جست‌وجوی استاد و پژوهشگر

Hanna Meyerمشاهده پروفایل

استاد

• Remote Sensing
• Spatial Modeling
• Machine Learning for Spatial Data
• +۷ مورد دیگر

Prof. Dr. Hanna Meyer is a Professor of Remote Sensing and Spatial Modeling at the Institute of Landscape Ecology, University of Münster (WWU). She leads the Remote Sensing and Spatial Modeling Group and is actively involved in teaching and research in geospatial data science, machine learning, and environmental monitoring. Her work is supported by multiple national and international funding bodies including the DFG, EU Horizon Europe, and internal university grants. B.Sc. Geography, Philipps University Marburg (2007–2010) M.Sc. Environmental Geography, Philipps University Marburg (2010–2013) Ph.D., Philipps University Marburg (2014–2018) Her research focuses on machine learning methods for spatial data, optical remote sensing, environmental monitoring, and spatio-temporal modeling. She develops and applies advanced statistical and machine learning techniques to satellite and drone-based data for mapping ecological variables, land cover, and environmental change. Her work emphasizes methodological rigor, model transferability, and uncertainty quantification in spatial predictions. The recent publications reflect a strong trend in developing and validating machine learning models for environmental mapping, with applications in soil science, peatland hydrology, forest ecology, and polar climatology. She contributes both to theoretical advancements in spatial model validation and to practical software tools in R for geospatial analysis. She has secured competitive research funding for projects such as PRISM, Carbon4D, Uebersat, and BEyond, focusing on spatial pattern recognition, carbon modeling, AI model transferability, and biodiversity prediction. She teaches courses on remote sensing, spatial data analysis with R, and environmental modeling, and supervises students and early-career researchers. She collaborates widely with researchers across institutions and leads a dynamic research group including postdoctoral researchers and students. Her open-source contributions, particularly R packages like CAST and uavRst, support reproducible research in geospatial machine learning.

• Human Genetics
• Animal Genetics
• Genomic Data Analysis
• +۵ مورد دیگر

Jeffrey R. O'Connell, DPhil is an Associate Professor in the Department of Medicine at the University of Maryland School of Medicine, with a secondary appointment in Epidemiology & Public Health. He has held his position at the University of Maryland since 2002, initially as an Assistant Professor (2002-2008) before being promoted to Associate Professor in 2009, a position he continues to hold. Additionally, he maintains a research appointment at the USDA Animal Genetics Improvement Laboratory where he has worked since 2007. Dr. O'Connell's educational background includes a BS in Mathematics from the University of Delaware (1979), an MS in Mathematics from Drexel University (1984), an MS in Computer Science from Drexel University (1993), and a D.Phil in Mathematical Biology from Oxford University (2000). His academic journey reflects a strong foundation in mathematics and computational methods that underpins his research career. Dr. O'Connell's research focuses on developing, implementing and applying methods to analyze genomic data in large pedigree and population data. His primary area has been human genetics, which expanded to animal genetics in 2007 with his appointment at the USDA Animal Improvement Laboratory. He is the developer of MMAP (Mixed Models for Analysis of Pedigree/Populations), a comprehensive software package implementing analysis options for genome-wide association, variance components estimation, linkage analysis, genotype imputation, genomic prediction, haplotyping, and mega analysis. His work extensively utilizes large pedigree data such as the Old Order Amish and Holstein cattle. Dr. O'Connell collaborates with numerous research consortia including the Genetics of Liver Disease (GOLD), the Genetic Factors for Osteoporosis (GEFOS), the Cohorts of Heart and Aging Research in Genetic Epidemiology (CHARGE) musculoskeletal, adiposity, and lipid groups, and the Gene-by-Lifestyle Interaction consortium. He is also a member of the TOPMed whole genome sequencing effort and has been heavily involved in developing analytical tools for cloud-based computing to run genome-wide association and rare variant analysis, generalized linear mixed models for binary and threshold traits, and multi- and correlated trait models for analysis of large omics data sets. His research has resulted in numerous high-impact publications spanning statistical genetics, genomic analysis methods, and applications to both human and animal genetics. His work bridges computational methodology development with practical applications in genetic epidemiology and animal breeding.

• Climate Dynamics
• Numerical Weather Prediction (NWP)
• Atmospheric Modeling
• +۳ مورد دیگر

Professor Eigil Kaas is affiliated with the Niels Bohr Institute at the University of Copenhagen . His work spans climate dynamics , numerical weather prediction (NWP) , and atmospheric modeling . As former Section Head of Climate and Computational Geophysics , he leads research on climate-chemistry coupling and sea ice impacts. Education : MSc (1987) and PhD (1993) in Meteorology from University of Copenhagen Research Focus : Climate dynamics and physics Numerical methods in atmospheric models Machine learning for weather prediction Arctic sea ice-climate interactions Thunderstorm electricity and radiation Coupled atmosphere-ocean modeling Article Trends : Recent work combines neural networks with radiative transfer optimization Focus on storm dynamics and gamma-ray flashes Extreme precipitation modeling under climate change Pioneering tidal flow studies in Faroe Island fjords Teaching Legacy : Instructor of Atmospheric Physics and Dynamical Meteorology courses Developed zonally averaged climate model for educational use Mentored 12 PhD/MSc students with DMI/ECMWF collaborations Professional Roles : Chairman of BFI Group 28 (Geosciences & Climate) Scientific Advisory Committee member at ECMWF Project lead in EU ENSEMBLES and PEGASOS initiatives

Jagjit Singh Sraiمشاهده پروفایل

استاد

• Supply Chain Management
• Digital Manufacturing
• Advanced Manufacturing
• +۵ مورد دیگر

Dr. Jagjit Singh Srai is a Director of Research (Professor equivalent) in the Department of Engineering at the University of Cambridge and Head of the Centre for International Manufacturing at the Institute for Manufacturing (IfM). His research focuses on the analysis, design, and operation of international manufacturing supply networks, with an emphasis on advanced production and digital technologies. He leads IfM’s Digital Supply Chains Consortium, collaborating with global firms like Caterpillar, IKEA, and Unilever, and teaches on the course 'Digitalisation of End-to-End Supply Chains.' Dr. Srai is Co-Chair of the World Economic Forum Council on the Future of Advanced Manufacturing and Production and contributes to UNCTAD’s annual industrial development reports. His interdisciplinary work spans healthcare and food/FMCG sectors, supported by grants including EPSRC Made Smarter Innovation projects and Innovate UK Smart Pharma Supply Chain. He has held senior roles at Unilever prior to academia, managing complex supply chains across global operations. Expertise Areas: Supply Chain Management, Digital Manufacturing, Sustainable Supply Chains, Advanced Manufacturing, Network Design, Food Security. Selected Scientific Awards: Fellow, Institute of Chemical Engineers (FIChemE) Chartered Engineer (CEng) Highly Commended Paper Award, Emerald Publishers (2016)

Christopher O'Neillمشاهده پروفایل

دانشیار

• Numerical Semigroups
• Algebraic Structures
• Computational Algebra
• +۵ مورد دیگر

Christopher O'Neill is an Associate Professor in the Mathematics and Statistics Department at San Diego State University. Previously, he held postdoctoral positions as an Arthur J. Krener Assistant Professor at the University of California Davis and a Visiting Assistant Professor at Texas A&M University. His primary research focuses on numerical semigroups, Kunz polyhedron, and related algebraic structures. Dr. O'Neill frequently incorporates computational tools like Sage, GAP, and Macaulay2 into his mathematical research, bridging theoretical mathematics with practical software implementation. Dr. O'Neill has developed numerous software packages for mathematical research including acm-sage for arithmetical congruence monoids, affinesgps-sage for cone decompositions of affine semigroups, and kunzpolyhedron for the Kunz polyhedron. His computational work demonstrates a strong integration of theoretical mathematics with practical software development. He is actively involved in academic service including organizing the Semigroups, Generating functions, and Polytopes (SGPs) Research Seminar, serving on the SDSU Mathematics and Statistics Department Colloquium committee, and participating in conferences focused on algebra and discrete mathematics. Dr. O'Neill advises undergraduate researchers through the SDSU Mathematics REU program and has demonstrated commitment to supporting underrepresented students in mathematics through his involvement with the Underrepresented Students in Topology and Algebra Research Symposium.

Gal Elidanمشاهده پروفایل

دانشیار

• Probabilistic Graphical Models
• Machine Learning
• Copula Models
• +۳ مورد دیگر

Gal Elidan is an associate professor in the Department of Statistics at the Hebrew University of Jerusalem, Faculty of Social Sciences. His work bridges statistical theory with practical applications in machine learning and artificial intelligence. Dr. Elidan's research focuses on Probabilistic Graphical Models , with emphasis on fundamental representations and methods for inference and learning in large-scale domains. His work addresses structure learning, discovery of hidden variables, transfer of knowledge between related tasks, and nonlinear high-dimensional representations of continuous or hybrid distributions. He also applies these techniques to real-life applications including computational biology, machine vision, and medical informatics, with recent focus on principled techniques for medical diagnosis. His publication record shows consistent contributions to top machine learning venues with a recent emphasis on copula models for high-dimensional data. His work demonstrates a progression from foundational graphical model research to specialized applications in medical informatics and time series analysis. Scientific recognition includes: Runner-up for Best Student Paper Award at UAI 2004 Best Paper Award at ISMB 2001 Dr. Elidan serves on the editorial board of the Journal of Artificial Intelligence Research (JAIR) and has contributed to the development of important software tools including FastInf, a library for large-scale inference of graphical models, and LibB, a package for Bayesian network inference and learning. He has organized significant academic events including the NIPS 2011 Workshop on Copulas in Machine Learning and co-organized the PASCAL2 Probabilistic Inference Challenge and UAI 2010 Approximate Inference Challenge.

Wayne Wobckeمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Science
• Machine Learning
• +۷ مورد دیگر

Associate Professor Wayne Wobcke is a faculty member in the School of Computer Science and Engineering at the University of New South Wales (UNSW), where he has been employed since 2002. His academic career includes previous positions at the University of Sydney until 1998, British Telecom Labs in the UK for three years, and the University of Melbourne for one year. He holds a PhD in Computer Science from the University of Essex (1989), an MSc from the University of Queensland (1985), and a BSc (Hons) in Mathematics/Computer Science from the University of Queensland (1984). Dr. Wobcke's research spans both theoretical and practical aspects of artificial intelligence and data science. His work encompasses intelligent agents, data mining, agent-based modeling, dialogue management, personal assistants, recommender systems, and computational social science. He has collaborated extensively with industry through three Cooperative Research Centres (Smart Internet Technology CRC, Smart Services CRC, and Data to Decisions CRC), where he served as a Programme Manager and Project Leader for over 10 years. Notable achievements include developing a voice-controlled mobile application for email and calendar interaction (a precursor to Apple's Siri) and deploying a people-to-people recommender system for online dating on one of Australia's largest dating sites. His recent research focuses on data science in humanitarian contexts and machine learning applications in official statistics, conducted in collaboration with BPS (Statistics Indonesia) and STIS (Politeknik Statistika, Indonesia). His publication record shows a consistent trajectory of impactful research, with recent work concentrating on poverty targeting, domain adaptation, natural language processing for recommender systems, and political opinion mining. Scientific Awards: Best Paper Nomination, 11th Workshop on Argument Mining (2024) UNSW Arc Postgraduate Research Supervisor Award (2017, 2018) AAAI Deployed AI Application Award, Twenty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (2014) Best application paper runner up, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (2013) Dr. Wobcke has successfully supervised numerous research students, with Irwan Rahadi currently working on 'Causal Modelling and Machine Learning for Official Statistics'. His grant portfolio includes significant funding from the Australian Research Council and various Cooperative Research Centres, totaling over $3.7 million since 2003. He teaches COMP9414 Artificial Intelligence and COMP9727 Recommender Systems at UNSW.

Amy G Froelichمشاهده پروفایل

دانشیار

• Statistics Education
• Psychometrics
• Educational Assessment
• +۳ مورد دیگر

Amy G Froelich serves as Associate Professor and Director of Undergraduate Studies in the Department of Statistics at Iowa State University, where she has taught over 3,000 students since joining in 2000. Her academic leadership includes curriculum development for undergraduate statistics programs and oversight of degree requirements. Ph.D. in Statistics, University of Illinois, Urbana-Champaign (2000) B.S. in Secondary Mathematics Education, University of Illinois, Urbana-Champaign (1994) Dr. Froelich specializes in statistics education, psychometrics, and educational assessment with particular expertise in categorical data analysis and probability instruction. Her research develops innovative teaching methodologies for introductory statistics courses and focuses on training future secondary mathematics teachers through specialized curriculum design. She investigates how electronic assessments and resampling techniques enhance student learning outcomes in statistical reasoning. Her publication record (2008-2018) demonstrates consistent contributions to statistics pedagogy, emphasizing practical classroom applications, assessment design, and teacher preparation. Key themes include the development of engaging activities (e.g., M&M's weight analysis, iPod randomness testing), psychometric methods for educational measurement, and curriculum reforms for mathematics educators. Waller Education Award from the American Statistical Association (2010) Multiple teaching and advising awards at Iowa State University Dr. Froelich has secured significant research funding from the National Science Foundation, U.S. Department of Agriculture, and U.S. Department of Education to support her statistics education initiatives. As Director of Undergraduate Studies since 2018, she mentors students through curriculum development projects and advises on academic pathways in statistics. Her co-authorship of Business Statistics and Analytics in Practice (9th ed., 2019) extends her impact to textbook development.

Danica M Ommenمشاهده پروفایل

دانشیار

• Forensic Statistics
• Computational Statistics
• Handwriting Analysis
• +۳ مورد دیگر

Danica M Ommen is an Associate Professor at Iowa State University, specializing in forensic statistics and computational methodologies. Her research bridges machine learning, handwriting analysis, and source identification frameworks. Education: Ph.D. in Computational Science and Statistics (2017), M.S. in Mathematics (2014), B.S. in Mathematics (2012), all from South Dakota State University. Affiliations: Chair of the OSAC Statistics Task Group; Vice-Chair of the ASA Advisory Committee on Forensic Science. Her work focuses on statistical modeling for forensic evidence , particularly in handwriting identification, aluminum powder analysis, and digital device forensics. Recent publications explore interpretable deep learning, synthetic data anchoring, and ensemble methods for likelihood ratios. The 15 most recent articles (2023–2025) span forensic machine learning, handwriting kinematics, multi-camera smartphone identification, and Bayesian frameworks. Keywords include Forensic Science , Machine Learning , and Computational Statistics , with subfields like Score-Based Likelihood Ratios and Smartphone Forensics .

• Civil Engineering
• Climate Science
• Machine Learning
• +۶ مورد دیگر

Brennan Bean is an Assistant Professor in the Mathematics and Statistics Department at Utah State University's College of Arts & Sciences. His work focuses on geospatial modeling, statistical methods for extreme weather analysis, and machine learning applications in structural and environmental engineering. Recent publications highlight expertise in snow load prediction, Bayesian entropy, and interdisciplinary data science. Notable contributions include optimizing design methods for insulated concrete wall panels and addressing deployment challenges for ML models in engineering contexts. Research trends span geospatial data integration, climate change impact assessments, and educational interventions in STEM. Key subfields include ground snow load mapping, extreme value statistics, climate downscaling, and high-dimensional ecological modeling.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +۳ مورد دیگر

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

Kate M. Lesciottoمشاهده پروفایل

استادیار

• Forensic Anthropology
• Sex Estimation
• Biomechanics
• +۶ مورد دیگر

Kate M. Lesciotto, JD, PhD, D-ABFA is an Assistant Professor at the Center for Anatomical Sciences , affiliated with the College of Biomedical and Translational Sciences at UNT Health in Fort Worth, TX. Her work bridges forensic anthropology, biomechanics, and legal systems, focusing on skeletal analysis, trauma interpretation, and the Daubert standard's impact on forensic science. Education PhD in Anthropology, Pennsylvania State University (2020) MS in Forensic and Biological Anthropology, Mercyhurst University (2015) Juris Doctor (JD), Washington University in St. Louis (2009) BS in Biological Science and Zoology, Colorado State University (2004) Her research centers on forensic anthropology , with three primary areas: (1) sex estimation from skeletal remains using metric/morphological data and statistical models; (2) biomechanics of skeletal trauma via experimental bending models and medical imaging; and (3) the intersection of forensic anthropology and legal systems , particularly Daubert standard admissibility criteria. Recent projects include NSF/National Institute of Justice-funded software development for sex estimation and collaborations on cranial bone development studies. Dr. Lesciotto's publications reflect expertise in forensic identification , methodological validation , and science communication . She has contributed to software tools, biomechanical protocols, and educational outreach initiatives like March Mammal Madness. Her 2021-2025 work demonstrates consistent focus on improving forensic accuracy, legal standards, and developmental imaging techniques. Scientific Awards ABFA Board-Certified Forensic Anthropologist (2023) As a practicing forensic anthropologist, she consults for Texas counties and trains law enforcement. At UNT Health, she directs Graduate Structural Anatomy (PHAN 5301) and teaches gross anatomy to medical, physical therapy, and physician assistant students. Her work integrates biomedical research , legal analysis , and pedagogical innovation , reflecting interdisciplinary expertise in forensic science.

Jürgen Hesserمشاهده پروفایل

استاد

• Medical Imaging
• Computational Oncology
• Inverse Problems in Science
• +۷ مورد دیگر

Jürgen Hesser is a Professor at the Mannheim Medical Faculty , Heidelberg University, specializing in Experimental Radiotherapy and Medical Imaging . His research focuses on solving inverse problems in imaging, particularly for CT reconstruction , brachytherapy planning , and low-dose imaging . Current affiliations: Clinic for Radiotherapy and Radiooncology, Mannheim University Hospital Collaborative ties: Interdisciplinary Center for Scientific Computing (IWR) and Center for Bioinformatics (ZITI) at Heidelberg University Research interests center on anisotropic total variation techniques for medical and industrial applications, including MR-guided interventions and real-time radiation therapy . His work has led to a 1000x speed improvement in brachytherapy planning algorithms. Recent publications highlight expertise in image reconstruction (CT/X-ray), noise optimization , machine learning for cancer classification, and big data management solutions. His methods are applied to both clinical and industrial imaging challenges. Additional contributions include scientific data infrastructure development and variance stabilization techniques for medical sensors. The research group maintains strong interdisciplinary links with Physics, Mathematics, and Computer Science faculties.

• Applied Statistics
• Biostatistics
• Time Series Analysis
• +۳ مورد دیگر

Dr. Carolina Euan is a Lecturer in Statistics at the School of Mathematical Sciences, Lancaster University, with affiliations to the Data Science Institute and STOR-i Centre for Doctoral Training. Her research focuses on time series analysis, spatio-temporal modeling, and their applications in environmental data science and brain data analysis. School of Mathematical Sciences Data Science Institute STOR-i Centre for Doctoral Training Biostatistics Research Group Centre of Excellence in Environmental Data Science Her work spans multiple disciplines: Environmental Data Science: Marine heatwaves, solar irradiance modeling, particle number size distribution Brain Data Analysis: Neural connectivity, brain signal clustering, coherence-based inference Statistical Methodology: Spatio-temporal extremes, spectral estimation, functional data analysis Recent publications demonstrate expertise in: 2025: Neural connectivity modeling, functional data analysis, particle distribution 2024: Virtual collaboration frameworks, precipitation regime modeling, spectral estimation 2023: Brain signal clustering, source apportionment, directional wave spectra Supervision includes: Kajal Dodhia: STOR-i (extreme sea temperatures) Jordan Hood: Bayesian modeling (COVID prevalence) Carla Pinkney: STOR-i

Florian Peinمشاهده پروفایل

مدرس

• Changepoint Regression
• Anomaly Detection
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Florian Pein is a Lecturer in Statistics at the School of Mathematical Sciences, Lancaster University . His research focuses on changepoint regression, anomaly detection, and applied projects linked to artificial intelligence, machine learning, and time series analysis. Current Position : Lecturer in Statistics Institution : Lancaster University School : School of Mathematical Sciences Florian's research addresses foundational challenges in statistical modeling of heterogeneous data, multiscale inference, and computational electrophysiology. His work bridges theoretical statistics with practical applications, particularly in analyzing ion channel recordings and developing open-source software tools like the stepR package. He has published extensively on changepoint detection, non-parametric methods, and noise modeling in time series. Notable trends in his publications include: Integration of changepoint regression with machine learning frameworks Development of robust cross-validation techniques for time series Advancements in multiscale and multiresolution analysis Applications in biophysics, including ion channel recordings and antibiotic resistance studies Focus on high-dimensional and non-parametric statistical methods Creation of computational tools for statistical inference Florian supervises PhD students in changepoint-related research and applies statistical methods to interdisciplinary challenges in data science and biophysics.