High-Dimensional Parametric PDEs, followed by a Master’s (2014) and Bachelor’s (2012) in Applied Mathematics from ETH Zürich and TU Wien, respectively. Research Interests : Zech’s work bridges Uncertainty Quantification (UQ), high-dimensional approximation, and computational mathematics. Key areas include sparse-grid techniques, neural networks, transport methods, Bayesian inverse problems, and the theoretical foundations of deep learning. His research emphasizes developing algorithms for stochastic modeling and analyzing their mathematical properties. Teaching : He has taught advanced courses such as High Dimensional Approximation, Theory of Deep Learning, and Numerical Methods for Bayesian Inverse Problems at Heidelberg University. He also served as a teaching assistant for numerous courses at ETH Zürich, covering numerical analysis, partial differential equations, and linear algebra. Publications : His recent work explores quantum computing applications in polynomial chaos expansions, statistical learning theory for neural operators, and multilevel optimization strategies. His articles reflect a strong focus on combining classical numerical methods with modern machine learning techniques. Labs/Teams : While no specific lab is mentioned, his research group is active in computational UQ and deep learning, collaborating internationally with institutions like MIT and ETH Zürich.

Damek Davisمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Damek Davis is an Assistant Professor in the Department of Mathematics at Cornell University, affiliated with both the College of Engineering and the College of Arts and Sciences. He holds a Ph.D. in Mathematics from the University of California, Los Angeles (2015). His research focuses on developing and analyzing optimization algorithms for large-scale, nonconvex, and nonsmooth problems arising in machine learning and signal processing. He emphasizes theoretical guarantees on algorithm performance and practical implementations that leverage modern computing architectures. His work spans stochastic optimization, subgradient methods, and convergence analysis, with applications to computational microscopy, phase retrieval, and decentralized systems. Recent contributions include advancements in adaptive stepsize methods, sharpness-aware optimization, and global optimality conditions for mixture models. Davis collaborates on theoretical foundations and algorithmic innovations in high-dimensional statistics, nonlinear optimization, and parallel computing. His scholarly output includes over 50 peer-reviewed articles, with recent highlights in top venues such as SIAM Journal on Optimization, Mathematical Programming, and IEEE Transactions on Signal Processing. His research is supported by grants from NSF and AFOSR, focusing on topics like robust statistical estimation and algorithmic scalability.

Melody Ghahramaniمشاهده پروفایل

استاد

• Time Series Analysis
• Biostatistics
• Environmetrics
• +۳ مورد دیگر

Melody Ghahramani is a Professor in the Department of Mathematics and Statistics at the University of Winnipeg. Her research focuses on time series analysis, environmetrics, and biostatistics with applications in finance and infectious disease modeling. She holds a Ph.D. in Statistics from the University of Manitoba and has held academic positions in Winnipeg since completing her postgraduate studies. Education: Ph.D. in Statistics, University of Manitoba M.Sc. in Statistics, Simon Fraser University Bachelor's Degree in Mathematics and Statistics, University of Manitoba Research interests emphasize statistical methodologies for zero-inflated and overdispersed data, environmental modeling of temporary streams in Canadian prairies, and biostatistical applications in healthcare and epidemiology. Her work bridges theoretical statistics with practical applications in finance and ecological systems. Publications highlight advancements in time series regression, volatility modeling, and cryptocurrency trading strategies. She has contributed to statistical frameworks for analyzing count data and financial market dynamics. Her applied research includes modeling long-term survival outcomes in healthcare and environmental water resource systems. No formal awards are listed, though her extensive publication record reflects sustained academic contribution. She advises courses in statistics and supervises student research in mathematical and statistical methodologies. Contact details: Office 6L25, Lockhart Hall, reachable at m.ghahramani@uwinnipeg.ca or 204.789.1414.

Patricia Moodieمشاهده پروفایل

عضو هیئت علمی

• Biostatistics
• Epidemiology
• Survival Analysis
• +۳ مورد دیگر

Patricia Moodie is a Research Scholar in the Department of Mathematics and Statistics at the University of Winnipeg since 2003. Previously, she held roles as a biostatistician at the Manitoba Cancer Treatment and Research Foundation and Head of Biostatistics in the Computer Department for Health Sciences at the University of Manitoba Faculty of Medicine. She also served as an adjunct lecturer in Community Health Sciences at the University of Manitoba. Education: B.Sc. (Honours Biology) from Memorial University of Newfoundland (1980), M.Sc. (Zoology) from the University of Alberta (1982), and M.S. (Biostatistics) from the University of Illinois at Chicago (1986). Research interests span biostatistical methods, clinical trial design, survival analysis, and ecological studies focusing on fish behavior and morphology. She has contributed to applications of statistical software (e.g., SAS) in healthcare and developed diagnostic models for emergency medicine. Her work intersects medical statistics, epidemiology, and computational biology. Publications reflect expertise in survival analysis, meta-analyses, and fish ecology. Notable contributions include studies on breast cancer survival, asymmetric stickleback behavior, and Bayesian diagnostic systems for acute abdominal pain. Advising and grants: Led biostatistical teams in health sciences, contributed to interdisciplinary research projects, and advised on clinical trial methodologies. No specific grants or awards explicitly listed. Labs/Teams: Active in the University of Winnipeg’s Mathematics and Statistics department, collaborating with healthcare institutions on biostatistical applications and ecological research.

Peter Adamicمشاهده پروفایل

دانشیار

• Actuarial Science
• Statistics
• Biostatistics
• +۳ مورد دیگر

Dr. Peter Adamic is an Associate Professor of Mathematics and Director of the Actuarial Science Minor at Ave Maria University, Florida. Previously, he held a tenured position at Laurentian University from 2007 to 2023. He earned his B.Math in Actuarial Science from the University of Waterloo (1999), followed by an M.Sc. and Ph.D. in Statistics from the University of Guelph (2004, 2007). Education: B.Math (Actuarial Science), University of Waterloo M.Sc. (Statistics), University of Guelph Ph.D. (Statistics), University of Guelph Research Interests: Dr. Adamic specializes in actuarial science, statistical modeling of survival data, competing risks analysis, and applications in biostatistics. His work bridges theoretical statistics with practical actuarial and biomedical problems, focusing on methods like cure models, nonparametric techniques, and kernel-based approaches. Publications: His research appears in journals such as Scandinavian Actuarial Journal , Metrika , and Biostatistics , emphasizing actuarial risk modeling, survival analysis, and sports injury epidemiology. Recent trends include advancements in cure models and generalized additive methods for aggregate claims. Awards and Grants: Dr. Adamic holds certifications as an Associate of the Canadian Institute of Actuaries (ACIA) and a Professional Statistician (P.Stat.). He secured research grants from the Natural Sciences and Engineering Research Council of Canada (NSERC). Advising and Grants: Supervised numerous Master’s students and two post-doctoral fellows. Refereed for journals such as Lifetime Data Analysis and Entropy . Recipient of NSERC grants for research in competing risks and statistical methodology. Labs/Teams: No specific lab mentioned, but collaborates across disciplines in actuarial science and biostatistics.