جست‌وجوی استاد و پژوهشگر

• Logics
• Databases
• Formal languages
• +۵ مورد دیگر

Gabriele Puppis is an Associate Professor at the Department of Mathematics, Computer Science, and Physics of Udine University since 2022. Previously, he held a tenure-track position at the same university (2019-2022) and was a tenured CNRS researcher at LaBRI, Bordeaux (2012-2019). He completed postdoctoral research at Oxford University (2009-2012) and Udine University (2006-2008). His academic journey began with a PhD in Computer Science at Udine University (2002-2006). Education: PhD in Computer Science, Udine University (2006) Research Interests: Logics and formal languages String transducers and automata theory XML schema validation and database repairability Interval temporal logic and spatial reasoning Higher-order query languages Academic Contributions: His work focuses on the decidability and complexity of problems in automata theory, particularly for finite-valued transducers and their equivalence. He has advanced understanding of bounded repairability for tree and word languages, dynamic data structures for timed automata, and resynchronizability in transducer models. His recent publications explore decomposition theorems for streaming string transducers and their equivalence to two-way models. Teaching and Leadership: He teaches courses in Distributed Systems, Algorithms and Data Structures, and Verification techniques in AI and Cybersecurity. He has organized events like the GandALF 2023 summer school and participated in workshops on unambiguity in automata theory and regular cost functions.

Eunsook Kimمشاهده پروفایل

استاد

• Psychometrics
• Structural Equation Modeling
• Multilevel Modeling
• +۴ مورد دیگر

Dr. Eunsook Kim is a Professor in the College of Education at the University of South Florida (USF Tampa campus). Her research specializes in advanced quantitative methodologies for educational and psychological measurement. Research Focus: Dr. Kim's work centers on psychometric innovations, including multilevel modeling, structural equation modeling, factor mixture approaches, and rigorous testing of measurement invariance in complex datasets. She frequently employs Monte Carlo simulations to evaluate methodological robustness in cross-cultural, longitudinal, and hierarchical research designs. Publication Trends: Her recent articles (2011-2017) demonstrate consistent focus on improving statistical methods for detecting measurement bias, validating instruments across populations, and developing reporting standards for multilevel analyses. Collaborative work features prominently, often addressing methodological challenges in large-scale educational and psychological assessment.

• Statistics
• Randomized Response Techniques
• Survey Sampling
• +۵ مورد دیگر

Pier Francesco Perri is a Full Professor of Statistics (SSD STAT-01/A) at the Department of Economics, Statistics, and Finance 'Giovanni Anania' (DESF) of the University of Calabria (Unical). He has held academic positions since 2002 and coordinates institutional activities such as the National Plan for Scientific Degrees (L-41, Statistics) and editorial roles in journals like Metron and Model Assisted Statistics and Applications . His research focuses on survey methodology, sensitive data estimation, and statistical modeling. Education: Ph.D. in Methodological Statistics (University of Trento, 2002), Honors Degree in Statistical and Actuarial Sciences (University of Calabria, 1997) Perri’s research spans randomized response techniques for sensitive populations, copula modeling for financial data, and inequality index linearization. He explores indirect questioning methods to detect truthfulness and improve survey accuracy, particularly for stigmatized behaviors like abortion, migration, and non-heterosexual identities. His work integrates auxiliary information and complex sampling designs to enhance estimation efficiency. Recent publications emphasize innovations in sample survey traditions, including the 2025 editorial on methodological advancements and 2024 studies on adjusting self-protective behaviors in prevalence estimation. His 2023–2021 research includes logistic regression extensions for randomized response models, prevalence estimates for health behaviors, and crossed model applications for sensitive topics. Earlier work (2020–2000) covers item sum techniques, financial copula modeling, and environmental sensitivity analysis. He serves on editorial boards and institutional committees, including the Italian Statistical Society’s Council and the Ph.D. Teaching Board at Unical. The Laboratorio Statistico Informatico supports his research in statistical computing and survey design.

Fraser A. Dalyمشاهده پروفایل

دانشیار

• Applied Probability
• Random Graphs and Networks
• Markov Processes
• +۴ مورد دیگر

Fraser A. Daly serves as Associate Professor in the Department of Actuarial Mathematics & Statistics within the School of Mathematical and Computer Sciences at Heriot-Watt University, Edinburgh. His academic appointment includes leadership roles as undergraduate admissions tutor and instructor for statistical modeling and programming courses. From 2022-2024, he directed the Scottish Mathematical Sciences Training Centre (SMSTC), a consortium serving nine Scottish universities, and previously held positions as MSc Financial Mathematics programme director and Royal Statistical Society Applied Probability section vice chair. Research interests center on applied probability with emphasis on distributional approximation techniques. His work develops theoretical frameworks for approximating complex probability distributions using tractable models like Gaussian or Poisson distributions, with explicit error bounds. Key application areas include random graphs and networks , Markov processes , and actuarial models . The research fingerprint reveals strong focus on approximations (100%), randomness (53%), distribution theory (51%), and Poisson approximation (41%), with significant contributions to Gaussian distribution analysis and graph theory. Analysis of his 15 most recent publications (2021-2025) shows consistent focus on Stein's method applications, distributional approximations across diverse contexts (asymmetric Laplace, geometric-type, negative binomial), and network theory applications. The work demonstrates interdisciplinary reach spanning probability theory, statistical inference, epidemiology modeling, and actuarial science, with increasing emphasis on random graph asymptotics and optimal control in stochastic systems. Professional service includes editorial contributions such as the 2019 Stochastic Networks special issue introduction, reflecting leadership in the probability community. His role in directing SMSTC from 2022-2024 demonstrates commitment to graduate mathematical education across Scotland. Actively supervising PhD students, Daly welcomes projects in random graphs, Markov processes, and actuarial models. His research program shows sustained productivity with 21 total publications including multiple high-impact articles in journals like Scandinavian Actuarial Journal and Journal of Statistical Physics , with growing citation impact (29 Scopus citations for his 2016 Conway-Maxwell-Poisson work).

Matthieu Marbac-Lourdelleمشاهده پروفایل

دانشیار

• Biostatistics
• Model-based Clustering
• Computational Statistics
• +۲ مورد دیگر

Matthieu Marbac-Lourdelle is an Associate Professor in Statistics at ENSAI/CREST (Bruz, France) since September 2023. Previously, he served as an Assistant Professor at the same institution from September 2017 to September 2023. He is also an external collaborator of the PreMedical team, a joint team between Inria and Inserm, since September 2022. He completed his PhD in October 2014 at the University of Lille 1 under the supervision of Christophe Biernacki and Vincent Vandewalle. In 2022, he defended his habilitation, the French qualification required to supervise PhD students. Dr. Marbac-Lourdelle's research focuses on Biostatistics , Model-based Clustering , Computational Statistics , Empirical Likelihood , and Mixture Models . His work bridges theoretical statistical methods with practical applications in various domains including epidemiology, sports science, and genomics. His recent publications reveal a strong emphasis on developing advanced clustering techniques, particularly for complex data structures including functional data, mixed data types, and data with missing values. His research has significant applications in health monitoring, epidemiological studies, and sports analytics. Dr. Marbac-Lourdelle serves as an Associate Editor for Computational Statistics & Data Analysis (since 2021) and Econometric and Statistics (since 2023). He currently supervises five PhD students working on diverse topics including nonparametric mixture models, soccer game analysis, swimming technique monitoring, arrhythmia detection in racehorses, and probabilistic substitution models. His collaborations span multiple institutions including CREST, Inria, Inserm, and international partners at HEC Montreal and the University of McMaster.

• Applied Probability
• Risk Models
• Stochastic Arrangements and Dependence
• +۷ مورد دیگر

Michael V. Boutsikas is an Associate Professor in the Department of Statistics and Insurance Science at the University of Piraeus , Greece. Since 2000 he has held progressively senior academic posts, beginning as Lecturer and becoming tenured Assistant Professor before his promotion to Associate Professor in 2016. He earned his Diploma (1995), M.Sc. (1998) and Ph.D. (2000) in Mathematics from the University of Athens, all awarded with distinction. His research lies at the intersection of applied probability, actuarial science and reliability theory . Core interests include risk models, stochastic dependence, probability metrics, extreme value theory, scan statistics, urn models and compound Poisson approximation . These themes are unified by the aim of quantifying and bounding the behaviour of complex stochastic systems. Across more than twenty-five peer-reviewed articles published in leading journals such as The Annals of Applied Probability , Journal of Applied Probability , Bernoulli , Insurance: Mathematics and Economics and Naval Research Logistics , a clear trajectory emerges: developing sharp approximations and limit theorems for rare events, system reliability, and aggregate claims in risk processes. While the text does not list formal awards, it notes that his work has attracted at least 150 citations from international journals and monographs and that he maintains an h-index of 9 . He is an active referee for more than twenty journals and a reviewer for Mathematical Reviews . Teaching duties span undergraduate courses ( Risk Management, Stochastic Processes, Simulation ) and postgraduate offerings ( Simulation Methods, Extreme Price Theory, Stochastic Financial Models ). He has also authored sixteen sets of detailed teaching notes and lecture notes covering reliability, risk management and statistical software. No specific doctoral students or grant details are provided, but his long-standing presence and prolific output indicate sustained supervisory and research funding activity within the Department of Statistics and Insurance Science.

Villó Csiszárمشاهده پروفایل

استادیار

• Probability Theory
• Statistics
• Information Theory
• +۵ مورد دیگر

Villó Csiszár is an Assistant Professor at the Department of Probability Theory and Statistics , Faculty of Science , Eötvös Loránd University . Her research focuses on Probability Theory , Statistics , Information Theory , and Markov Chains . Education: PhD in Mathematics (2009, Eötvös Loránd University) MSc in Mathematics (2000, Eötvös Loránd University) Erasmus scholarship at the University of Sheffield (1998-1999) Positions: Assistant Professor (2010-present, ELTE Faculty of Economics) Teaching Assistant (2002-2010, ELTE Faculty of Economics) Visiting Researcher (2007-2008, Rényi Institute of Mathematics) Her work spans random permutations , conditional independence models , hierarchical models , and probabilistic inequalities , with applications in environmental science and interdisciplinary research . Publications include studies on Chebyshev-type inequalities , Markov bases , and statistical inference . She has presented at international conferences such as the 25th EMS European Meeting of Statisticians (Oslo, 2005) , Workshop on Polytopes and Algebraic Statistics (Stockholm, 2008) , and Prague Stochastics (2010) .

• Environmental Economics
• Causal Inference
• Data Science
• +۳ مورد دیگر

Vincent Bagilet is a Postdoctoral Fellow at ENS Lyon in the Department of Economics and the Center for Economic Research on Governance, Inequality and Conflict (CERGIC). He completed his PhD in Sustainable Development at Columbia University in 2024. Research Interests: Individual and collective environmental attitudes Unstructured data analysis Causal inference and data visualization Short-term health effects of air pollution His work includes retrospective power analysis of causal inference literature and projects like 'Causal Exaggeration: Unconfounded but Inflated Causal Effects' and 'Estimating Small Effects: Air Pollution and Health'. Technical implementations are available on his GitHub portfolio. Contact: vincent.bagilet@ens-lyon.fr | Personal Website | GitHub

• Latent Variable Models
• Mixture Models
• Rank Data Models
• +۷ مورد دیگر

Claire Gormley is an Associate Professor of Statistics at University College Dublin (UCD) and a Funded Investigator at the Insight Centre for Data Analytics. She joined UCD in 2006 as an Assistant Lecturer, was promoted to Assistant Professor in 2008, and to Associate Professor in 2017. Her research group specializes in Machine Learning & Statistics with applications spanning genetics, agriculture, and medical sciences. Education PhD in Statistics, Trinity College Dublin (2007) Visiting Scholar, Department of Statistics/Centre for Statistics and the Social Sciences, University of Washington Seattle (2006) Research Expertise Professor Gormley develops advanced statistical methodologies including latent variable models, mixture models, and Bayesian approaches for high-dimensional data analysis. Her work addresses dimensionality challenges in diverse fields such as metabolomics, orthopaedics, and social science, with current projects focusing on model-based clustering for mixed-type high-dimensional datasets. Methodological innovations are consistently applied to real-world problems involving large-scale biological and agricultural data. Publication Trends Her recent publications (2023-2025) demonstrate strong interdisciplinary impact across genetics (tuberculosis, dairy cattle), agricultural science (nitrogen utilization), and medical research (neonatal sepsis). Methodologically, she pioneers mixture models and latent variable frameworks for complex data structures, particularly in multi-omics integration and network analysis. A clear trajectory shows increasing focus on computationally intensive solutions for biological big data, with applications directly addressing food security and healthcare challenges. Research Leadership As a Funded Investigator at the Insight Centre, Dr. Gormley leads a dynamic research group securing competitive grants for interdisciplinary projects. Her team collaborates with veterinary scientists, geneticists, and medical researchers on grant-funded initiatives including tuberculosis genomics and metabolomics data analysis. The group maintains strong industry partnerships in agricultural technology and healthcare analytics, with recent projects developing predictive models for dairy cattle health monitoring and clinical diagnostics.

Yoon Jin-heeمشاهده پروفایل

استاد

• Mathematical Data Science
• Soft Computing Theory
• Fuzzy Logic
• +۳ مورد دیگر

Professor Yoon Jin-hee is affiliated with the Department of Mathematics and Statistics at Sejong University, where she leads the Mathematical Data Science Lab. Her research focuses on mathematical data science and soft computing theory, bridging fuzzy logic, neural networks, and statistical modeling. Her work spans fuzzy logic and its applications in behavioral and conflict modeling neural network design with fuzzy systems regression analysis for complex data chaotic dynamics in socioeconomic models time series forecasting in AI-driven environments Recent publications emphasize hybrid architectures like LSTM and self-organizing fuzzy neural networks, along with advanced moderation/mediation frameworks. The Mathematical Data Science Lab serves as a hub for developing evolutionary algorithms, attention mechanisms, and theoretical advancements in fuzzy inner products and polynomial classifiers.

Mark Barashمشاهده پروفایل

دانشیار

• forensic genetics
• molecular phenotyping
• forensic DNA analysis
• +۷ مورد دیگر

Mark Barash is an Associate Professor in the Department of Justice Studies at San Jose State University's College of Social Sciences. His research integrates molecular biology, anthropology, microbiology, human genetics, genomics, bioinformatics, statistics, and machine learning to advance forensic DNA analysis capabilities. University: San Jose State University College: College of Social Sciences Department: Justice Studies Email: mark.barash@sjsu.edu Barash's primary research focuses on developing novel forensic DNA panels using massively parallel sequencing technology and machine learning pipelines to create molecular identikits for criminal investigations. His work addresses critical challenges in DNA recovery from firearms/ammunition and genetic/epigenetic trait prediction for cold cases, missing persons, and counter-terrorism. Key research trends include: Enhancing forensic DNA recovery techniques from challenging surfaces Machine learning applications in DNA phenotyping Development of the "Genetic Witness" tool for appearance prediction Interdisciplinary collaborations with forensic industry partners Validation of probabilistic genotyping systems Microbial profiling for forensic identification His work impacts criminal investigations by addressing declining police clearance rates for homicides (66%) and sexual assaults (32%) in the U.S., aiming to resolve over 250,000 accumulated cold cases. Barash combines his academic work with practical forensic experience, having served over nine years as a forensic DNA officer (Chief Inspector) in the Israel Police, analyzing evidence in approximately 600 criminal cases.

Yiqun Chenمشاهده پروفایل

استادیار

• Biostatistics
• Artificial Intelligence in Medicine
• Social Network Analysis
• +۵ مورد دیگر

Yiqun Chen, PhD is an Assistant Professor in the Department of Biostatistics at Johns Hopkins University's Bloomberg School of Public Health. With a PhD obtained in 2022, Dr. Chen maintains an active research program at the intersection of biostatistics, artificial intelligence, and public health applications. Dr. Chen's research spans multiple high-impact domains including: Statistical methodology development for selective inference and graph-based models AI applications in single-cell biology and biomedical engineering Social network analysis in HIV transmission contexts Clinical risk stratification systems for dermatology and oncology Recent publication trends show strong focus on methodological innovations in statistical computing (2023-2025) with increasing applications in biomedical domains. The work demonstrates particular expertise in adapting cutting-edge AI techniques like ChatGPT for biological data analysis while maintaining rigorous statistical foundations. Dr. Chen's research has garnered attention across multiple platforms including 80+ X posts, Bluesky references, and news coverage, indicating significant impact in both academic and public spheres.

• Diabetes
• Beta Cell Research
• Developmental Biology
• +۶ مورد دیگر

Dr. Mostafa Bakhti serves as Group Leader of the Beta Cell Development and Regeneration research group at the Institute of Diabetes and Regeneration Research (IDR), part of the Helmholtz Diabetes Center within Helmholtz Munich. His work focuses on uncovering molecular mechanisms of insulin-producing β-cell formation and function to develop novel diabetes therapies through regenerative approaches. His academic background includes a Bachelor in Biology from the University of Isfahan, a Master in Cell and Molecular Biology from the University of Tehran, and a PhD in Cell Biology from the Max Planck Institute for Experimental Medicine in Göttingen. Bakhti's research integrates developmental biology, endocrinology, and stem cell technologies to investigate pancreas development, islet biology, and human endocrinogenesis. His team employs mouse genetics, 3D organoids, confocal microscopy, multi-omics, and gene editing to decipher molecular programs governing endocrine differentiation and morphogenesis, aiming to translate discoveries into diabetes treatments and tissue engineering solutions. His publication portfolio demonstrates consistent innovation in beta cell regeneration, with key contributions including the discovery of Synaptotagmin-13's role in islet morphogenesis, CD81 as a beta cell maturation marker, and pharmacological strategies for diabetes remission. These works emphasize single-cell technologies and 3D modeling to advance understanding of pancreatic development and disease. Notable recognitions include: DZD NEXT Young Talent Program (2022) DZD NEXT grant (2018) Paula and Richard von hertwig Award (2018) Iranian National Institute of Elites membership (2007) Bakhti secures competitive funding through the German Center for Diabetes Research (DZD), including the DZD NEXT Young Talent Program which supports postdoctoral researchers for beta cell regeneration projects. He actively collaborates within the Helmholtz Diabetes Center and DZD networks to advance translational diabetes research. His laboratory at Helmholtz Munich's Campus Neuherberg operates as a specialized unit developing cutting-edge methodologies for studying pancreatic endocrinogenesis, with ongoing projects focused on human stem cell differentiation, islet morphogenesis mechanisms, and regenerative therapy development for diabetes.

Mizuho Iwaiharaمشاهده پروفایل

استاد

• Database Systems
• Web Information Systems
• Text Mining
• +۳ مورد دیگر

Professor Mizuho Iwaihara is affiliated with Waseda University's Faculty of Science and Engineering and Graduate School of Information, Production, and Systems. Her research focuses on database systems, web information retrieval, text mining, security/privacy, and social media analysis. She has led significant projects on Wikipedia edit history analysis, knowledge graph construction, and privacy-preserving frameworks. Key research areas: Database Query Processing, Web Information Systems, Text Mining, Knowledge Management, Social Media Her recent publications address semantic analysis of collaborative content, topic evolution tracking, and privacy behavior modeling. Over 85 papers with 349 citations reflect her impact in database and social media research. Scientific achievements include: Best Demo Award (2014) for WikiReviz Best Paper Award (2008) at IFIP e-Business Conference EC-Web2006 recognition Grants from Japan Society for the Promotion of Science (JSPS) span multiple projects on knowledge graph development, social content analysis, and privacy-preserving systems. She supervises numerous graduate students and leads the Data Engineering Laboratory at Waseda University.

• Life, Health and Medical Informatics
• Bioinformatics
• Genomics
• +۵ مورد دیگر

Takahiro Mimori is a Researcher at the Faculty of Science and Engineering , Waseda University. His work focuses on Life, Health and Medical Informatics with extensive contributions to genomic data analysis , phylogenetic inference , and computational methods for healthcare applications . Current affiliation: Waseda Research Institute for Science and Engineering Previous roles: RIKEN (2019-2025), Tohoku Medical Megabank Project His research spans algorithms for structural variant detection , HLA typing , and multiomics integration , particularly in maternal and cancer studies. Recent work includes computational frameworks for microdroplet barcode design and hyperbolic phylogenetic tree embeddings . Key tools developed: HLA-VBSeq, STR-realigner, AP-SKAT, iSVP Major projects: Tohoku Medical Megabank, Maternity Log Study His publications demonstrate expertise in machine learning applications to medical diagnostics and genomic variant analysis , with over 29 peer-reviewed papers and 796 Scopus citations.