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Markus HolzView profile

Professor

• Operations management
• Process optimization
• Cost reduction
• +7 more

Prof. Dr. Markus Holz is a professor at the Department of Economics at Anhalt University of Applied Sciences. He serves as Vice President for Research, Transfer and Sustainability. His research focuses on operations management, process optimization, cost reduction, lean management, system analysis of macrologistic processes, sustainability concepts in logistics, logistics of energy sources and raw materials, digitalization in logistics, ground-based logistics processes in the aviation sector, and drone logistics. Dr. Holz coordinates and supervises the mobility semester and acquisition and implementation of third-party funded projects. His recent publications focus on multivariate statistical analysis of women's empowerment in labor market, environmental sustainability assessment of green hydrogen production, land suitability analysis for green ammonia units, advancement of power-to-x strategies, wind-to-hydrogen production via electrolysis, and photovoltaic module efficiency improvements. Dr. Holz teaches courses including Strategic Logistics Management, Scientific Work, Project Logistics Management, Co-opter Management in Logistics and Air Transport, International Cargo Air Transport, Innovation Management, Strategic Corporate Management, Leadership & Management Skills, Management Game, Reporting & Controlling, and Cost and Performance Accounting.

Javier CabreraView profile

Professor

• Biostatistics
• Data Mining
• Clinical Trial Methodology
• +5 more

Javier Cabrera is a Professor in the Department of Statistics at Rutgers University with a joint affiliation at the Cardiovascular Institute. He holds a Ph.D. from Princeton University and is recognized as a Fulbright Scholar. His office is located at Hill Center 471, 110 Frelinghuysen Road, Piscataway, NJ. His research focuses on: Biostatistics and clinical trial methodology Data mining for functional genomics and DNA/protein arrays Statistical computing, machine vision, and high-dimensional data analysis Cardiovascular health applications using statistical modeling Recent publications (2022-2025) demonstrate strong emphasis on: Novel statistical methods for medical/biological data Machine learning applications in diagnostics and genomics Clinical risk modeling and epidemiological studies Big data reduction techniques and computational efficiency He frequently publishes in interdisciplinary collaborations at the intersection of statistics, biomedicine, and computational science. Awards: Fulbright Scholar He collaborates extensively with the Cardiovascular Institute, contributing statistical expertise to research on cardiovascular outcomes, disease risk modeling, and clinical data analysis.

Shuhab D KhanView profile

Professor

• Remote Sensing
• Geospatial Research
• Hyperspectral Imaging
• +5 more

Dr. Shuhab D Khan is a Full Professor in the Department of Earth and Atmospheric Sciences at the University of Houston's College of Natural Sciences and Mathematics. He leads the Remote Sensing and Geospatial Research Laboratory (GeoRS), focusing on advanced geospatial techniques for earth science applications. His research integrates remote sensing, geospatial analytics, and field geology to address critical geological and environmental challenges. PhD in Geoscience from The University of Texas at Dallas (1996-2001) Dr. Khan specializes in hyperspectral imaging, land subsidence analysis, tectonic studies, and rare earth element exploration. His work bridges traditional geological field methods with cutting-edge remote sensing technologies, particularly in developing innovative spectral indices for mineral identification. He has made significant contributions to understanding active tectonics in the Himalayas and hydrogeological systems in North Africa. His recent publications show a strong trend toward multi-scale analysis of geological phenomena, from microscopic mineral identification to regional tectonic studies. He has pioneered techniques using drone-based hyperspectral imaging for rare earth element detection and developed novel approaches for analyzing land subsidence in urban areas using InSAR technology. Dr. Khan actively mentors students through research projects and leads the Remote Sensing and Geospatial Research Laboratory. His laboratory work focuses on developing practical applications of geospatial technologies for resource exploration, environmental monitoring, and natural hazard assessment. He has secured research funding for projects related to coastal erosion monitoring, subsidence analysis, and mineral exploration.

• Latent Variable Modeling
• Statistical Learning
• Algorithmic Fairness
• +4 more

Dr. Gabriel Wallin is an Assistant Professor in Statistics at the School of Mathematical Sciences, Lancaster University. Previously, he held postdoctoral positions at Inria and a Research Fellow role at the London School of Economics and Political Science, where he was supervised by Dr. Yunxiao Chen and Professor Irini Moustaki. His academic journey began with a PhD from Umeå University in 2020 under Professor Marie Wiberg. Education: PhD in Statistics, Umeå University (2020) Supervisor: Marie Wiberg Dr. Wallin's research bridges latent variable modeling and statistical learning, focusing on fairness and interpretability in social, behavioral, and health sciences. Key areas include structural learning of latent models, multivariate outlier detection, model-based clustering, algorithmic fairness in education, and change-point detection for latent factors. His methodology development addresses test score equating, robustness analysis, and sparse loadings in factor models. Recent publications highlight trends in propensity score methods for test equating (2025), latent change-point modeling for educational assessment (2025), and sparse rotation techniques for exploratory factor analysis (2023). Collaborative work spans educational testing, psychometrics, and social data science. Scientific Awards: Best Reviewer Award, Psychometric Society (2025) ODA Early Career International Fellowship (2024) Dr. Wallin supervises PhD student Yawen Ma and actively collaborates with researchers like Yunxiao Chen and Marie Wiberg. He has secured grants from EPSRC, Duolingo, and the ODA, with recent projects on test security and fairness in digital learning environments. His service roles include co-editing Psychometric Society proceedings and associate editorships at Teaching Mathematics and its Applications .

Paolo OliveriView profile

Professor

• Analytical Chemistry
• Chemometrics
• Pharmaceutical Chemistry
• +7 more

Paolo Oliveri is a Full Professor at the Department of Pharmacy (DIFAR) within the University of Genoa. His work focuses on analytical chemistry, chemometrics, and their applications in pharmaceutical, forensic, and environmental sciences. He teaches courses including Analytical Chemistry (CTF), Forensic Analytical Chemistry, and Instrumentation and Quality Control. Research Highlights: He applies chemometric methods and spectroscopic techniques (NIR, TD-NMR) to analyze material properties across diverse domains—from pharmaceutical raw materials to archaeological dating. His recent work explores hyperspectral imaging for ecological studies and multivariate analysis of rare-earth element profiles in soil science. Contact: Available at the Chemistry and Pharmaceutical and Food Technologies Section of DIFAR (Viale Cembrano, 4) or via email at paolo.oliveri@unige.it. Appointments require prior coordination through his email.

• Operations Research
• Multiple Criteria Decision Making
• Data Envelopment Analysis
• +1 more

Dr. Muhammed Maruf is an Assistant Professor at the Faculty of Economics and Administrative Sciences , Kırşehir Ahi Evran University since 2019, with prior roles at Şırnak University (2017-2019) and Dokuz Eylül University (2011-2017). He holds a PhD in Business Administration (2013-2017) and an MSc in Management Science (2011-2013) from Dokuz Eylül University, and a BSc in Business Administration (2002-2005) from Erciyes University. Licenses : YÖKDİL (2017, 82), ÜDS (2011, 71) Administrative Roles : Research Application Center Director (2019-), Deputy Department Head (2019-), Head of Department (2018-), Dean Assistant (2018-), Faculty Council Member (2017-) His research focuses on Operations Research and Multiple Criteria Decision Making , applying advanced methods like CRITIC , MAIRCA , DEA , and Fuzzy Logic to analyze performance in municipal governance, tourism websites, banking transformation, and political participation. Collaborative work spans 13 co-authors, notably with Kadir Özdemir (6 publications) and Bekir Gündoğmuş (5 publications). Key Trends : 15+ journal articles (2021-2025), 4 books, 4 book chapters Methodological Expertise : CRITIC-MAIRCA integration, Fuzzy DEA, Grey System Theory Project Leadership : 2 projects (2020-2021, 2022-2024), 2 as co-researcher Academic Impact : Google Scholar h-index 8 (157 citations), YÖKSİS h-index 6 (365 total points). His 43+ total publications include 28 journal articles and 9 conference proceedings.

• Bayesian statistics
• robust estimation
• information-assisted hypothesis testing
• +3 more

Jordan Bryan is an Assistant Professor of Data Science at the University of Virginia's School of Data Science, specializing in multivariate statistical methods with applications spanning environmental monitoring, high-energy physics, and cancer genomics. He earned his Ph.D. in Statistics from Duke University in 2023 and a B.S. in Mathematics from Stanford University, and currently serves as Secretary of the junior section of the International Society for Bayesian Analysis. Education: Ph.D. in Statistics, Duke University B.S. in Mathematics, Stanford University Research Interests: Dr. Bryan develops advanced methodologies in Bayesian statistics , robust estimation , and information-assisted hypothesis testing , with particular emphasis on integrating auxiliary data sources to enhance statistical power. His work bridges theoretical rigor with real-world applications in genomics, environmental systems, and physics, recently expanding to incorporate large language model-derived information for genomic analysis. Publication Trends: Analysis of his 15 most recent publications (2020-2025) reveals a dual trajectory: foundational contributions to statistical methodology (e.g., multirank likelihood frameworks, subscedastic estimation) and high-impact applications in cancer genomics (functional screens, drug repurposing) and environmental science (source apportionment). A notable 2025 publication pioneers LLM integration for genomic hypothesis testing, signaling a strategic expansion into AI-augmented statistics. Scientific Awards: No specific awards, prizes, or fellowships were documented in the source materials. Advising and Grants: Supported by National Institute of Environmental Health Sciences (NIEHS) and National Heart Lung and Blood Institute (NHLBI) training grants during his UNC Chapel Hill postdoctoral fellowship, Dr. Bryan now leads independent research at UVA. While formal student advising isn't detailed in current records, his extensive publication record suggests active mentorship in collaborative projects.

Daniela BeisserView profile

Professor

• Bioinformatics
• Computational Biology
• Microbial Ecology
• +6 more

Prof. Dr. Daniela Beisser is a Professor at the Department of Engineering and Natural Sciences (FB 8) of the Westphalian University of Applied Sciences in Recklinghausen, Germany. Her research focuses on bioinformatics and biostatistical methods for high-throughput 'omics data, applied to biomedicine and freshwater ecology. She previously held academic roles at the University of Duisburg-Essen (2017–2023) and University Hospital Essen. 2004–2008: B.Sc. in Molecular Biology with Bioinformatics focus, FH Gelsenkirchen 2006–2008: M.Sc. in Molecular Biology with Bioinformatics focus, FH Gelsenkirchen 2008–2011: Ph.D. in Bioinformatics, University of Würzburg Her research integrates computational approaches with experimental data to study molecular responses to environmental stressors in freshwater organisms, genome analyses in human and protists, and proteomic studies in plants. She also investigates eco-evolutionary theories in microorganisms and links biodiversity to ecosystem functions. Recent publications highlight her work on amplicon sequencing (Natrix2 pipeline), metatranscriptomic analysis of microbial communities, and machine learning frameworks for environmental data. She contributes to software tools like TaxMapper and BioNet for reproducible workflows. Best Poster Award, German Conference on Bioinformatics (2013) Travel scholarships: DAAD, DAAD PROMOS, German Symposium on Systems Biology E-fellows.net scholarship (2006–2008) She has supervised numerous PhD, Master’s, and Bachelor’s students on topics such as protist community dynamics , fungal degradation processes , and stressor recovery mechanisms . Her lab collaborates on the CRC 1439 'RESIST' project and develops tools for environmental DNA analysis.

• Sufficient Dimension Reduction
• Variable Selection
• High Dimensional Analysis
• +1 more

CHEN Xin is an Associate Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech), where he has been employed since January 2019. He initially joined the Department of Mathematics before transitioning to his current department in August 2019. Prior to SUSTech, he held academic positions as Assistant Professor at the National University of Singapore (2011-2019) and Syracuse University (2010-2011). Education History: PhD in Statistics from University of Minnesota, USA (2005-2010) MS in Statistics and Applied Probability from National University of Singapore (2001-2003) BS in Mathematics from Nankai University, China (1994-1999) Research focuses on developing statistical methodologies for high-dimensional and complex data analysis. Primary research domains include: sufficient dimension reduction techniques for efficient data representation; variable selection methods for high-dimensional models; robust statistical approaches for complex datasets; and optimization of computational efficiency in statistical estimation. His work consistently addresses theoretical foundations while developing practical implementations for modern data challenges. Publications demonstrate sustained focus on statistical methodology development, particularly in dimension reduction and high-dimensional analysis. Recent works emphasize robust methods for ultrahigh-dimensional data, efficient computation algorithms, and model diagnostics. Research consistently appears in top-tier statistics journals including Biometrika, Journal of the American Statistical Association, and Annals of Statistics. No scientific awards, student advising relationships, or research grants are mentioned in the source materials. Similarly, there is no information available regarding laboratory affiliations or research team composition.

Wolfgang K. SchiefView profile

Professor

• Integrable systems
• Soliton theory
• Differential geometry
• +3 more

Wolfgang K. Schief is a Professor at the School of Mathematics and Statistics, University of New South Wales (UNSW), Sydney, Australia. He previously served as Head of the Department of Applied Mathematics. His academic history includes positions as Professor at Technische Universität Berlin (Germany), Associate Professor and Senior Lecturer at UNSW, and Queen Elizabeth II Research Fellow. His research spans integrable systems, soliton theory, differential geometry (continuous/discrete), continuum mechanics, and general relativity. Key themes include geometric aspects of nonlinear equations, discretization methods, and applications in physics. Recent work emphasizes discrete differential geometry, integrable discretizations, and connections between geometry and physical models. Professor Schief's publications (2015–2019) predominantly explore discrete integrable geometry, surface theory, and geometric mechanics. Trends include advanced discretization techniques, projective/minimal surfaces, circle complexes, and multi-dimensional consistency in heavenly-type equations.

Rancoita Paola Maria VittoriaView profile

Associate Professor

• Biostatistics
• Bioinformatics
• Survival Data Analysis
• +6 more

Rancoita Paola Maria Vittoria is an Associate Professor of Medical Statistics (MED/01) at the Faculty of Medicine and Surgery, Vita-Salute San Raffaele University (UniSR), since 2022. She is actively involved in research activities at the University Center for Statistics for Biomedical Sciences (CUSSB) and serves on the Board of Directors of CUSSB (since 2016) and the Scientific Committee of CeNRI (Center for Nursing Research and Innovation, since 2016). She also participates in academic governance through multiple committees in the International MD Program (since 2020) and the Doctoral School in Public Health at the University of Milan-Bicocca (since 2020). Research Focus : Her work centers on developing and applying statistical methodologies in biostatistics and bioinformatics, including survival analysis with frailty models, repeated measures and clustered data analysis, genomic data integration via Bayesian regression, and stochastic geometry for angiogenesis image analysis. These techniques address clinical challenges like missing data handling and prognostic index identification. Academic Background : PhD in Mathematics and Statistics for Computational Sciences (2010), University of Milan Post-doctoral Researcher (2010-2011), IDSIA/IOSI Switzerland Research Fellow (2012-2013), Statistics SECS-S/01, Faculty of Psychology, UniSR Fixed-Term Researcher (2014-2021), Medical Statistics MED/01, UniSR Scientific Recognition : WHO Statistical Consultant (2022) Task Force Member, Stop TB Partnership (2020) ISBA Travel Award (2010) INdAM Fellowship (2004-2006) Leadership Roles : She served as Treasurer in the Italian Region of the International Biometric Society (2018-2021) and held leadership roles in Società Italiana di Statistica Medica ed Epidemiologia Clinica (2020-2021). Her teaching spans statistics, bioinformatics, and clinical data science across undergraduate and postgraduate programs at UniSR and Milan-Bicocca University.

Ewa SynówkaView profile

Researcher

• Fixed Point Theory
• Nonlinear Analysis
• Graph Theory
• +5 more

Ewa Synówka is a researcher at the Institute of Mathematics, University of Zielona Góra , Poland. Her academic work spans interdisciplinary applications of mathematics and computer science. Research focuses on iterative methods for fixed point problems in Hilbert spaces Contributions to graph theory, particularly coloring models and combinatorial geometry Investigations into nonlinear wave propagation and stochastic equations Applications of computer science in secure data transmission and supply chain privacy She teaches advanced statistical methods, econometric modeling, and multivariate data analysis. Her work also emphasizes modern pedagogy using open-source tools for primary/secondary school outreach.

Sima Siami-NaminiView profile

Lecturer

• macroeconomics
• monetary policy
• time series analysis
• +5 more

Sima Siami-Namini serves as a Lecturer at Johns Hopkins University, teaching in the MS in Applied Economics program with extensive experience in undergraduate and graduate instruction across economics, statistics, and finance disciplines. Her academic credentials include advanced interdisciplinary training: PhD in Applied Economics (minor: Statistics), Texas Tech University, 2020 Master's in Statistics, Texas Tech University, 2022 Master's in Artificial Intelligence (Machine Learning focus), University of North Texas, 2023 Her research program integrates macroeconomic theory with cutting-edge computational methods, specializing in monetary policy analysis, time series econometrics, and AI-driven forecasting. She bridges traditional economic modeling with machine learning applications, particularly in anomaly detection, data visualization, and large language model implementations for economic forecasting. Analysis of her publication trajectory (2020-2024) reveals three dominant research streams: (1) deep learning architectures (LSTM, TCN) for time series forecasting and anomaly detection, (2) monetary policy impacts on income inequality using FAVAR/SVECM models, and (3) natural language processing applications for Federal Reserve communication analysis. Her recent work increasingly incorporates large language models for domain-specific economic analysis and code generation. No documented scientific awards or major honors appear in the available records. She mentors students in the Applied Economics program with emphasis on quantitative research methods, though specific grant funding details remain undisclosed. Her teaching methodology incorporates experiential learning techniques adapted from digital forensics education frameworks. No dedicated research laboratories or institutional teams are referenced in the source materials.

Politis KonstantinosView profile

Professor

• Stochastic developments
• Risk and bankruptcy theory
• Actuarial mathematics
• +3 more

Professor Konstantinos Politis is a distinguished academic in the Department of Statistics and Actuarial Science at the University of Piraeus, where he has served since 2004, progressing from Assistant Professor to his current position as Professor (2025-present). His academic career spans prestigious institutions including the University of Manchester, University of Southampton, and the University of Cambridge where he earned his PhD. Education: B.A. in Mathematics, University of Athens (1989) MSc in Statistics, University of Sheffield, UK (1991) PhD in Statistics, University of Cambridge, UK (1997) Professor Politis specializes in stochastic processes with particular focus on ruin theory, risk analysis, and actuarial mathematics. His research examines complex probability models related to insurance risk, surplus processes, renewal theory, and failure rate analysis. He has made significant contributions to understanding the mathematical properties of insurance risk models, particularly in the Sparre Andersen framework and compound Poisson processes. His work bridges theoretical probability with practical applications in actuarial science and financial risk management. His extensive publication record demonstrates consistent contributions to leading journals in probability and actuarial science. The research trajectory shows evolution from foundational work on ruin theory and risk models toward more sophisticated analyses of renewal processes, failure rate functions, and stochastic bounds. His recent work focuses on precise mathematical characterizations of recurrence times, convolution properties, and monotonicity in stochastic systems. Professor Politis has co-authored significant textbooks including Introduction to Probability: Models and Applications (2019) and Introduction to Probability: Multivariate Models and Applications (2021) with Balakrishnan and Koutras, as well as his Greek-language work Introduction to the Theory of Collective Risk (2012, 2nd ed. 2016). In teaching, Professor Politis has contributed to both undergraduate and graduate education, offering courses including Probability I, Special Topics in Probability, Variance Analysis, Loss Distributions, Generalized Linear Models, and Risk Theory II. His academic journey reflects a deep commitment to advancing statistical theory while maintaining practical relevance to actuarial science and risk management.

Benedikt EhingerView profile

Assistant Professor

• Computational Cognitive Science
• Visual Cognitive Science
• EEG Analysis
• +5 more

Benedikt Ehinger is a Tenure-Track Professor for Computational Cognitive Science at the Stuttgart Center for Simulation Science (SC SimTech) and the Institute for Visualization and Interactive Systems (VIS) at the University of Stuttgart. His research bridges cognitive neuroscience, computational modeling, and visualization techniques to understand visual perception and decision-making processes. Education 2018: PhD in Cognitive Science from University of Osnabrück with thesis "Predictions, Decisions and Learning in the visual sense" 2013: Master of Science in Cognitive Science from University of Osnabrück with thesis "Filling in Blind-Spots: A psychophysical and an EEG study" 2011: Bachelor of Science in Cognitive Science from University of Osnabrück with thesis "Electrophysiological Correlates of Category Learning" Research Interests Ehinger's research focuses on the intersection of visual cognitive science, computational modeling, and neuroimaging techniques. His work primarily investigates predictive coding mechanisms in visual perception, statistical learning in visual scenes, eye movement control, method development for combined EEG and eye-tracking analyses, visual completion phenomena like the blind spot, and category learning and neural plasticity. His approach combines behavioral experiments, EEG recordings, eye-tracking, and advanced statistical modeling to uncover the computational principles underlying human visual cognition. Publication Trends Ehinger's publication record shows a clear evolution from foundational work on visual perception and category learning toward methodological innovations in neuroimaging analysis. His early work focused on visual completion phenomena, category learning, and melanopsin modeling. More recently, he has pioneered techniques for analyzing combined EEG and eye-tracking data, developing toolboxes like "unfold" that address critical challenges in temporal overlap correction and regression-based analysis. His research demonstrates a consistent thread of applying computational approaches to understand visual cognition while simultaneously advancing the methodological toolkit of cognitive neuroscience. Scientific Contributions Development of the "unfold" toolbox for overlap correction and regression-based EEG analysis Creation of the EEGVIS toolbox for EEG visualization Establishment of comprehensive eye-tracking test batteries for validating mobile eye-tracking devices Innovative approaches to modeling fixation durations and eye movement patterns Research Environment Ehinger leads the Computational Cognitive Science group within the Institute for Visualization and Interactive Systems at the University of Stuttgart. His work is situated at the intersection of cognitive science, neuroscience, and computer science, collaborating with researchers across these disciplines. His lab utilizes behavioral experiments, EEG, eye-tracking, and computational modeling to investigate visual cognition, with emphasis on open science practices and methodological transparency.