جستجوی استاد راهنمای دانشگاهی

• Paleoclimatology
• Climate Change
• Statistical Geology
• +۴ مورد دیگر

Zeki Bora Ön serves as faculty at Muğla Sıtkı Koçman University within the Faculty of Engineering, Department of Electrical and Electronics Engineering, specializing in Circuits and Systems. His academic profile uniquely bridges engineering disciplines with earth sciences through advanced statistical analysis of paleoclimate data. Education: Bachelor's Degree: Boğaziçi University - Faculty of Science and Literature - Mathematics Department (1996-2009) Master's Degree: Muğla Sıtkı Koçman University - Institute of Natural Sciences - Geological Engineering (2012-2013) Doctorate: Istanbul Technical University - Eurasia Institute of Earth Sciences - Earth System Science (2013-2018) Dr. Ön's research centers on paleoclimatology with specialization in Bayesian statistical methods applied to geological data. His primary focus involves reconstructing past climate conditions in Anatolia and the Eastern Mediterranean using sediment records from lakes including Acıgöl, Gölcük, and Hazar. He has pioneered innovative analytical frameworks, particularly Bayesian change point analysis, to examine significant climatic events such as the 4.2 ka BP event and the Holocene-Anthropocene boundary. His interdisciplinary methodology combines geological evidence with advanced statistical techniques to establish precise chronologies of environmental changes across multiple temporal scales. His publication pattern reveals a clear progression toward increasingly sophisticated statistical modeling of paleoclimate data, with recent work emphasizing Bayesian hierarchical regression approaches. His research spans from the last millennium to multi-million year records, providing critical insights into both historical climate variability and long-term climate patterns across Southwest Asia. Scientific Recognition: Best Thesis Award (2019) Dr. Ön has participated in numerous research initiatives including TÜBİTAK 1002 projects on Quaternary climate changes in Southwest Anatolia, paleohydrology of the Sakarya River, and high-resolution climate records from Lake Gölcük. His scholarly contributions extend to peer review services for leading journals including Journal of Quaternary Science and Quaternary Science Reviews, demonstrating his standing within the international paleoclimate research community. His academic work operates within Muğla Sıtkı Koçman University's geological research infrastructure while maintaining extensive international collaborations. His findings significantly contribute to understanding regional climate dynamics in Anatolia and their connections to global climate systems throughout the Quaternary period.

• Spatial Statistics
• Statistical Consulting
• Biostatistics
• +۳ مورد دیگر

Ronald Barry is a Professor of Statistics at the University of Alaska Fairbanks, where he has maintained an active research and consulting practice since 1991. Holding a PhD from the University of California, Irvine, he operates from office CH 201C (contact: 907-474-7226, rpbarry@alaska.edu) and specializes in methodological innovations with cross-disciplinary applications. Education PhD in Statistics, University of California, Irvine (1991) Research Focus Barry's core expertise lies in spatial statistics, developing advanced techniques for geolocated data analysis in regions with irregular boundaries and holes. His work includes diffusion-based density estimators, nonparametric co-kriging, and lattice-based smoothing algorithms implemented through R packages. This methodological research extends to biostatistical applications in wildlife biology, nutrition, and fisheries, alongside chemometrics projects involving NMR spectroscopy for chemical mixture analysis and medical statistics research on sentinel node biopsy failures in melanoma using SEER cancer data. Publication Trends His recent publications (2015-2021) reveal three dominant trajectories: (1) continuous refinement of spatial modeling techniques for complex geometries, (2) expansion into biomedical and petroleum engineering applications through collaborative consulting, and (3) development of statistical software tools that bridge theoretical methodology with practical implementation. The interdisciplinary nature of his work is evident in publications spanning environmental statistics, reservoir engineering, and adolescent health studies. Scientific Awards No specific awards, fellowships, or honors were documented in the source material. Advising and Collaborations Barry actively mentors graduate students at UAF, with Bernard, Paryani, Canary, and Maier serving as primary authors on collaborative publications. His extensive consulting portfolio—covering experimental design, analysis, and reporting across wildlife biology, nutrition, and fisheries—suggests significant engagement with external research funding, though specific grants aren't detailed. Current projects indicate ongoing collaboration with medical researchers on melanoma diagnostics and petroleum engineers on reservoir modeling.

• Experimental Design
• Evolutionary Game Theory
• Microeconomics
• +۲ مورد دیگر

Prof. Dr. Lorens Imhof is a Professor at the Department of Economics at the University of Bonn, affiliated with the Institute for Financial Economics & Statistics and the Hausdorff Center for Mathematics . His research bridges economics and mathematics through experimental and evolutionary frameworks. Institute for Financial Economics & Statistics Hausdorff Center for Mathematics Research Interests include experimental design, evolutionary game theory, and applications to behavioral economics. His work explores strategic interactions, incentive mechanisms, and mathematical modeling of economic and biological systems. Publication Trends highlight interdisciplinary contributions to journals like Management Science , PNAS , and Journal of Economic Theory . Key themes include tournament design, Bayesian inference, Lyapunov stability in evolutionary models, and optimal experimental frameworks. Contact : limhof@uni-bonn.de | Office hours by appointment.

Elif Yıldırımمشاهده پروفایل

پژوهشگر

• Statistics
• Machine Learning
• Health Informatics
• +۷ مورد دیگر

Elif Yıldırım is a Researcher at the Computer Engineering Department , Istanbul Technical University . She is currently pursuing her PhD in Computer Engineering after completing a Master's degree in Computer Engineering (2022) and a Bachelor's degree in Industrial/Computer Engineering (2016-2020). PhD in Progress: Computer Engineering, Istanbul Technical University (2025) Master's: Computer Engineering, Istanbul Cultural University (2022) Bachelor's: Industrial Engineering & Computer Engineering, Marmara University (2017); Computer Engineering, Istanbul City University (2016) Her research focuses on Statistics and Machine Learning with applications in Health Informatics and Public Health . She specializes in Survival Analysis, Regression Modeling, and Psychometric Scale Validation , particularly in post-pandemic contexts and cross-cultural adaptations. Recent trends show her work spans Nutrition Literacy , Long COVID Prediction , and Joint Modeling of Longitudinal Data . She has published in journals like Frontiers in Nutrition and eBioMedicine while maintaining a Scopus h-index of 22 .

Frank Tuylمشاهده پروفایل

مدرس

• Applied Statistics
• Biostatistics
• Statistical Theory
• +۳ مورد دیگر

Dr. Frank Tuyl is a Lecturer at the University of Newcastle, affiliated with the College of Engineering, Science and Environment and the School of Information and Physical Sciences , with a focus on Data Science and Statistics . He holds a PhD in Statistics from the University of Newcastle and has over 20 years of experience in statistical and operations research consulting across manufacturing, IT, academia, and health sectors. Research Expertise: Applied Statistics, Biostatistics, Statistical Theory Email: frank.tuyl@newcastle.edu.au Dr. Tuyl specializes in Bayesian methods and applications, particularly in prior elicitation, posterior predictive distributions, and handling zero-event scenarios in statistical modeling. His work spans healthcare monitoring, tobacco use projections, financial statistics, and clinical prediction rules. He has contributed to the development of credible intervals for probabilistic evidence in physical therapy and epidemiology. His 2024 article in Statistical Science revisits Jeffreys' century-old prior point mass methodology for binomial distributions, while earlier works focus on Bayesian consensus priors, multinomial models, and global tobacco trends. His publications demonstrate a consistent application of Bayesian inference across public health, clinical diagnostics, and industrial statistics. Supervision and Grants: Dr. Tuyl has served as Principal or Co-Supervisor for four PhD completions. He has secured 10 grants totaling $638,341, primarily from the World Health Organization, Department of Health and Aged Care (Australia), and internal University of Newcastle funds. Projects include mortality impact analysis for tobacco interventions, Bayesian hierarchical modeling, and statistical tool development for the WHO Interactive Smoking Projection Tool.

Qian Qinمشاهده پروفایل

استادیار

• Bayesian Statistics
• Markov Chain Monte Carlo (MCMC)
• Statistical Computing
• +۳ مورد دیگر

Qian Qin is an Assistant Professor in the Department of Statistics at the University of Minnesota (Twin Cities). Their research focuses on Bayesian statistics and computational methods, particularly Markov Chain Monte Carlo (MCMC) algorithms and data augmentation techniques. Research interests include: Convergence analysis of MCMC samplers Bayesian robust multivariate linear regression Hierarchical structure-based sampling algorithms Statistical computing efficiency Trans-dimensional MCMC methods Random-scan Gibbs samplers Recent research outputs (2018-2024) demonstrate expertise in improving convergence rates for Bayesian computation. Key collaborations include work with Jones, G. L. and Wang, G. Current projects include the NSF-funded initiative Large Sample Analysis of MCMC in Bayesian Statistics from a Frequentist Perspective (2021-2025). No formal awards or honors are currently listed in the provided materials.

• Ultrasonics
• Nondestructive Testing
• Robotics
• +۳ مورد دیگر

Morteza Tabatabaeipour is a Lecturer in Advanced Manufacturing at the School of Engineering, Ulster University . His research focuses on ultrasonic nondestructive testing, guided wave inspection, and robotic applications in industrial analysis. Institution: Ulster University Academic Rank: Lecturer Research Areas: Ultrasonics, Robotics, Composite Materials, Guided Wave Technology His work includes developing flexible ultrasonic arrays, mitigating biofouling in marine environments, and integrating machine vision with ultrasonic testing. Recent projects explore wall thickness quantification using shear horizontal waves and calibration-free guided wave analysis. Email: m.tabatabaeipour@ulster.ac.uk

• Cognitive Science
• Mathematical Psychology
• Memory
• +۴ مورد دیگر

Professor Scott Brown is a distinguished cognitive scientist at the University of Newcastle, where he serves as Professor in the School of Psychological Sciences within the Psychology department. With a unique interdisciplinary background combining psychology and mathematics, Brown has established himself as a leading researcher in mathematical psychology, focusing on how humans process information, make decisions, and solve complex problems. His academic journey began at the University of Newcastle, where he earned multiple degrees: Bachelor of Science (Psychology) Bachelor of Mathematics Bachelor of Science PhD in Psychology After completing his doctorate in 2002, he spent four years as an Assistant Professor at UC Irvine before returning to Australia to join the University of Newcastle. His research has been consistently supported by prestigious fellowships, including an Australian Research Council (ARC) Fellowship from 2008-2013 and a Queen Elizabeth II Fellowship from 2006-2010. Professor Brown's research program uniquely bridges theoretical cognitive science with real-world applications. His primary focus is on mathematical modeling of higher-order cognitive processes, particularly memory and decision-making. What sets his work apart is the application of these models to critical real-world scenarios where human cognition directly impacts outcomes. His team employs a combination of laboratory experiments and analysis of real-world data to develop precise mathematical theories that can make exact predictions about human behavior and thinking. He frequently emphasizes that mathematical theories in psychology allow for precision impossible to achieve with intuition alone. Brown's recent publications reveal a researcher at the forefront of several exciting interdisciplinary frontiers. His work spans from understanding medical decision-making in cancer patients to developing countermeasures against cyber hackers by exploiting their cognitive biases. He has also applied psychological theory to citizen science efforts for endangered species conservation and to improving public transportation systems. The mathematical rigor of his approach is evident across all these domains, with consistent emphasis on developing models that can make precise, testable predictions about human behavior in critical situations. Among his notable scientific achievements are being named a Fellow of the Society for Mathematical Psychology (2015) and a Fellow of the Psychonomic Association (2012). He has also received prestigious New Investigator Awards from both the Society for Mathematical Psychology (2008) and the American Psychological Association (2006). These awards recognize his significant contributions to advancing mathematical approaches in psychological science. Professor Brown has successfully secured substantial research funding, particularly through competitive Australian Research Council grants. His work often involves interdisciplinary collaboration across psychology, mathematics, computer science, medicine, and ecology. He has mentored numerous PhD students and early-career researchers, taking particular pride in how his former students have 'gone on to think much bigger, brighter and deeper thoughts than me!' His approach to research emphasizes the importance of mathematical precision in understanding human cognition, challenging common misconceptions about psychological research, particularly the notion that 'everyone understands what it is to be human' therefore psychological research is unnecessary. Currently, Brown leads a strong research group focused on mathematical psychology, collaborating with researchers across multiple disciplines. His lab investigates how cognitive models can be applied to improve outcomes in critical domains including healthcare decision-making and cybersecurity. One particularly innovative project involves studying state-sponsored hackers to develop cognitive countermeasures that can impede their effectiveness by exploiting psychological biases. This work, funded by a US intelligence agency, represents the cutting-edge application of cognitive science to national security challenges.

• Monetary policy
• Economic cycles
• Macroeconomic forecasting
• +۳ مورد دیگر

Prof. Dr. Maik Wolters is Research Director of the ifo Center for Macroeconomics and Surveys at the ifo Institute for Economic Research (since October 2020). Concurrently he is Affiliate Professor and Research Fellow at the Institute for Monetary and Financial Stability, Goethe University Frankfurt, and Research Fellow at the Kiel Institute for the World Economy. Prior to joining ifo, he held professorships at the University of Würzburg (2017-2020) and the University of Jena (2012-2017), and was Junior Professor at Kiel University and the Kiel Institute for the World Economy (2010-2012). Education Ph.D. (Dr. rer. pol.), Goethe University Frankfurt, 2007-2010 M.Sc. Quantitative Economics, Goethe University Frankfurt, 2005-2007 M.A. International Business, ESC Rennes School of Business, 2004-2005 Vordiplom, Bielefeld University, 2002-2004 Research Interests Prof. Wolters’ work lies at the intersection of monetary economics , macro-econometrics , and applied forecasting . He studies the transmission of monetary policy, especially during financial crises, and develops econometric techniques for real-time estimation of business-cycle indicators such as the output gap. A second strand of research investigates global financial cycles and their historical evolution since 1880. Recently, he has also examined energy-market shocks, analysing supply and demand disruptions in the German natural-gas market. Scientific Awards & Recognition While no specific awards are listed, Prof. Wolters’ research is published in top-tier journals such as the Journal of Applied Econometrics , Journal of International Money and Finance , Journal of Business & Economic Statistics , and the International Journal of Forecasting , attesting to strong peer recognition. Research Projects & Grants ifo Konjunkturprognose (ifo Business-Cycle Forecast) – ongoing internal project producing quarterly macro forecasts. Gemeinschaftsdiagnose 2018-2022 – funded by the German Federal Ministry for Economic Affairs and Energy (BMWi) to deliver the Joint Economic Forecast for Germany. Teams & Networks He leads the ifo Center for Macroeconomics and Surveys team at the ifo Institute and collaborates extensively with the Institute for Monetary and Financial Stability at Goethe University Frankfurt and the Kiel Institute for the World Economy , serving as a bridge between academic research and policy-oriented forecasting.

Mohammad Samsul Alamمشاهده پروفایل

پژوهشگر ارشد

• Biostatistics
• Bioinformatics
• Longitudinal Data Analysis
• +۴ مورد دیگر

Mohammad Samsul Alam is a Postdoctoral Associate in the Department of Biostatistics & Bioinformatics at Duke University. His research focuses on advanced statistical methodologies for analyzing complex biomedical and environmental datasets. Research interests include longitudinal data analysis, multi-omics integration, clustering algorithms, and skewed functional data modeling. His work addresses dynamic risk estimation in Alzheimer's disease, rainfall pattern recognition in Bangladesh, and statistical frameworks for heterogeneous data. Recent publications highlight his expertise in integrative data analysis, with applications in both precision medicine and environmental science. He has co-authored methodological advancements in journals such as Statistics in Medicine and Biometrics .

Le Baoمشاهده پروفایل

استاد

• Bayesian Methods
• Data Mining
• Computational Statistics
• +۷ مورد دیگر

Le Bao is a Professor of Statistics at Pennsylvania State University , specializing in Bayesian methods, data mining, and computational statistics with applications in public health, epidemiology, and environmental science. They are affiliated with the Huck Institutes of the Life Sciences through the Center for Infectious Disease Dynamics and the Department of Statistics . Research Focus: HIV epidemic modeling, spatial statistics, Bayesian hierarchical models, and health inequities Key Collaborations: Applied work in cardiovascular health, nutritional science, and gene-environment interactions Recent Trends: Development of statistical tools for antenatal HIV surveillance, multivariate spatial modeling, and network scale-up population estimation Scientific Awards: No specific awards mentioned in available text. Publications: Active in journals including Biometrics , Annals of Applied Statistics , and Journal of the Royal Statistical Society . Their work addresses HIV recency classification, environmental risk assessment, and public health policy modeling.

Andrea Carronمشاهده پروفایل

مدرس ارشد

• Model Predictive Control
• Learning-based Control
• Coverage Control
• +۷ مورد دیگر

Dr. Andrea Carron is a Senior Lecturer at ETH Zürich, affiliated with the Intelligent Control Systems group under Professor M. Zeilinger at the Department of Mechanical and Process Engineering. He holds a PhD in Information Technology from the University of Padova (2016) and was a Postdoc at ETH Zurich from 2016 to 2020. Education: B.S. and M.Sc. in Control Engineering (University of Padova, 2010 and 2012) Professional Roles: Senior Lecturer (ETH Zurich, 2022–present), Postdoc Fellow (ETH Zurich, 2016–2020) Research Interests: Andrea Carron's work focuses on Model Predictive Control (MPC) and Learning-based Control with safety guarantees. His research addresses challenges in Distributed Safe Learning , Coverage Control , and Autonomous Racing , utilizing Gaussian Processes and Stochastic Control frameworks. He has developed safety filters for racing vehicles, scalable MPC for mobility-on-demand systems, and Kalman-filter-enhanced GP regression techniques. Article Trends: Recent publications emphasize Autonomous Racing (ForzaETH Race Stack), Safe Learning for distributed systems, Gaussian Process applications in control, and Robust MPC under uncertainty. His work bridges machine learning and classical control theory, with applications in robotics and real-time systems. Teaching Activities: He has taught courses such as Signals and Systems and Advanced Model Predictive Control at ETH Zurich and Ashesi University since 2017. Course content includes discrete-time signal processing, system identification, and control algorithms.

• Machine Learning
• Bayesian Deep Learning
• Gaussian Process Models
• +۷ مورد دیگر

Rui Li is a Doctoral Researcher in the Department of Computer Science at Aalto University, Finland. Their research focuses on Bayesian deep learning, Gaussian processes, and robust machine learning methods. They collaborate closely with Professor Arno Solin and colleagues on problems involving uncertainty quantification, model generalization, and sensor fusion applications in robotics. Recent work highlights advancements in few-shot classification generalization, scalable Bayesian deep learning predictions, and hyperparameter optimization for Gaussian process models. Publications span conferences like WACV, ICLR, ICML, and ICCVW, addressing theoretical and applied challenges in machine learning and computer vision. Rui Li contributes to interdisciplinary research groups tackling problems in semantic segmentation robustness and visual-inertial SLAM optimization. Their collaborative efforts include participation in the Robust Semantic Segmentation UNCV2023 Challenge with a multi-institutional team.

• System Identification
• Control Theory
• Bayesian Estimation
• +۵ مورد دیگر

Yue Ju is a Postdoctoral Fellow at the Royal Institute of Technology (KTH) in Stockholm, Sweden, specializing in system identification, control theory, and statistical learning methods. Their research focuses on regularization techniques, Bayesian approaches, and their applications in control systems engineering. Dr. Ju's research interests span theoretical aspects of system identification and control theory with a strong emphasis on regularization methods and statistical learning. Their work bridges the gap between theoretical analysis and practical implementation, particularly in addressing challenges related to hyper-parameter estimation, convergence properties, and efficient computational methods for complex control problems. Recent research has expanded into dynamic programming for exploration, neural network approaches for nonlinear system modeling, and spatial-temporal data analysis. The publication history demonstrates a consistent focus on regularization methods for system identification, with particular expertise in empirical Bayes approaches, hyper-parameter estimation, and convergence analysis across multiple theoretical frameworks. Their work shows strong mathematical foundations applied to practical engineering problems, particularly in control systems where statistical learning methods can enhance traditional approaches. Dr. Ju's research has significant implications for both theoretical understanding and practical implementation of control systems, with applications spanning various engineering domains where precise system modeling and control are critical.

Pablo Lemosمشاهده پروفایل

پژوهشگر ارشد

• Cosmology
• Machine Learning
• Astrophysics
• +۳ مورد دیگر

Pablo Lemos is a postdoctoral Research Fellow at the Montrel Institute for Learning Algorithms (MILA) and the CIELA institute at the University of Montreal, where he is affiliated with the Department of Physics. He co-leads the accelerated forward modelling group at the Simons Collaboration in Learning the Universe and is a member of the Dark Energy Survey (DES) collaboration. Research Focus Lemos specializes in applying machine learning techniques to astrophysics and cosmology problems. His core research areas include: Graph neural networks for cosmological simulations Symbolic regression methods for data analysis Simulation-based inference techniques Bayesian inverse problem solving Large-scale structure analysis Publication Analysis Lemos's recent publications (2024-2025) demonstrate strong interdisciplinary work combining cosmology, astrophysics, and machine learning. Major themes include cosmological constraints from galaxy clustering and weak lensing, advanced Bayesian inference methods using diffusion models, and applications of neural networks to astrophysical data. Approximately 60% of recent publications focus on machine learning applications in cosmology, while 25% address astrophysical inverse problems, and 15% explore cometary science and planetary physics. Collaborations and Groups Co-leader of accelerated forward modelling group at Simons Collaboration in Learning the Universe Active member of Dark Energy Survey (DES) collaboration