جستجوی استاد راهنمای دانشگاهی

Deniz Baglanمشاهده پروفایل

دانشیار

• Econometrics
• Nonparametric Econometrics
• Semiparametric Models
• +۴ مورد دیگر

Deniz Baglan is an Associate Professor of Economics and serves as the Director of Undergraduate Studies in the Department of Economics at Howard University, which is part of the College of Arts and Sciences. He holds a Ph.D. in Economics from the University of California, Riverside, and contributes significantly to econometric theory and its applications in macroeconomics and finance. Research Interests: His primary research lies in nonparametric and semiparametric econometric methods, particularly applied to panel data models. He investigates issues related to unobserved heterogeneity, dynamic modeling, and structural inference in economic and financial contexts. His work spans both theoretical developments and empirical applications, focusing on macroeconomic dynamics and empirical finance. Publication Trends: His recent scholarly output shows a consistent focus on advancing nonparametric techniques for panel data, including identification, estimation, and testing in models with fixed effects, time-varying parameters, and nonlinear structures. These contributions are published in notable journals such as Journal of Macroeconomics and Studies in Nonlinear Dynamics and Econometrics , reflecting a strong trajectory in methodological econometrics. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: While specific students and grant details are not listed, his role as Director of Undergraduate Studies suggests active involvement in academic mentoring and curriculum development. As a faculty member publishing in top-tier econometrics journals, he likely contributes to research supervision at graduate levels and may participate in externally funded research projects. Labs and Research Teams: There is no mention of specific labs, research centers, or collaborative teams in the provided information.

Lindesay Scott-Haywardمشاهده پروفایل

دانشگاهی

• Ecological Modelling
• Statistical Ecology
• Wildlife Population Dynamics
• +۷ مورد دیگر

Lindesay Scott-Hayward is a Senior Research Fellow and Head of the Centre for Research into Ecological & Environmental Modelling (CREEM) at the School of Mathematics and Statistics, University of St Andrews, UK. She is actively involved in ecological and statistical research with applications in conservation and renewable energy impact assessment. Her work spans international collaborations, particularly in Namibia and Denmark. Research Interests: Her expertise lies in statistical ecology , wildlife population dynamics , and environmental risk modeling . She specializes in spatial analysis, point process modeling, and monitoring wildlife populations affected by environmental stressors such as drought and offshore wind development. Her work contributes to UN Sustainable Development Goals related to life on land and climate action. Publication Trends: Her recent research output shows a strong focus on conservation applications of statistical models , particularly in African elephant and seabird populations. There is a clear trend toward impact assessment of renewable energy projects , open science practices (with published code and data), and collaborative, interdisciplinary research involving government and industry partners. Scientific Awards: No specific awards or fellowships were mentioned in the provided text. Advising and Grants: She is accepting postgraduate research students and has supervised at least one research project. She leads multiple externally funded research projects, including Understanding the dynamics of wildlife populations in Namibia (funded by EPSRC), Danish Windfarms (NIRAS A/S), and two projects with the Carbon Trust: ORJIP Closing the Loop and AssESs . These grants reflect her leadership in applying statistical ecology to real-world conservation and energy challenges. Labs and Teams: She leads CREEM (Centre for Research into Ecological & Environmental Modelling), a prominent research center at the University of St Andrews focused on developing and applying statistical methods to ecological and environmental problems. Her team collaborates extensively with Danish institutions like Aarhus University and UK-based organizations such as the Carbon Trust.

Jianhan Chenمشاهده پروفایل

استاد

• Computational Biophysics
• Molecular Modeling
• Biomolecular Simulation
• +۱۲ مورد دیگر

Jianhan Chen is a Professor in the Department of Chemistry at the University of Massachusetts Amherst . He is affiliated with the Graduate Program in Molecular & Cellular Biology and the Institute for Applied Life Sciences (IALS), where he leads the Chen Research Group focused on computational biophysics and biomaterials. Ph.D., University of California, Irvine (2002) B.S., University of Science and Technology of China (1998) His research centers on theoretical and computational methods for studying biophysical and biomedical systems. Key areas include intrinsically disordered proteins , biomolecular condensates , ion channel gating , and multiscale modeling of proteins and RNAs. He has developed advanced algorithms for conformational sampling, implicit solvent models, and GPU-accelerated simulations, with tools such as CHARMM/OpenMM , MMTSB , and HyRes widely distributed. The recent publications (2021–2025) reflect a strong focus on ion channel dynamics (BK, TRP, HCN, TMEM16), phase separation , and machine learning applications in protein function prediction. His work integrates computational modeling with experimental collaboration to study neurodegenerative diseases, drug delivery, and single-molecule dynamics. He has no listed scientific awards in the provided materials. Jianhan Chen actively mentors students and postdoctoral researchers. His lab maintains significant computational infrastructure, including a dedicated GPU cluster ( Pikes ) and access to institutional and national high-performance computing resources. He is involved in training the next generation of computational scientists and encourages applications from students with backgrounds in biochemistry, biophysics, and programming. The Chen Research Group operates within the Life Science Laboratories and is part of broader interdisciplinary initiatives at UMass Amherst, including Models to Medicine and Center for Bioactive Delivery .

Aristidis K. Nikoloulopoulosمشاهده پروفایل

دانشیار

• Copula Modeling
• Biostatistics
• Econometrics
• +۳ مورد دیگر

Aristidis K. Nikoloulopoulos is an Associate Professor in Statistics at the School of Mathematics, University of East Anglia (since December 2023). Previously, he was an Associate Professor in the School of Computing Sciences (2013-2023) and a Lecturer (equivalent to Assistant Professor) there (2009-2013). He completed his PhD in Statistics at Athens University of Economics and Business in 2007, followed by postdoctoral work at Laval University and the University of British Columbia. He has also held positions at Athens University of Economics and Business and Cyprus University of Technology. Current affiliation: School of Mathematics, University of East Anglia Past affiliations: School of Computing Sciences (UEA), Department of Statistics (AUEB), Medical School (Cyprus UoT) His research focuses on copula modeling, with applications in biostatistics, econometrics, and finance. Key contributions include copula-GARCH models for financial returns, factor copula models for survey data, and copula-mixed models for meta-analysis of diagnostic tests. He has developed CRAN packages like CopulaREMADA and FactorCopula, and his work addresses dependence structures in multivariate discrete data. Recent publications highlight vine copula mixed models for diagnostic accuracy studies, joint meta-analysis of correlated diagnostic tests, and factor tree copula models for psychometric applications. These works emphasize dependence modeling, computational efficiency, and practical implementations in health and social sciences. He actively supervises PhD students and has delivered numerous invited seminars and short courses on copula modeling, including engagements at the University of Geneva and the Vine Copula Workshop. His teaching portfolio includes statistical methods, linear regression, time series, and computational statistics at undergraduate and postgraduate levels.

Stefanie Eiflerمشاهده پروفایل

استاد

• Sociological theories of action
• Sociology of crime
• Sociology of social control
• +۵ مورد دیگر

Prof. Dr. Stefanie Eifler is a Professor of Sociology and Empirical Social Research at the Catholic University of Eichstätt-Ingolstadt, leading the Chair of Sociology and Empirical Social Research. She serves as Dean of Studies for the Faculty of History and Social Sciences and Program Director of the Master's program in Sociology. Her work bridges sociological theory and empirical methodology, focusing on crime, social control, and urban sociology. Eifler is a key figure in advancing factorial survey methods and experimental techniques in criminological research. Education: Doctorate in Sociology (details not specified in text). Her research explores sociological action theories, crime rationality, and the validity of survey methodologies. Notable contributions include studies on vignette experiments, situational crime opportunities, and the interplay of self-control and criminal behavior. She co-edits journals like Methods, data, analyses and contributes to interdisciplinary criminology handbooks. Eifler’s projects include funded research on moral filters in criminal behavior (2024–2027) and urban disorder phenomena. She actively participates in academic committees and editorial boards, advocating for methodological rigor in social science research.

Kizito Salakoمشاهده پروفایل

مدرس

• Software reliability
• Bayesian statistics
• Probability theory
• +۷ مورد دیگر

Dr. Kizito Salako is a Lecturer in the Department of Computer Science at City, University of London (now merged with St George's). He is an applied mathematician and software developer at the Centre for Software Reliability, specializing in software reliability assessment using advanced statistical methods. Dr. Salako holds a first-class double honors degree in Mathematics and Statistics from the University of Lagos, a Master of Advanced Study in Mathematics from the University of Cambridge (where he was both a Shell Centenary Scholar and a Commonwealth Scholar), and a PhD in Computer Science from City, University of London. His research focuses on applying probability theory, Bayesian statistics, geometry and machine learning techniques to simulate, assess and forecast the failure behavior of software-based systems. He specializes in conservative Bayesian approaches for dependability assessment, particularly for safety-critical systems. His work has significant applications in autonomous vehicle safety and critical infrastructure protection. Dr. Salako's publications reveal a strong focus on developing statistical techniques that support conservative dependability claims for safety-critical systems, with particular attention to the challenges of assessing reliability when software systems evolve or when operational testing data may be limited. His recent work has increasingly focused on autonomous vehicle safety assessment. He has contributed to numerous research projects including DISIEM (EU Horizon-2020), CEDRICS (EPSRC), AFTER (EU FP7), DIDERO-PC (EPSRC), PIA-FARA (Technology Strategy Board), and others related to critical infrastructure interdependency analysis and cyber security. Dr. Salako is experienced in building simulations of complex systems using C++ and was instrumental in developing the PIA-FARA simulation engine for analyzing probabilistic, functional and process relationships in large-scale interdependent complex critical infrastructure.

Huajie Zhangمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Graphical and Probabilistic Models
• +۵ مورد دیگر

Huajie Zhang is a Professor at the Faculty of Computer Science , University of New Brunswick, with over 14 years of service. He holds a PhD in Computer Science from the University of Western Ontario and an MSc from Harbin Institute of Technology. Research Interests: Machine Learning Data Mining Graphical and Probabilistic Models Intelligent Systems Transfer Learning Semi-supervised Learning Efficient Learning Algorithms for Large Data Publication Trends: Huajie Zhang's work focuses on probabilistic models like Bayesian networks and Naive Bayes, emphasizing accuracy, efficiency, and adaptability in semi-supervised and transfer learning contexts. His recent papers explore scalable algorithms for big data, hybrid classifiers, and optimization techniques. Scientific Awards: Best Paper Award (Second Place) at FLAIRS Conference 2004 Teaching Activities: CS6735 Machine Learning CS3383 Algorithm Design

Carina da Silvaمشاهده پروفایل

استادیار

• Statistical Model Checking
• Monte Carlo Simulation
• Stochastic Hybrid Systems
• +۷ مورد دیگر

Dr. Carina da Silva is an academic researcher and educator at the Institute of Computer Science, University of Münster, within the Department of Mathematics and Computer Science. She holds the position of Akademische Rätin, a tenured academic role combining research and teaching responsibilities, equivalent to Assistant Professor. Her work focuses on formal methods and probabilistic verification in hybrid systems. Institution: University of Münster School: Department of Mathematics and Computer Science Department: Department of Computer Science Email: carina.dasilva@uni-muenster.de Office: Room 215, Einsteinstraße 62, 48149 Münster, Germany Dr. da Silva's research centers on the development and application of statistical model checking and Monte Carlo simulation techniques for analyzing stochastic hybrid systems, particularly hybrid Petri nets and stochastic hybrid automata. Her work enables rigorous dependability evaluation of complex systems such as battery-powered and critical infrastructures. She actively supervises bachelor’s and master’s theses and teaches courses including Simulation and Statistical Model Checking, as well as an advanced seminar on scientific work for computer science students. Her research is frequently published in top-tier venues like ACM Transactions on Modeling and Computer Simulation, NASA Formal Methods, and QEST. The most recent publications demonstrate a consistent trajectory in optimizing reachability analysis, rare event simulation, and model transformation, with a strong emphasis on formal correctness and computational efficiency. She is also involved in collaborative benchmarking efforts such as the ARCH competition, contributing to the evaluation and advancement of stochastic verification tools. Ursula von Euch Grant (2024) Recognized in Ausgezeichnete Informatikdissertationen 2021 Dr. da Silva is actively involved in academic governance, serving as a deputy member of the Department Council for Mathematics and Computer Science, representing academic staff. She returned from parental leave in March 2024 and continues to supervise theses and offer seminars. While specific advisee names are not listed, she regularly invites students to work on topics such as simulation frameworks, uncertainty quantification, convergence analysis, and integration of empirical data in model checking. She is not part of a named lab but collaborates closely with researchers like Prof. Anne Remke and contributes to the broader formal methods and embedded systems research groups at Münster.

• Statistics
• Biostatistics
• Missing Data Analysis
• +۷ مورد دیگر

Dr. Biao Zhang is a Professor in the Department of Mathematics and Statistics at the University of Toledo, College of Natural Sciences and Mathematics. His research focuses on advanced statistical methodologies, particularly in missing data analysis, empirical likelihood, and semiparametric modeling, with applications to clinical trials and biostatistics. University: University of Toledo School: College of Natural Sciences and Mathematics Email: biao.zhang@utoledo.edu Research Interests Dr. Zhang specializes in Statistics , Biostatistics , and Missing Data Analysis . His work develops innovative methods for: Empirical likelihood in clinical trials Density ratio models for ROC curve analysis Causal inference with missing responses Semiparametric estimation techniques Robust methods for treatment effect estimation Publications Overview His extensive publication record (59 papers, 1994-2024) reveals expertise in: Empirical likelihood applications ROC surface methodologies Multivariate analysis Case-control study frameworks Nonparametric and semiparametric approaches Statistical software development Co-author network includes Steven T. Haller , Christopher Cooper , and Pamela S. Brewster .

• Sentence Processing
• Psycholinguistics
• Neurolinguistics
• +۹ مورد دیگر

Leticia Pablos Robles is an Associate Professor at Leiden University's Humanities Centre for Linguistics (LUCL), specializing in Experimental Linguistics with a focus on sentence comprehension , prediction in the brain , and processing of long-distance dependencies . Her work integrates syntax, semantics, and prosody to explore how linguistic structures are processed across languages like Basque, Spanish, Mandarin, Dutch, French, and English. PhD in Linguistics (University of Maryland, 2006) BA in English Philology (University of Deusto, 1999) Her research employs event-related potentials (ERPs) , eye-tracking , and self-paced reading to analyze language comprehension. She investigates: Syntactic processing (e.g., clitic left dislocation, wh-dependencies) Syntax-semantics interface (e.g., Negative Polarity Items) Syntax-prosody interface (e.g., Wh-in-situ questions) Cognitive control in bilingualism and code-switching Recent publications highlight trends in cross-linguistic interference , bilingual reading , and ERP-based syntactic validation . She also contributes to replication studies and statistical methodology in linguistics. Scientific Awards : Teaching Excellence Award, University of Maryland (2002) Basque Government Post-doctoral Grant (2006-2008) Basque Government Pre-doctoral Grant (2002-2006) She supervises PhD candidates Zhenlin Chen and Ou Ourilige , and collaborates with projects like BRAINGLOT (Bilingualism and Cognitive Neuroscience). Her work bridges theoretical linguistics and experimental validation , emphasizing the competence-performance dichotomy.

Anne Gégout-Petitمشاهده پروفایل

استاد

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

Anne Gégout-Petit is a Professor at the University of Lorraine (UL) within the Faculty of Science and Technology, where she leads significant research initiatives in statistics and its applications. She serves as Director of the Elie Cartan Institute of Lorraine (UMR 7502) and is an active member of the INRIA BIGS team, demonstrating her leadership position in the French statistical research community. Her research expertise spans multiple domains of statistics with strong application components: Applied statistics and biostatistics Statistical modeling for biological and environmental systems High-dimensional data analysis Probability theory applications Epidemiological modeling Algorithm development for medical diagnostics Professor Gégout-Petit's publication record from 2022-2024 reveals a strong interdisciplinary focus, with research spanning forest pathology, cancer biology, pandemic surveillance, and genetic profiling. Her work consistently bridges theoretical statistical methodology with practical applications, particularly in environmental science, medicine, and public health. She has developed novel approaches including the FINE algorithm for medical data analysis and specialized modeling techniques for disease spread in ecosystems. She has demonstrated significant organizational leadership through directing major statistical events including the ENBIS meeting in Nancy (September 2-6, 2018) and the Statistics Days conference in Nancy (June 3-7, 2019), highlighting her standing within the European statistical community. Her professional activities include: Directing the Elie Cartan Institute of Lorraine Collaborating with researchers across biology, medicine, and environmental science Developing statistical methodologies for complex real-world problems Contributing to public health research during the COVID-19 pandemic

• Software Engineering
• Artificial Intelligence
• Cyber-Physical Systems
• +۳ مورد دیگر

Mason Brown serves as a Visiting Assistant Professor of Computer Science at Hamilton College, based in Taylor Science Center 3005. His research integrates software engineering and artificial intelligence to advance cyber-physical systems across ground, air, and space domains, with emphasis on practical implementations for real-world applications. Educational Background: Ph.D. in Computer Science from Griffith University M. IT. from Griffith University B. I.T. from Charles Sturt University His doctoral dissertation focused on An Ilities Tool for Minisatellite Software Validation , developed in collaboration with Gilmour Space Technologies. Research expertise spans satellite systems validation, multi-robot coordination, and AI-driven cyber-physical solutions. His postdoctoral work at the University of Technology Sydney involved multi-robot target tracking and path planning under Professor Robert Fitch. Current investigations include aircraft visual inspection via machine learning and natural language interfaces for robotic systems, demonstrating strong interdisciplinary connections between space engineering and robotics. Publication analysis reveals a clear trajectory from foundational satellite software validation (2020-2021) toward advanced human-robot collaboration (2023-2024), with consistent focus on verification methodologies and real-world deployment challenges. Key thematic threads include ilities-based validation frameworks, multi-robot field experimentation, and machine learning applications in aerospace. Teaching includes Computer Science for All and Algorithms and Data Structures , with active supervision of student projects in mechatronics and computer science. He particularly encourages new students to engage in research. Professional activities include appointment to the Australian Computer Science faculty in 2025 and collaborations with laboratories at Griffith University (Professor Paulo de Souza) and University of Technology Sydney (Graeme Best).

Thomas Fredrik Hansenمشاهده پروفایل

استاد

• Evolutionary Biology
• Theoretical Biology
• Quantitative Genetics
• +۵ مورد دیگر

Thomas Fredrik Hansen is a Professor at the Department of Biosciences, Faculty of Mathematics and Natural Sciences, University of Oslo, where he is affiliated with the Centre for Ecological and Evolutionary Synthesis (CEES). His research primarily focuses on evolutionary biology, theoretical biology, and quantitative genetics, with particular emphasis on evolvability, macroevolution, and phylogenetic comparative methods. Hansen's research interests span evolutionary quantitative genetics, morphological evolution, evolvability, and macroevolutionary dynamics. His work integrates theoretical frameworks with empirical data to understand how genetic architecture influences evolutionary potential and constraints. He has made significant contributions to understanding how evolvability shapes macroevolutionary patterns, the role of epistasis in morphological divergence, and the application of phylogenetic comparative methods to study evolutionary processes across diverse taxa. His theoretical work on evolutionary constraints and adaptive landscapes has provided new insights into the mechanisms that govern long-term evolutionary change. Analysis of Hansen's recent publications reveals a consistent focus on the intersection of quantitative genetics and macroevolution. His work demonstrates how microevolutionary processes scale up to macroevolutionary patterns, with particular attention to evolvability as a key determinant of evolutionary trajectories. He has pioneered approaches to measuring and modeling the relationship between genetic architecture and evolutionary potential across different time scales. His research often involves collaborations with empirical evolutionary biologists to test theoretical predictions using data from diverse organisms including plants, insects, fish, and mammals. Hansen has been actively involved in mentoring students and collaborating with researchers across various institutions. His work has contributed significantly to methodological advances in evolutionary biology, particularly in the areas of phylogenetic comparative methods and quantitative genetics. He has also contributed to important theoretical debates regarding evolutionary constraints, the measurement of selection, and the interpretation of evolutionary patterns in the fossil record. Hansen maintains an active research program through the Centre for Ecological and Evolutionary Synthesis (CEES), where he collaborates with researchers studying diverse aspects of evolutionary biology, ecology, and biodiversity. His work bridges theoretical and empirical approaches to evolutionary biology, making substantial contributions to our understanding of how genetic architecture shapes evolutionary potential across different biological scales.

Ioulia Papageorgiouمشاهده پروفایل

دانشیار

• Sampling Theory
• Model-Based Clustering
• Mixture Models
• +۳ مورد دیگر

Dr. Ioulia Papageorgiou is an Associate Professor in the Department of Statistics at the Athens University of Economics and Business (AUEB), School of Information Sciences and Technology. She holds a B.Sc. in Mathematics (2.1) and a Ph.D. in Statistics, both from the University of Ioannina. Her career spans since 2001, with prior roles as a Post-Doctoral Research Fellow at AUEB and Nottingham Trent University, and Lecturer at AUEB (2002-2007). Education B.Sc. in Mathematics (2.1), University of Ioannina Ph.D. in Statistics, University of Ioannina Employment History Associate Professor, AUEB Department of Statistics (2007-present) Lecturer, AUEB Department of Statistics (2002-2007) Post-Doctoral Research Fellow, AUEB (2001-2002) Post-Doctoral Research Fellow, Nottingham Trent University (1998-2001) Her research focuses on Sampling Theory, Model-Based Clustering, Mixture Models, and their Applications to Archaeometry. She has developed statistical methodologies for ceramic artifact provenance studies, explored hierarchical cluster analysis for overpainted artwork identification, and addressed optimal sampling designs for autocorrelated populations. Her work bridges statistical innovation with archaeological and medical applications. Analysis of her recent publications reveals a strong emphasis on statistical modeling in archaeology (e.g., ceramic provenance via p-XRF), computational methods for multivariate data, and pharmacoeconomic assessments for chronic diseases. Keywords across her work include Archaeometry, Cluster Analysis, Multivariate Modeling, and Survey Sampling.

Sabrina Giordanoمشاهده پروفایل

دانشیار

• Categorical Data Analysis
• Longitudinal Data Analysis
• Copula Functions
• +۷ مورد دیگر

Sabrina Giordano is Associate Professor of Statistics at the University of Calabria , where she teaches Statistics and Data Science courses in both Italian and English. She holds a PhD in Methodological Statistics from the University of Milano-Bicocca and has held visiting positions at RWTH Aachen University, University of Florida, and University of Plymouth. Her research focuses on Statistical modeling of categorical and ordinal data Latent variable models and copula functions Longitudinal analysis and hidden Markov chains Applications in finance, health, and environmental statistics Her methodological contributions are implemented in the hmmm R-package and supported by grants like the PRIN2022 project "SMILE: Statistical Modelling and Inference to Live the Environment". She serves as Associate Editor for Biometrical Journal and Statistical Methods & Applications Director of the Post-Graduate Master in Artificial Intelligence & Data Science Local coordinator for the COST Fin-AI research group Her recent collaborative work explores Dynamic response styles in longitudinal data Fairness-aware classification algorithms Copula-based dependence structures Applications to financial vulnerability and risk perception