جست‌وجوی استاد و پژوهشگر

Azizur Rahmanمشاهده پروفایل

استاد

• Data Science
• AI and Machine Learning
• Statistics
• +۷ مورد دیگر

Professor Azizur Rahman is a leading academic in applied statistics and data science at Charles Sturt University (Australia), where he leads the Data Mining Research Group and directs the Data Analytics Lab. He holds a PhD in Economics and Applied Statistics from the University of Canberra, with postdoctoral experience at the University of Adelaide. His expertise spans statistical methodologies, microsimulation, AI/ML, and data-driven policy analysis. He has attracted over $4.03M in research funding and published over 200 scholarly works. Education: BSc (Honours) in Statistical Science, University of Chittagong (2000) MSc (Thesis) in Biostatistics, University of Chittagong (2003) PhD in Economics and Applied Statistics, University of Canberra (2010) Research Focus: Big data analytics and AI applications in health, environment, and policy Bayesian inference and multilevel modeling Spatial microsimulation for socioeconomic analysis Water quality modeling and environmental systems Public health challenges in developing nations Key Contributions: Developed novel computational methods for microsimulation modeling Founder of the Data Analytics Lab at CSU Editorial roles in journals like PLOS ONE and International Journal of Microsimulation Recipient of 2023 ANZRSAI Outstanding Service Award and Charles Sturt Excellence Awards Grants & Awards: $4.03M+ external research funding secured 30+ awards including travel grants, fellowships, and service recognitions Advising & Mentorship: Supervised 6 PhD/DIT students and co-supervised many more. Active in mentoring early-career researchers through editorial boards and conference organizing roles. Labs & Collaborations: Leads DaMRG (Data Mining Research Group) and collaborates with global institutions on interdisciplinary projects involving AI/ML, environmental science, and public health.

Harrison Huibin Zhouمشاهده پروفایل

استاد

• High-Dimensional Data Analysis
• Nonparametric Estimation
• Statistical Decision Theory
• +۳ مورد دیگر

Harrison Huibin Zhou is the Henry Ford II Professor of Statistics and Data Science at Yale University. He has held leadership roles, including Department Chair of Statistics and Data Science (2018–present) and former Chair of Statistics (2012–2017). His academic career at Yale spans over two decades, with promotions from Assistant Professor (2004–2009) to Associate (2009–2010) and full Professor (2010–present). Research Interests: Dr. Zhou specializes in high-dimensional statistical theory, including nonparametric estimation, minimax theory, and applications in network analysis, machine learning, and functional data analysis. His work bridges theoretical foundations with computational methods, addressing challenges in modern statistical decision-making. Publications: His recent work focuses on spectral clustering, quantum state tomography, and optimal estimation in high-dimensional models. Notable contributions include theoretical guarantees for algorithms like the EM method in Gaussian mixtures and advancements in community detection in networks. Teaching: He teaches advanced courses such as Functional Data Analysis, Nonparametric Estimation, and Decision Theory, reflecting his expertise in statistical methodology and theory. Professional Service: Organized workshops on topics like Empirical Processes (2015) and High-Dimensional Data (2012), underscoring his role in fostering academic collaboration.

Deborah Talmiمشاهده پروفایل

دانشیار

• Emotional Cognition
• Memory Mechanisms
• Neurobiology of Emotion
• +۷ مورد دیگر

Dr. Deborah Talmi is a University Associate Professor in the Department of Psychology and a Fellow of Lucy Cavendish College at the University of Cambridge. Her research focuses on the cognitive, computational, and neurobiological mechanisms underlying emotional processes, particularly their intersection with memory, perception, and attention. She leads the Emotional Cognition Lab, which employs behavioral experiments, EEG, fMRI, and computational modeling to explore how emotions influence memory formation and retrieval. Roles: Associate Professor (Department of Psychology), Fellow (Lucy Cavendish College), Principal Investigator (Emotional Cognition Lab) Affiliations: School of Biological Sciences, Cambridge Neuroscience Her research investigates questions such as why emotional experiences feel a certain way, how emotion shapes memory, and the neural basis of similarity perception. A recent study demonstrated that emotionally charged stimuli evoke more similar neural representations than neutral ones, even when controlling for thematic content. This work was published in the Journal of Neuroscience and is part of broader efforts to model emotional memory using frameworks like the Bayesian Brain hypothesis. Dr. Talmi collaborates with institutions like the Swiss Centre for Affective Sciences and employs interdisciplinary methods, including animal models of emotional memory with Prof. Amy Milton. She actively mentors PhD students and welcomes volunteers through the SONA platform. Her lab’s findings have implications for understanding chronic pain, decision-making, and mental health. Her work has been featured in The Conversation and TEDx, emphasizing public engagement. She is also committed to advancing education, as seen in her reflections on remote teaching during the pandemic.

Milica Miočevićمشاهده پروفایل

دانشیار

• Quantitative Psychology
• Statistical Mediation Analysis
• Bayesian Methods
• +۳ مورد دیگر

Milica Miočević is an Associate Professor in the Department of Psychology at McGill University. She previously held an Assistant Professor position at Utrecht University's Department of Methods and Statistics. Her research focuses on statistical mediation analysis and Bayesian methods applied to social, health, and behavioral sciences, with three main research lines: optimal methods for Bayesian mediation priors, synthesizing mediated effect findings across studies, and mediation analysis in Single Case Experimental Designs (SCEDs). Dr. Miočević earned her Ph.D. from Arizona State University under advisors Dave MacKinnon and Roy Levy. Her work emphasizes methodological innovation in small sample contexts and evidence synthesis. The Miocevic Lab at McGill explores quantitative psychology and modeling techniques. Her recent publications span Bayesian vs frequentist estimation comparisons, prior distribution development, and mediation analysis methodologies. She has contributed to edited volumes on small sample solutions and published extensively in Structural Equation Modeling journals. Dr. Miočević's research bridges statistical theory and applied practice, advancing tools for researchers working with complex data structures and limited sample sizes.

Y. Samuel Wangمشاهده پروفایل

استادیار

• Causal Discovery
• Graphical Models
• High-Dimensional Statistics
• +۳ مورد دیگر

Y. Samuel Wang is an Assistant Professor in the Department of Statistics and Data Science at Cornell University. He holds a PhD in Statistics from the University of Washington and a BS in Applied Mathematics and Economics from Rice University. Prior to academia, he worked as a management consultant and served as a postdoctoral researcher at the University of Chicago’s Booth School of Business. His research focuses on causal discovery, graphical models, mixed membership models, and high-dimensional data analysis, with applications in network science, environmental studies, and healthcare. Education: PhD in Statistics, University of Washington BS in Applied Mathematics & Economics, Rice University Research Interests: Development of interpretable statistical methods for causal inference and graphical model structures High-dimensional data analysis, particularly in non-Gaussian settings Applications in collaborative networks, environmental microbiology, and healthcare outcomes Recent Research Trends: Recent publications emphasize causal discovery under latent confounding, functional graphical models, and gender dynamics in scholarly collaborations. Methodological contributions include robust high-dimensional inference techniques and computational tools for cyclic structural equation models. Professional Activity: Licensed on GitHub, Google Scholar, and ORCID GitHub repositories include projects on causal discovery (highDNG), gender homophily analysis (genderHomophily), and mixed membership models (mixedMem)

Luciano Pomattoمشاهده پروفایل

استاد

• Microeconomic Theory
• Economics of Risk and Uncertainty
• Theories of Information
• +۶ مورد دیگر

Luciano Pomatto is a Professor of Economics at the California Institute of Technology (Caltech) and Executive Officer for the Social Sciences since 2025. He holds affiliations with the Ronald and Maxine Linde Institute of Economic and Management Sciences, the Center for Social Information Sciences (CSIS), and the Center for Theoretical and Experimental Social Sciences (CTESS). Education: B.A. (2007), M.A. (2009) from Universita di Torino; Ph.D. (2015) from Northwestern University. Academic Career: Assistant Professor at Caltech (2016–22), promoted to Professor in 2022. Editorial Roles: Associate Editor at the Journal of Political Economy , Journal of Economic Theory , and Journal of Mathematical Economics . His research focuses on decision-making under uncertainty, risk analysis, information theory, and Bayesian epistemology. Recent work explores stochastic dominance, ambiguity preferences, and the mathematical foundations of forecasting. The 15 most recent publications span Econometrica, American Economic Review, Journal of Economic Theory, and Journal of Political Economy. They reflect interdisciplinary trends bridging microeconomic theory with probability theory, cognitive science, and social decision-making. Scientific Awards: Brass Division Award (2022). Teaching includes courses on Game Theory, Foundations of Economics, and Advanced Economic Theory, with cross-departmental offerings in Social, Cognitive, and Decision Sciences. No student names or grant details are explicitly listed in the provided text.

Michael C. Gurnisمشاهده پروفایل

استاد

• Computational Geodynamics
• Mantle Dynamics
• Plate Tectonics
• +۱۲ مورد دیگر

Michael C. Gurnis is the John E. and Hazel S. Smits Professor of Geophysics at the California Institute of Technology and has served as Director of the Seismological Laboratory (2009-2024) and Schmidt Academy for Software Engineering (2019-). With a B.S. from the University of Arizona (1982) and a Ph.D. from Australian National University (1987), he has held faculty positions at Caltech since 1994, becoming full Professor in 1996 and Smits Professor in 2005. His research spans computational geodynamics, mantle convection, and plate tectonics. California Institute of Technology Division of Geological and Planetary Sciences Seismological Laboratory Schmidt Academy for Software Engineering Research Interests: Gurnis focuses on computational geodynamics to link mantle convection to surface tectonics, using forward/inverse models to study plate motion dynamics , subduction zone mechanics , and deep mantle structures . His work bridges present-day geophysics with historical models through 4D Earth modeling , integrating seismic tomography, mineral physics, and paleogeography via the GPlates consortium. Recent projects include dynamic emergence of continents , plume interactions , and impact-driven subduction . Publications: Gurnis' research spans high-resolution global models of mantle flow, subduction zone rheology, and cratonic basin dynamics. Key trends include multi-physics coupling , Bayesian inversion techniques , and planetary-scale tectonic drivers . His 2024 papers address nonlinear mantle viscosity , shear zone weakening , and impact origins of tectonics . Scientific Awards: Gordon Bell Prize Finalist (2008, 2010) Springer CSE Prize (2011) for plate tectonics simulation work Advising: Mentors graduate students in computational geodynamics, including Jiaqi Fang, Erin Hightower, Yida Li, and Ojashvi Rautela. Collaborates with postdocs and international institutions like ETH-Zurich and National University of Mexico on mantle wedge dynamics and seismic geography. Labs & Teams: Leads the Seismological Laboratory and co-founded the Schmidt Academy for Software Engineering , driving innovations in geodynamic simulations and computational tools.

Jean-Philippe Avouacمشاهده پروفایل

استاد

• Geomechanics
• Seismology
• Geodesy
• +۵ مورد دیگر

Jean-Philippe Avouac is the Earle C. Anthony Professor of Geology and Mechanical and Civil Engineering at the California Institute of Technology (Caltech). He is also the Associate Director of the Center for Autonomous Systems and Technologies and the former Director of the Tectonic Observatory (2004–2013). His academic career includes roles at the University of Cambridge (2018–2021) and leadership in geomechanics research. Avouac holds a M.E. from École Polytechnique (1987), a Ph.D. from Institut de Physique du Globe de Paris (1991), and a Habilitation (1992). His research focuses on crustal deformation, earthquake mechanics, and geomorphic processes, using field observations, geodetic measurements, and remote sensing. He has authored over 200 peer-reviewed publications and pioneered geodetic imaging techniques. Key research areas include seismicity forecasting, fault dynamics, and subsurface engineering impacts. Awards include the AGU Fellow distinction and the Wolfson Merit Award. Avouac advises numerous PhD students and postdocs, directing labs like the Center for Geomechanics and Mitigation of Geohazards. His work spans tectonic processes in the Himalayas, induced seismicity, and planetary surface dynamics.

Elliot J. Crowleyمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Automated Machine Learning
• Low-Resource Deep Learning
• +۳ مورد دیگر

Dr Elliot J. Crowley is a Senior Lecturer in Electronics and Electrical Engineering at the University of Edinburgh, serving as Discipline Programme Manager. He co-leads the Bayesian and Neural Systems research group. His research focuses on simplifying machine learning, automated ML, low-resource deep learning, and engineering applications. He holds an MEng in Engineering Science and a DPhil (PhD) from the University of Oxford, with postdoctoral experience at Edinburgh's School of Informatics. He leads the EPSRC New Investigator Award and participates in the dAIEdge Horizon Network. Notable contributions include foundational work in neural architecture search (NAS), probabilistic methods for model efficiency, and applications in computer vision. His courses, such as the Data Analysis and Machine Learning module, emphasize practical Python-based learning for engineering students. Key awards include an EPSRC grant and recognition through distinguished papers at ASPLOS 2021. His team includes current PhD students (Linus Ericsson, Miguel Espinosa) and former advisees (Chenhongyi Yang at Meta, Jack Turner at Qualcomm). Research spans from NAS algorithms to ethical machine learning practices, with a focus on bridging theoretical advances and real-world engineering challenges.

• Sparse signal processing
• Signal processing for machine learning
• Wireless communications
• +۴ مورد دیگر

Dr. Wei Dai is a Senior Lecturer (Associate Professor) in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. He holds affiliations with the EPSRC Centre for Maths of Precision Healthcare and the Communications and Signal Processing group. His research focuses on sparse signal processing, machine learning applications in signal processing, linear and bilinear inverse problems, wireless communications, and random matrix theory. Notably, he contributed to the first compressive sensing DNA microarray prototype and has a highly cited 2009 paper on compressive sensing reconstruction. Dr. Dai's educational background includes a Ph.D. in Electrical and Computer Engineering from the University of Colorado at Boulder (2007) and postdoctoral research at the University of Illinois at Urbana-Champaign (2007-2010). His work bridges theoretical signal processing with practical applications in sensing, communication systems, and biomedical signal analysis. He leads research initiatives in gridless DOA estimation, robust beamforming, and cortico-muscular coupling analysis using advanced optimization techniques. His research outputs span topics like spectral compressed sensing, Bayesian methods for integrated sensing-communication systems, and dictionary learning for causal discovery. Ongoing work emphasizes low-rank matrix recovery, distributed compressed sensing, and mathematical frameworks for super-resolution localization. Dr. Dai collaborates across disciplines, leveraging signal processing innovations for healthcare technology and next-generation wireless systems.

• Time Series Econometrics
• Financial Econometrics
• Volatility Modeling
• +۳ مورد دیگر

Paolo Santucci de Magistris is a Professor of Econometrics at the Department of Economics and Finance of Luiss University (Rome) since February 2018 and previously served as Head of the Department from 2021 to 2024. He held roles as Associate Professor and Assistant Professor at Aarhus University (Denmark) from 2013 to 2018, with prior postdoctoral research at the University of Padova and CREATES. His education includes a PhD from the University of Pavia and visiting research at Northwestern University’s Kellogg School of Management. His research focuses on time series econometrics, financial econometrics, and energy economics, with emphasis on volatility modeling, liquidity risk, and climate impacts. Notable contributions include work on liquidity coverage in financial markets, cointegration models, and the interplay between wind energy and CO2 emissions. His publications span top journals like the Journal of Financial Economics and Journal of Econometrics , exploring topics ranging from stochastic volatility to climate policy analysis. Ongoing projects include research on option price dynamics, energy market connectedness, and risk-neutral density fitting via the RNDfittool MATLAB application. Prof. Santucci de Magistris collaborates internationally with institutions like CREATES and has contributed to policy-relevant studies on energy transition and financial stability. His methodological innovations include Bayesian econometric techniques and tools for analyzing financial risks embedded in derivatives markets.

Mattias Villaniمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• Computational Methods
• +۳ مورد دیگر

Mattias Villani is Professor of Statistics at Stockholm University, specializing in Bayesian statistics and machine learning. He obtained his PhD in Statistics from Stockholm University in 2000 and has held positions at Sveriges Riksbank and Linköping University. Villani develops computationally efficient Bayesian methods for inference, prediction and decision-making with flexible probabilistic models. Research Interests: His work spans Bayesian computation (MCMC, HMC, variational inference), machine learning (Gaussian processes, mixture models), and applications in neuroimaging, transportation, and econometrics. Research focuses on scalable Bayesian methods for large datasets and complex models. Publication Focus: Recent articles concentrate on Bayesian neuroimaging analysis, transportation network modeling, and efficient MCMC algorithms. Methodological innovations in subsampling techniques for large-scale Bayesian computation represent a significant research trend. Student Advising: Supervises PhD students in statistical methodology development and applications. Current research groups focus on spatiotemporal modeling, locally stationary processes, and neuroimaging statistics.

Anirban Mondalمشاهده پروفایل

دانشیار

• Bayesian Inference
• Markov Chain Monte Carlo Methods
• Spatial Statistics
• +۵ مورد دیگر

Anirban Mondal is an Associate Professor and Director of Graduate Studies at Case Western Reserve University's Department of Mathematics, Applied Mathematics and Statistics, specializing in Bayesian Inference, Markov Chain Monte Carlo Methods, and Uncertainty Quantification. Holding a Ph.D. in Statistics from Texas A&M University, his research spans spatial statistics, inverse problems, and data mining applications across biomedical, materials science, and public health domains. Education: Ph.D. in Statistics, Texas A&M University His recent publications (2022-2024) demonstrate interdisciplinary applications including heart disease prediction via optimized machine learning, additive manufacturing defect analysis, and pandemic transmission modeling. While primarily focused on Bayesian frameworks and computational statistics, his work extends to geomechanics, remote sensing, and environmental risk assessment. Current research explores advanced sampling algorithms, functional data emulation, and multiscale hierarchical modeling for complex systems. Key trends include uncertainty quantification in machine learning systems (2024), Bayesian calibration methods (2023), and pandemic modeling (2022). His work balances methodological innovation with real-world applications in medical diagnostics, materials science, and climate science. Contact: anirban.mondal@case.edu

James E. Aguirreمشاهده پروفایل

دانشیار

• Astronomy
• Cosmology
• Galaxy Formation
• +۳ مورد دیگر

James E. Aguirre is an Associate Professor in the Department of Physics and Astronomy at the University of Pennsylvania. His research focuses on understanding galaxy formation, cosmology, and large-scale structure through advanced instrumentation and observational techniques. He leads projects such as HERA (Hydrogen Epoch of Reionization Array) and TIM (Terahertz Intensity Mapper), dedicated to studying the early universe and distant star-forming galaxies. Aguirre’s work involves cutting-edge millimeter-wave and radio instrumentation design, including Z-Spec, PAPER, and MUSTANG. He has contributed to significant discoveries, such as detecting massive water reservoirs around quasars and determining distances to gravitationally lensed galaxies. Supported by NSF grants, his research bridges observational astronomy with cosmological theory. Education: Ph.D. in Astrophysics (thesis work on TopHat balloon-borne telescope). Teaching: ASTR011 Introduction to Astrophysics I. Current Projects: HERA, TIM, Simons Observatory, and PAPER. Grants: NSF Grant No. 0807990 and others. His research group collaborates on instrumentation like the Bolocam Galactic Plane Survey and explores techniques for mitigating calibration errors and improving signal analysis in radio interferometry. Aguirre’s efforts advance both observational methods and our understanding of cosmic evolution from the epoch of reionization to present-day galaxy formation.

• Mathematical Statistics
• Non-Euclidean Data Analysis
• Algebraic Statistics
• +۳ مورد دیگر

Andrew McCormack is an Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on mathematical statistics, with a particular emphasis on non-Euclidean data analysis, algebraic statistics, and information geometry. His research interests include: Non-Euclidean Data Analysis: Developing methodologies for data on manifolds and metric spaces. Algebraic Statistics: Applying nonlinear algebra to understand statistical models, especially graphical models. Tensor Decompositions: Exploring low-dimensional representations of high-dimensional data using matrices and tensors. Information Geometry: Studying statistical models through geometric lenses, incorporating ideas from optimal transport. Statistical Decision Theory: Investigating foundational approaches to evaluating statistical methods from both classical and Bayesian perspectives. Mccormack's recent work spans robust statistical methods, information geometry in covariance modeling, and the application of algebraic techniques to statistical problems. His contributions often bridge theory and computation, addressing challenges in high-dimensional and geometric data analysis. No scientific awards listed. No advisees or grants mentioned. Labs or teams are not specified in the provided information.