جست‌وجوی استاد و پژوهشگر

Susan E. Clarkمشاهده پروفایل

استادیار

• Cosmic Magnetic Fields
• Interstellar Medium
• Astrophysical Magnetic Fields
• +۶ مورد دیگر

Susan E. Clark is an Assistant Professor of Physics at Stanford University, where she investigates cosmic magnetic fields, magnetohydrodynamic processes, and the interstellar medium (ISM) through observation, simulation, and analytic theory. Prior to Stanford, she was a NASA Hubble Fellow and postdoctoral member at the Institute for Advanced Study in Princeton, New Jersey, after earning her Ph.D. in Astrophysics from Columbia University (2017) and B.S. in Physics from the University of North Carolina at Chapel Hill (2012), supported by a Morehead-Cain scholarship. Education: Ph.D., Astrophysics, Columbia University (2017) — NSF Graduate Fellow B.S., Physics, University of North Carolina at Chapel Hill (2012) — Morehead-Cain scholarship Her research focuses on Galactic and extragalactic magnetism, interstellar turbulence, star formation, and polarized cosmological foregrounds. She leads an interdisciplinary group tackling these problems via observational data, numerical simulations, and machine learning techniques. Current projects include characterizing magnetic field alignment, modeling 3D ISM structure, and analyzing data from experiments like the Atacama Cosmology Telescope, Simons Observatory, CMB-S4, CCAT-Prime, LiteBIRD, and the Galactic Australian SKA Pathfinder (GASKAP). Recent publications highlight her work on dust filament misalignment, HI morphology for gas phase separation, equipartition magnetic field estimation, and tomographic MHD simulations of galactic magnetic fields. These studies integrate astrophysics, computational methods, and observational astronomy to decode the universe's magnetic structure and dynamics. Scientific Awards: Alfred P. Sloan Research Fellowship NSF Graduate Fellowship NASA Hubble Fellowship Clark co-founded the Pan-Experiment Galactic Science Group and co-directs Stanford's Center for Decoding the Universe, fostering interdisciplinary collaborations. Her lab includes postdocs, graduate students, and undergraduates, with alumni transitioning to roles in academia and industry. Funding comes from NSF, NASA, and the Sloan Foundation.

Rajen Shahمشاهده پروفایل

استاد

• Statistical Inference
• High-Dimensional Data Analysis
• Machine Learning
• +۳ مورد دیگر

Professor Rajen Shah is a faculty member in the Statistical Laboratory at the University of Cambridge, part of the Department of Pure Mathematics and Mathematical Statistics (DPMMS) within the Faculty of Mathematics. His research focuses on statistical methodology, particularly in high-dimensional data analysis, machine learning, and robust statistical inference. He is known for contributions to areas such as change-point regression, inverse propensity score weighting, and efficient estimation techniques in complex models. Key research interests include developing novel methods for variable selection, robust hypothesis testing, and scalable algorithms for large-scale data. He has collaborated on interdisciplinary projects, such as functional genomics studies (e.g., screening conserved genes of unknown function). His work often emphasizes theoretical rigor alongside practical applications in fields like causal inference and computational statistics. Prof. Shah has published extensively in top-tier journals like The Annals of Statistics , Bernoulli , and Journal of the Royal Statistical Society Series B . His recent articles address challenges in cross-validation for change-point detection, rank-transformed subsampling, and sandwich boosting methods. He is actively involved in the academic community, contributing to the Cambridge Statistics Clinic and supervising research in statistical methodology. His research group is affiliated with the Statistical Laboratory at the Centre for Mathematical Sciences, Cambridge. The lab focuses on advancing statistical theory and applications, with a strong emphasis on high-dimensional and assumption-lean methods.

Linan Chenمشاهده پروفایل

دانشیار

• Probability Theory
• Analysis
• Geometry
• +۴ مورد دیگر

Linan Chen is an Associate Professor in the Department of Mathematics and Statistics at McGill University since 2014, following a postdoctoral position at the same institution (2011–2014). He holds a Ph.D. from MIT (2011, supervised by Daniel Stroock) and a B.Sc. from Tsinghua University (2006). His research focuses on probability theory and its intersections with analysis and geometry, including partial differential equations, functional analysis, Gaussian measures, and random geometry. He is affiliated with the Probability Lab of the Centre de Recherches Mathématiques (CRM) and the CNRS-Unite Mixte Internationale (CNRS-UMI) since 2014. Chen teaches advanced probability courses such as Honours Probability (Math 356) and Advanced Probability Theory I/II (Math 587/589), alongside special topics courses like Topics in Geometry and Topology (Math 599). He has advised students including Leila Sloman, Reinhold Willcox, Ulysse Blau, and Olivier Nadeau-Chamard through independent study programs. His research explores cutting-edge topics in probability, including Gaussian free fields, degenerate diffusion equations, and asymptotic properties of geometric stochastic structures. Recent work addresses high-dimensional phenomena, stochastic processes in geometry, and applications in mathematical physics. Chen’s contributions span theoretical advancements and methodological innovations, with publications in journals such as the Journal of Theoretical Probability, Annales Henri Poincaré, and SIAM Journal on Mathematical Analysis.

Antonio Peyracheمشاهده پروفایل

دانشیار

• Applied Economics
• Econometrics
• Productivity Analysis
• +۳ مورد دیگر

Associate Professor Antonio Peyrache is a Deputy Head of School at the School of Economics, University of Queensland, within the Faculty of Business, Economics and Law. His research focuses on applied economics, econometrics, and productivity/efficiency analysis with particular emphasis on production systems, public sector efficiency, and systemic risk modeling. He leads the Centre for Efficiency and Productivity Analysis (CEPA), driving advancements in efficiency measurement methodologies. Key research interests include judicial system efficiency, banking systemic risk, and multilevel production networks. Recent work explores homothetic production technologies and optimal organizational structures for public institutions. Featured projects include analyzing productivity in Australian horticulture and European judicial systems. His expertise spans both theoretical contributions (e.g., decomposition frameworks) and applied policy analysis (e.g., healthcare delivery efficiency). Publications span journals like European Journal of Operational Research and Omega, with a focus on operational research techniques and their policy applications. He has directed projects funded by the Asian Productivity Organization and collaborated on EU-KLEMS growth accounting initiatives. Professional roles include editorial contributions and leadership in academic productivity analysis.

Zheng (Tracy) Keمشاهده پروفایل

دانشیار

• High-dimensional statistics
• Machine learning
• Social network analysis
• +۳ مورد دیگر

Zheng (Tracy) Ke is an Associate Professor of Statistics at Harvard University. She holds a Ph.D. from Princeton University (2014) and a B.S. from Tsinghua University (2009). Her research focuses on high-dimensional statistics, machine learning, social network analysis, text mining, and bioinformatics. Notable contributions include advancements in network data analysis (e.g., SCORE normalization), text analysis methodologies, and statistical genetics pipelines. Dr. Ke has received prestigious awards such as the COPSS Emerging Leader Award (2024) and the Sloan Research Fellowship (2023). She has organized major conferences like the Workshop on Statistical Network Analysis and Beyond (2024) and contributed to the MADStat dataset analyzing statisticians' co-authorship networks. Her research interests span theoretical and applied domains, with a focus on developing scalable algorithms and rigorous statistical frameworks for complex data. Recent work emphasizes challenges like severe degree heterogeneity in networks and rare/weak signal detection in high-dimensional settings. Dr. Ke is an Associate Editor for the Journal of the American Statistical Association and actively collaborates on interdisciplinary projects.

Chun Wangمشاهده پروفایل

استاد

• Quantitative Research Methods
• Item Response Theory (IRT)
• Computerized Adaptive Testing (CAT)
• +۳ مورد دیگر

Chun Wang is a Professor in the Department of Measurement & Statistics at the University of Washington's College of Education. His research focuses on advancing quantitative methods in educational and psychological measurement, with expertise in item response theory (IRT), computerized adaptive testing (CAT), and cognitive diagnostic modeling. He holds affiliate faculty status at the Center for Statistics and the Social Sciences. Education: B.S. in Psychology, Peking University (China) M.S. and Ph.D. in Quantitative Psychology, University of Illinois at Urbana-Champaign Research Interests: Development and validation of multidimensional/mixture IRT models Computerized adaptive testing optimization Cognitive diagnostic modeling for classroom applications Health measurement and bias detection in assessments Recent Trends in Articles: His work bridges statistical innovation with practical applications, emphasizing fairness and efficiency in assessments. Notable areas include: - Healthcare : Predictive models for discharge disposition and functional outcomes - Education Technology : Adaptive learning systems and diagnostic feedback mechanisms - Methodology : Bias detection (DIF), Bayesian estimation techniques, and computational efficiency Scientific Awards : Includes the Anne Anastasi Award (2020), McKnight Presidential Fellowship (2017), and multiple best reviewer recognitions from leading psychometrics journals. Advising & Grants: Supervised students including Xiao J., Zhu R., and Lu J.* in high-impact projects. Co-led a $10M NIH grant (AmplifyGAIN Center) to advance Gen AI in STEM education. Published extensively in Psychometrika , Journal of Educational and Behavioral Statistics , and other top outlets. Labs/Teams: Directs the Pmetrics Lab ( https://sites.uw.edu/pmetrics/ ), collaborating on cutting-edge measurement tools for education and healthcare.

• Composite Materials
• Structural Engineering
• Machine Learning Applications
• +۳ مورد دیگر

Dr. Thuc Vo is an Associate Professor in Civil Engineering at La Trobe University, Australia. His expertise lies in structural engineering, composite materials, and machine learning applications. He previously held roles at Northumbria University (UK) and Airbus’ Advanced Composite Training and Development Centre. His research focuses on shear deformation theories for composite structures and machine learning for structural engineering. He has authored over 120 publications in prestigious journals and conferences. Education & Experience: Associate Professor, La Trobe University (2019–present) Senior Lecturer & Program Leader in Civil Engineering, Northumbria University (2013–2019) Lecturer at Airbus’ Advanced Composite Training and Development Centre/Wrexham Glyndwr University (2011–2013) Research Associate, University of Liverpool (2010–2011) Research Interests: Composite material analysis (FGMs, nanoporous materials) Machine learning for structural prediction Vibration and buckling analysis Advanced beam and plate theories Collaboration & Supervision: Offers supervision for masters/PhD students and collaborates on industry projects. His work bridges theoretical mechanics with data-driven approaches, addressing challenges in smart materials and structural optimization.

Naratip Santitissadeekornمشاهده پروفایل

مدرس ارشد

• Data Assimilation
• Inverse Problems
• Geophysical Fluid Dynamics
• +۵ مورد دیگر

Naratip Santitissadeekorn is a Senior Lecturer in Data Assimilation at the School of Mathematics and Physics, University of Surrey, where he is affiliated with the Mathematics at the Interface Group. His work bridges mathematics, data science, and real-world applications in urban planning, crime analysis, and geophysical fluid dynamics. Dr. Santitissadeekorn received his PhD from Clarkson University in 2008, with a dissertation titled "Transport Analysis and Motion Estimation of Dynamical Systems of Time-Series data." His doctoral research was supervised by Professor Erik Bollt. Following his PhD, he completed two significant postdoctoral positions: from 2008-2011 at the University of New South Wales, Sydney, Australia, working with Professor Gary Froyland on numerical techniques for finite-time Lagrangian coherent set identification, with applications to delimiting the polar vortex and Agulhas rings; and from 2011-2014 at the University of North Carolina-Chapel Hill, working with Professor Chris Jones on data assimilation projects. Dr. Santitissadeekorn's research focuses on inverse problems and data assimilation in geophysical fluid dynamics, the applications of Lagrangian Coherent Structures (LCS), and computational ergodic theory. His work combines theoretical mathematics with practical applications, particularly in urban growth modeling and crime analysis. He has developed innovative methods for identifying coherent structures in fluid flows, estimating transition probabilities from spatiotemporal data, and creating data-driven frameworks for urban expansion scenarios. His research demonstrates how mathematical techniques can be applied to solve real-world problems in environmental science, urban planning, and public safety. An analysis of Dr. Santitissadeekorn's recent publications (2020-2023) reveals a strong focus on urban expansion modeling and network analysis. His work on urban growth has evolved from basic cellular automata models to sophisticated frameworks that manage uncertainty through parameter clustering and growth mode identification. His research on Hawkes processes has advanced ensemble-based filtering techniques for analyzing count data in large networks. These publications demonstrate a consistent pattern of applying mathematical rigor to complex spatiotemporal phenomena, with increasing emphasis on data-driven approaches and practical applications. Dr. Santitissadeekorn has made significant contributions to data assimilation methods, particularly through the development of the extended Poisson-Kalman filter (ExPKF) for urban crime modeling. His teaching includes courses in Algebra and Bayesian Statistics, reflecting his expertise in both theoretical and applied mathematics. While specific awards are not mentioned in the available information, his extensive publication record in high-impact journals demonstrates recognition within his field. Dr. Santitissadeekorn's research has practical implications for urban planning and law enforcement. His work on urban expansion models helps planners understand different growth trajectories, while his crime modeling research contributes to improved police patrolling strategies. His interdisciplinary approach, combining mathematics, computer science, and domain-specific knowledge, positions him at the forefront of applying data science to societal challenges.

Tony Smithمشاهده پروفایل

استاد

• Macroeconomics
• Environmental Economics
• Econometrics
• +۳ مورد دیگر

Tony Smith is the William K. Lanman, Jr. Professor of Economics at Yale University, where he previously served as Chair of the Department of Economics from 2019 to 2025. He holds a B.S. in Economics from MIT (1984) and a Ph.D. in Economics from Duke University (1990). He has also taught at Queen's University, Carnegie Mellon University, and the University of Rochester. As a Research Associate at the National Bureau of Economic Research, his research focuses on macroeconomics, particularly income and wealth heterogeneity, and econometrics, including simulation estimation of structural models. His recent work integrates macroeconomics with environmental economics, developing high-resolution global economy-climate models. He currently serves as Co-Editor of Macroeconomic Dynamics and has been an Associate Editor at Review of Economics Dynamics . His scholarly contributions emphasize computational methods and interdisciplinary approaches to economic challenges.

• Stochastic Analysis
• Rough Path Theory
• Mathematical Finance
• +۳ مورد دیگر

Peter K. Friz is an Einstein Professor in Mathematics at TU-Berlin, affiliated with the Institute of Mathematics, and associated with the Weierstrass Institute for Applied Analysis and Stochastics. His research focuses on stochastic analysis, rough path theory, and mathematical finance, with particular emphasis on volatility modeling and applications to quantitative finance. He has held prestigious grants, including ERC Starting and Consolidator Grants, and coordinates the DFG research unit 'Rough paths, stochastic partial differential equations, and related topics.' Friz's work bridges theoretical stochastic analysis and practical financial applications, emphasizing rough path theory and its implications for differential equations and stochastic processes. His collaborations include organizing international conferences and courses on rough paths, with invited lectures at institutions like Cambridge, Paris, and Bonn. Supported by DFG, the European Research Council, and the Einstein Foundation, his research explores geometric aspects of pathwise analysis and stochastic volatility dynamics. He has advised numerous PhD students and maintains active roles in academic administration, including coordinating Berlin Mathematical School programs and teaching advanced topics in stochastic calculus. His contributions to rough path theory and stochastic finance are recognized through his academic leadership and influential publications, including co-authoring the seminal book Multidimensional Stochastic Processes as Rough Paths .

Tom Bootمشاهده پروفایل

دانشیار

• Macroeconomic Forecasting
• High-Dimensional Data Analysis
• Econometric Theory
• +۴ مورد دیگر

Dr. Tom Boot is an Associate Professor at the Department of Economics, Econometrics & Finance at the University of Groningen. He holds a PhD in Econometrics from Erasmus University Rotterdam (2017) and an MSc in Econometrics from the same institution (2012), along with an MSc in Physics from the University of Groningen (2010). His research focuses on econometric theory applied to macroeconomic forecasting, high-dimensional data analysis, and causal inference. He has been recognized with the Veni grant (2021–2024) for his work on forecasting methodologies. Boot’s research interests include improving forecast accuracy through methods like subspace projections, structural break modeling, and privacy-aware marketing analytics. His recent work explores privacy-utility trade-offs in data-driven marketing and unbiased estimation techniques for clustered errors. He has supervised PhD students including Jhordano Aguilar Loyo and Gilian Ponte, whose theses addressed panel data heterogeneity and differential privacy applications. Boot is also a program director for the MSc Econometrics, Operations Research, and Actuarial Studies (since 2024). His contributions to econometrics span over a dozen peer-reviewed publications, with a focus on advanced statistical techniques for economic forecasting and policy analysis. Collaborations include work with institutions like Harvard/MIT and the organization of workshops on causal inference and machine learning.

• Econometrics
• Bayesian Statistics
• Quantitative Marketing
• +۲ مورد دیگر

Kenichi Shimizu is an Assistant Professor in Econometrics (tenure-track) at the Department of Economics, University of Alberta. He holds a PhD in Economics from Brown University (2021) and previously worked at the Adam Smith Business School, University of Glasgow. His research focuses on Bayesian econometrics, quantitative marketing, industrial organization, and time-series analysis. He teaches courses such as Introductory Econometrics (ECON 399) and Applied Econometrics (ECON 599). Education: PhD in Economics from Brown University (2021). Professional affiliations include roles at the University of Alberta and University of Glasgow. His work emphasizes methodological advancements in econometrics with applications to marketing and industrial organization. Research trends in his publications highlight Bayesian methodologies for dynamic modeling, structural breaks, and high-dimensional data. Key topics include semiparametric estimation, sparse models, and policy evaluation frameworks. Grants: Recipient of SSHRC Insight Development Grant (2024-2026) for research on Bayesian econometric methods in industrial organization and marketing. Active presenter at major conferences including the NBER-NSF Seminar, Canadian Economic Association meetings, and the World Congress of the Econometric Society. Teaching responsibilities include undergraduate and graduate econometrics courses with emphasis on applied regression methods and model specification.

Martin Norgrenمشاهده پروفایل

استاد

• Electromagnetic Inverse Problems
• Material Characterization
• Biomedical Imaging
• +۳ مورد دیگر

Martin Norgren is a Professor at KTH Royal Institute of Technology, leading the Department of Electromagnetic Fusion Physics. His research focuses on electromagnetic inverse problems, including material characterization, biomedical imaging (e.g., brain current sources), environmental monitoring (e.g., snow and avalanche prediction), and smart grid technologies. He specializes in reconstructing object properties using electromagnetic measurements and has contributed to applications in healthcare, energy systems, and environmental science. His work involves advanced analytical and numerical methods such as mode-matching techniques, perturbation theory, and convex optimization. Notable projects include noncontact current measurement in power grids and transformer diagnostics using microwave radiation. Norgren teaches courses in electromagnetic field theory and electrical engineering design, emphasizing practical applications and interdisciplinary collaboration. Recent research trends highlight advancements in glide/twist symmetry-based metamaterial design, waveguide analysis, and inverse scattering techniques. His studies bridge fundamental physics with applied engineering, addressing challenges in energy infrastructure and medical diagnostics. As a department head, he oversees educational and research programs at KTH, fostering innovation in electromagnetism and fusion physics. His contributions to curriculum development include project-based courses integrating theory and hands-on design.

Samson Zhouمشاهده پروفایل

استادیار

• Algorithms and theory
• Data science
• Machine learning
• +۷ مورد دیگر

Samson Zhou is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Purdue University and dual B.S. and M.Eng. degrees from MIT in Computer Science and Mathematics. His research focuses on theoretical computer science, data science, and machine learning, with specializations in numerical linear algebra, streaming algorithms, and differential privacy. He has held postdoctoral positions at institutions including Carnegie Mellon University and Rice University. Education: Ph.D., Computer Science, Purdue University (2018) M.Eng., Computer Science, MIT (2011) B.S., Computer Science & Mathematics, MIT (2010-2011) Research Interests: Dr. Zhou explores intersections of algorithms, data science, and machine learning, emphasizing numerical linear algebra, streaming algorithms, differential privacy, and adversarial robustness. He designs efficient algorithms for large-scale data processing with provable guarantees. Key Contributions: His work includes advancements in sliding window clustering, adversarially robust streaming algorithms, and privacy-preserving techniques. Notable achievements include the Silver Best Paper Award at ICML 2021 and spotlight presentations at ICLR 2025. Service & Leadership: He organizes workshops (e.g., TTIC 2024 on Learning-Augmented Algorithms) and serves on program committees for SOSA, NeurIPS, and COLT. He also co-leads the TAMU Math Circle's problem-solving sessions and the Algorithms & Data Science Reading Group. Labs/Teams: He collaborates on projects involving co-hosted postdocs (e.g., Chen Wang) and masters students (e.g., Shenghao Xie), focusing on topics like coresets, streaming algorithms, and fair clustering.

Suhasini Subba Raoمشاهده پروفایل

استاد

• Time Series Analysis
• Nonstationary Processes
• Nonlinear Processes
• +۳ مورد دیگر

Suhasini Subba Rao is a Professor of Statistics at Texas A&M University, affiliated with the Department of Statistics within the College of Arts & Sciences. Her research focuses on time series analysis, nonstationary processes, nonlinear processes, and spatio-temporal models. She holds a prominent academic position and contributes to both theoretical and applied statistical methodologies. Education details are not explicitly provided in the text, but her academic career includes extensive work in statistical theory and methodology. Her research interests emphasize developing robust statistical techniques for analyzing nonstationary and complex time series data, with applications in spatio-temporal modeling and recursive algorithms. Her recent articles highlight advancements in inverse covariance estimation, graphical models for nonstationary time series, and spectral methods for small sample data. She has also explored reconciliation of Gaussian and Whittle likelihoods, enhancing estimation accuracy in frequency domains. No scientific awards are explicitly mentioned. Her advising and grants are not detailed, but her prolific publication record suggests active research collaborations and funding. She maintains a lab/team focused on time series analysis, though specific team names are not provided.