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Alex KoiterView profile

Associate Professor

• Soil Erosion
• Watershed Management
• Sediment Dynamics
• +5 more

Dr. Alex Koiter is an Associate Professor in the Department of Geography & Environment at Brandon University, Manitoba, Canada. His research focuses on soil erosion, watershed management, and sediment dynamics, with an emphasis on sediment fingerprinting techniques and agricultural land management practices. He holds a Ph.D. from the University of Northern British Columbia (2016) and has published extensively on topics such as phosphorus mobilization, riparian zone management, and the impacts of climate change on agricultural systems. Koiter is affiliated with the Rural Development Institute (RDI) and leads projects addressing excess moisture management in Manitoba's agricultural regions. His work integrates field studies, geochemical analyses, and community engagement to address environmental sustainability challenges. Affiliations: Brandon University (2017–present), Rural Development Institute (RDI) Education: B.Sc. (Environmental Science, University of Guelph, 2006); M.Sc. (Soil Science, University of Manitoba, 2008); Ph.D. (Natural Resources & Environmental Studies, UNBC, 2016) Research Interests: Koiter’s work spans sediment source identification, riparian zone ecology, and the impacts of extreme weather on agricultural productivity. He uses geochemical tracers, radionuclides, and spectral analysis to trace sediment origins in watersheds like Tobacco Creek and La Salle River. Recent projects include evaluating cattle grazing as a tool for phosphorus management and assessing the economic and ecological consequences of excess moisture events in Manitoba. Publications & Awards: Over 30 peer-reviewed articles since 2007, including high-impact work on sediment fingerprinting (e.g., Journal of Soils and Sediments , Geoderma ). Notable awards include the Governor General’s Academic Gold Medal (2017) and the Alexander Graham Bell Canada Graduate Scholarship (2010–2013). Grants & Advising: Principal Investigator for the Excess Moisture Management project (funded by Manitoba Wheat & Barley Growers Association) and advisor to multiple graduate students and research assistants. His lab focuses on field experiments and interdisciplinary collaboration with experts from the Universities of Manitoba and Saskatchewan.

Carlos CinelliView profile

Assistant Professor

• Causal Inference
• Statistics
• Machine Learning
• +4 more

Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington. He is also a Data Science Fellow at the eScience Institute and Affiliate Faculty at the Center for Statistics and the Social Sciences. He obtained his Ph.D. in Statistics from the University of California, Los Angeles, where he was advised by Chad Hazlett and Judea Pearl. His research focuses on developing new causal and statistical methods for transparent and robust causal claims in empirical sciences. Key interests include: Causal inference challenges in social and health sciences Intersections of causality with machine learning and AI Sensitivity analysis for omitted variable bias Robust statistical methods for observational studies Instrumental variables and Mendelian randomization Generalizability of experimental findings His publications demonstrate a consistent focus on developing practical sensitivity analysis tools and advancing causal methodology, with recent work emphasizing applications in machine learning and econometrics. Honors include: Best Paper Award at SBE 2024 in Econometrics UCLA Dissertation Year Fellowship (2020) He actively advises PhD students and has received research funding from: NSF/MMS Royalty Research Fund He leads development of several open-source software packages for sensitivity analysis and maintains active collaborations with researchers at UCLA and other institutions.

Armeen TaebView profile

Assistant Professor

• Selective Inference
• Multiple Testing
• Graphical Models
• +7 more

Armeen Taeb is an Assistant Professor in the Department of Statistics at the University of Washington . Previously, he was a postdoctoral fellow at ETH Zürich under the ETH Foundations of Data Science, mentored by Peter Bühlmann. He earned his PhD in Electrical Engineering at Caltech under Venkat Chandrasekaran's supervision. Research Interests : His work bridges optimization and statistics , focusing on Graphical and latent-variable modeling Provably optimal causal model learning False positive error control in non-traditional settings Domain adaptation Applications in physical sciences Article Trends : His publications span causal inference (2022-2025), graphical models (2017-2025), convex optimization (2018-2025), and statistical robustness (2020). Recent work (2025) addresses extremal graphical modeling and selective inference challenges. Scientific Awards : ETH Zürich Foundations of Data Science Postdoctoral Fellowship (2019-2021) Caltech Resnick Institute Fellowship (2016-2018) W. P. Carey & Co. Prize for Applied Mathematics (2020) Caltech Graduate Fellowship (2013-2014) Grants : National Science Foundation DMS-2413074 (PI), University of Washington Royalty Research Fund (PI). Service : President of the Institute of Mathematical Statistics New Researcher Group; co-organized IMS New Researchers Conferences (2024-2025).

• Cancer Prevention
• Nutritional Biochemistry
• Phytochemicals
• +3 more

Weiqun (George) Wang is a Professor in the School of Health Sciences within the College of Health and Human Sciences at Kansas State University. Based in Justin Hall, he teaches graduate courses including Nutrient Metabolism (HN 620) and Micronutrient Metabolism (HN 812), with research focused on dietary strategies for cancer prevention through molecular mechanisms of body weight control and phytochemical action. His work bridges nutritional science, food technology, and molecular oncology to develop functional foods for chronic disease management. Dr. Wang earned his educational foundation in China with a B.S. in Biochemistry from Nanjing University (1983) followed by a Ph.D. in Animal Physiology and Biochemistry from Nanjing Agricultural University (1990), establishing expertise in biochemical and physiological research methodologies. His research program investigates how whole grain phytochemicals—particularly from sorghum, wheat bran, and corn—modulate cancer pathways and metabolic health. Key areas include: molecular mechanisms of ferulic acid in colon cancer inhibition, anthocyanin-enriched bioengineered foods for colorectal prevention, and the role of β-carotene oxygenase 2 in mitochondrial function. He examines how agricultural practices (e.g., high tunnel coverings, light quality) influence phytochemical content in crops, translating these findings into functional food applications with antioxidant and anti-inflammatory benefits. Analysis of his 15 most recent publications (2022-2025) reveals a dominant focus on plant-based cancer prevention, particularly colorectal cancer mechanisms. His work integrates in vitro cancer cell models with food science innovations like encapsulation technology, while contributing methodological advances in longitudinal data analysis for nutritional genomics. This interdisciplinary approach spans food chemistry, molecular biology, and computational statistics to develop evidence-based dietary interventions. Scientific Awards: No specific awards, fellowships, or medals were documented in the source materials. Advising and Grants: While no named graduate students or postdoctoral researchers were listed, Dr. Wang's instruction of advanced graduate courses (HN 620, HN 812, HN 820) indicates active mentorship in nutrition science. His extensive publication record suggests collaborative research involving students and colleagues, though specific grant funding details were not disclosed in the available profile. Laboratories and Research Teams: The profile does not specify dedicated laboratory facilities, but his research involving in vitro cancer cell assays, phytochemical analysis, and 3D hydrogel models implies access to biochemical and cell culture laboratories within Kansas State University's research infrastructure, likely through collaborations across health sciences and agricultural departments.

Valeria VitelliView profile

Associate Professor

• High-dimensional Data Analysis
• Functional Data Analysis
• Bayesian Statistics
• +6 more

Valeria Vitelli is an Associate Professor in Statistics at the Oslo Centre for Biostatistics and Epidemiology, University of Oslo, where she has been faculty since 2018. Her research spans high-dimensional and functional data analysis, Bayesian methods, clustering, and preference learning. As a Principal Investigator, she leads projects within the Norwegian Centre for Knowledge-driven Machine Learning (Integreat), funded by the Norwegian Research Council. Education: PhD in Statistics, Politecnico di Milano, Italy (2012) MSc in Mathematical Engineering (cum laude), Politecnico di Milano, Italy (2008) Bachelor in Mathematical Engineering (cum laude), Politecnico di Milano, Italy (2006) Dr. Vitelli's research focuses on developing statistical methods for complex high-dimensional data, with applications spanning biomedical research, cancer genomics, and precision medicine. Her work integrates Bayesian inference, machine learning, and functional data analysis to address challenges in modern biostatistics. She has made significant contributions to ranking models, clustering algorithms, and methods for analyzing curves and intrinsically smooth processes. Her publication record demonstrates strong interdisciplinary collaboration across medicine, particularly in cancer research, ophthalmology, and neurology. Recent work shows a clear trend toward developing novel statistical frameworks for integrating complex datasets in precision medicine applications, with emphasis on Bayesian approaches and high-dimensional data analysis. Scientific Recognition: Principal Investigator for the Norwegian Centre for Knowledge-driven Machine Learning (Integreat) Awarded major FRIPRO grants from the Research Council of Norway Dr. Vitelli actively mentors students and researchers in statistical methodology development. Her grant portfolio includes significant funding from the Norwegian Research Council for projects focused on Bayesian clustering methods for high-dimensional omics data. She teaches courses including "Introduction to Machine Learning in Biomedical Research" and "Statistical Principles in Genomics" for medical students and PhD candidates. She leads a research group focused on statistical models for high-dimensional and functional data within the Oslo Centre for Biostatistics and Epidemiology, collaborating closely with medical researchers across various specialties to develop and apply advanced statistical methods to biomedical challenges.

• Mechanical Engineering
• Structural Engineering
• Materials Science
• +7 more

Prof Zhen (Jeff) Luo is a Professor at the School of Mechanical and Mechatronic Engineering at the University of Technology Sydney (UTS). Since 2012, he has led the Advanced Metamaterials & Metastructures (AMM) and Engineering Computation and Optimisation (ECO) research groups, focusing on multi-disciplinary engineering innovations. Expertise : Advanced materials design, topology optimization algorithms, additive manufacturing, and computational mechanics. Education : PhD in Mechanical Engineering from Huazhong University of Science and Technology (2005). His research bridges Mechanical, Structural, Aerospace, and Biomechanical Engineering , developing cutting-edge metamaterials and computational methods. Recent work includes two-scale lattice optimization , stochastic bandgap analysis , and machine learning-aided virtual modeling for structural reliability. Key contributions span 3D-printed heat sinks , frequency-selective surfaces , and hydrogen storage systems . As a World’s Top 2% Scientist (Stanford, 2019–present), he has secured over AUD $7 million in grants, including from the Australian Research Council (ARC) and National Intelligence Discovery Research Grants (NI220100074). Awards : IAAM Scientist Award, IAAM Fellow. Leadership : Editorial roles in Structural and Multidisciplinary Optimization , Frontiers in Bioengineering and Biotechnology , and organizing roles in 21 international conferences.

• Psychometrics
• Structural Equation Modeling
• Bayesian Statistics
• +5 more

Prof. Dr. Holger Brandt serves as Professor of Psychometrics at the Methods Center within the Department of Social Sciences, Faculty of Economics and Social Sciences, Eberhard Karls University of Tübingen since August 2021. Previously, he held Assistant Professor positions at the University of Zurich (2019-2021) and University of Kansas (2016-2019), following a postdoctoral fellowship at Tübingen's Hector Institute for Empirical Educational Research (2013-2016). His educational background includes a PhD (Promotion) from Goethe University Frankfurt's Institute of Psychology in 2013. Brandt's research pioneers advanced methodological frameworks at the intersection of psychometrics, statistics, and machine learning. He specializes in developing dynamic models for intensive longitudinal data, Bayesian estimation techniques, causal mediator analysis, and identification of inattentive response behaviors in surveys. His work rigorously addresses challenges in measurement invariance, structural equation modeling, and handling complex dependencies in social science data. Analysis of his recent publications reveals a dominant focus on Bayesian approaches for latent variable modeling, particularly spike-and-slab priors and latent class methods. His research consistently targets data quality issues in survey methodology while advancing causal inference techniques that relax traditional no-unmeasured-confounder assumptions. Applications span educational research, psychological assessment, and therapeutic alliance dynamics. As a core member of Tübingen's Methods Center, Brandt provides critical methodological infrastructure for social science research across the university, supporting researchers through statistical consulting and advanced methodology development.

Mark EdwardsView profile

Associate Professor

• Visual Cognition
• Attentional Control
• Cognitive Empathy
• +7 more

Mark Edwards is an Associate Professor in Psychology at the Australian National University's School of Medicine and Psychology. His academic career spans over two decades, with a focus on visual cognition, attentional control, and cognitive empathy. Edwards holds a PhD in Psychology from the University of Melbourne, a Psychology degree from ANU, and a Mechanical Engineering degree from the University of Queensland. Edwards' educational background is uniquely interdisciplinary, beginning with a Mechanical Engineering degree from the University of Queensland (1982-1985), followed by a Psychology degree at ANU (1987-1990), and culminating in a PhD in visual psychophysics from the University of Melbourne (1991-1995). After seven years of research at NTT Basic Research Laboratories in Japan and the School of Optometry at the University of California at Berkeley, he returned to ANU in 2001 to take up a faculty position. Edwards' research primarily focuses on visual cognition and attentional control. His work investigates how the human visual system processes information, with particular emphasis on motion processing, face recognition, and stereopsis. He explores how various visual pathways interact at different levels in the brain, including situations where the visual system misinterprets combinations of features, such as in synesthesia. More recently, his research has expanded to examine the implications of attentional control in everyday functioning, particularly in driving contexts, and how it varies across different populations including those with psychopathic traits. Analysis of Edwards' recent publications (2023-2024) reveals a strong focus on individual differences in cognitive processes. His work examines how attentional breadth, working memory, and emotional processing vary across individuals and impact real-world performance. Key themes include the relationship between psychopathic traits and visual attention, cognitive failures in trait anxiety, emotion-induced blindness, and the development of tools like the Cognitive Failures Questionnaire 2.0 to measure subjective cognitive experiences. 2019-2021: Australian Research Council Discovery Grant ($390,000) 2011-2015: Australian Research Council Discovery Grant ($582,074) 2009-2014: Australian Research Council Discovery Grant ($475,000) 2006-2011: Australian Research Council Centre of Excellence in Vision ($10,000,000) Edwards has served as Principal Investigator or Co-Investigator on numerous research projects, including "Investigating cognitive predictors of crash risk in provisional drivers" and "Using a scientific understanding of human cognition to reduce crash risk in older adult drivers." His research has practical applications in understanding and improving driver safety, particularly for vulnerable populations.

Dipak DeyView profile

Professor

• Bayesian analysis
• Bioinformatics
• Biostatistics
• +8 more

Dipak Dey is a Professor in the Department of Statistics at the University of Connecticut . His work bridges theoretical and applied statistics, with a focus on Bayesian methodologies and computational statistics. Affiliations : University of Connecticut, Department of Statistics Contact : dipak.dey@uconn.edu , Office: AUST 327, Phone: (860) 486-4755 His research interests span Bayesian analysis , Biostatistics , Computational statistics , Statistical genetics , and Spatio-temporal modeling . He has pioneered techniques in spatial curvature processes and scalable Bayesian algorithms for large datasets. Recent publications highlight his contributions to Bayesian spatial modeling (blockNNGP, curvature processes), survival analysis (skew-t distributions, cure rate models), and computational statistics (variable selection in Gaussian processes, fast inference algorithms). Applications include insurance data , epidemiology , and environmental statistics . Scientific Awards : Board of Trustees Distinguished Professor (University of Connecticut) He actively collaborates on interdisciplinary projects and mentors researchers in advanced statistical methodologies for complex data structures.

Victor Hugo LachosView profile

Professor

• Mixed-effects models
• Censored regression models
• Time series analysis
• +7 more

Dr. Victor Hugo Lachos is a Professor in the Department of Statistics at the University of Connecticut. His research focuses on advanced statistical methodologies for handling complex data structures, including censored regression models, mixed-effects models, and heavy-tailed distributions. He has contributed extensively to Bayesian inference, EM algorithms, and software development for statistical analysis. Multivariate Student-t and skew-normal distributions Longitudinal and spatial data modeling Regularization techniques for high-dimensional data Software packages for censored data analysis His recent publications emphasize robust modeling of censored and irregularly observed data, with applications in medical research (e.g., HIV longitudinal studies) and environmental modeling (e.g., acid rain analysis). His work integrates theoretical advances in distribution theory with practical computational tools in R packages like ‘StempCens’ and ‘mixsmsn’. These contributions are complemented by methodological innovations in EM algorithm applications, influence diagnostics, and semiparametric regression. Dr. Lachos' research has been applied to diverse fields such as medical data analysis, environmental science, and educational measurement. While no explicit awards or student advisement details are listed, his prolific output in top-tier journals and software development underscores his active academic engagement.

Greg ShriverView profile

Professor

• Avian ecology
• Conservation biology
• Ecological monitoring
• +7 more

Greg Shriver is a Professor of Wildlife Ecology at the University of Delaware’s Department of Entomology & Wildlife Ecology, College of Agriculture & Natural Resources. His research focuses on avian ecology, conservation biology, and impacts of climate change on tidal marsh ecosystems. Education: Ph.D. in Environmental Forest Biology, State University of New York (2002) M.S. in Wildlife Conservation, University of Massachusetts (1996) B.S. in Wildlife Management, University of Maine (1991) Research interests: Avian ecology and conservation in fragmented habitats Ecological monitoring of tidal marsh birds Sea-level rise impacts on coastal ecosystems Tick-borne disease dynamics in urban forests Scientific awards: Elected member, American Ornithologists Union (2010) Elected Fellow, American Ornithologist Union (2020) Council member, Association of Field Ornithologists (2008-2010) Treasurer, Association of Field Ornithologists (2008-2016) Key publications (2019-2021) examine mercury contamination in tidal marsh birds, climate change impacts on aridland birds, eDNA metabarcoding for forest biodiversity, and hybrid zone genetics in sparrows. His work frequently employs Bayesian networks, GPS tracking, and habitat-based modeling.

Dakota W. CintronView profile

Assistant Professor

• Latent Variable Modeling
• Measurement Theory
• Causal Inference
• +5 more

Dakota W. Cintron is an Assistant Professor in the Division of Behavioral and Organizational Sciences at Claremont Graduate University. He specializes in advanced quantitative methods in psychology, focusing on latent variable modeling, measurement theory, and causal inference. Education: BS MS EdM PhD Research interests include studying how psychosocial factors influence well-being and health outcomes over time, particularly in at-risk populations. He applies methods like growth mixture modeling, alignment optimization, and natural language processing to analyze social disparities, emotional dynamics, and long-term health trends. Selected publications highlight his work on heterogeneous treatment effects in social policies, intersectional measurement invariance, and the use of big data for psychological modeling. His methodological contributions address classification accuracy in mixture models and enhance policy evaluation frameworks. Teaching: Psych 315E: Multilevel Modeling Psych 315NN: Bayesian Statistics Psych 302: Research Methods (PhD)

Oğuz KöklüView profile

Lecturer

• Statistics Education
• Probability Education
• Mathematics Teacher Education
• +3 more

Oğuz Köklü is a Lecturer in the Department of Mathematics Education at Marmara University. He earned his Ph.D. in Mathematics Education from the University of Georgia (2017), M.S. in Mathematics and Science Education from Boğaziçi University (2012), and B.S. in Mathematics Teaching from Boğaziçi University (2008). Ph.D.: Mathematics Education, University of Georgia (2017) M.S.: Mathematics and Science Education, Boğaziçi University (2012) B.S.: Mathematics Teaching, Boğaziçi University (2008) His research focuses on Statistics Education , Probability Education , and Mathematics Teacher Training , with particular emphasis on Bayesian reasoning , statistical variability , and geometric proportionality . Recent publications analyze preservice teacher misconceptions and technology-enhanced instruction methods. Köklü’s work includes grants from TUBITAK and NSF , such as a 2023 workshop leader role for a TUBITAK-funded project on data science education for gifted students. He has advised 5 Master’s students and contributed to curriculum design frameworks in Turkish journals.

Michael MumaView profile

Professor

• Robust Data Science
• Signal Processing
• Machine Learning
• +5 more

Michael Muma is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. His research focuses on robust data science theory and methods applied to signal processing and machine learning in biomedicine and engineering. He leads the ERC Starting Grant ScReeningData project, developing methods for reproducible information discovery in biomedical databases, and is a Principal Investigator in the LOEWE center emergenCITY and BMBF cluster curATime. Prior roles include Independent Junior Research Group Leader (Athene Young Investigator) and Lecturer at TU Darmstadt from 2017 to 2022, and Research Associate (Post-Doc since 2014) from 2009 to 2017. His research interests span robust statistical methods, high-dimensional data analysis, emergency response systems, and biomedical signal processing. Notable projects include FDR-controlled portfolio optimization, ECG delineation algorithms, and radar-based vital sign estimation. Muma has contributed to distributed sensor networks, robust clustering, and sparse regression techniques. His work addresses challenges in multi-source detection, financial data analysis, and genomics through interdisciplinary approaches combining signal processing, machine learning, and robust statistics. Recent publications emphasize scalable solutions for high-dimensional problems, including applications in robotics, cardiology, and financial index tracking.

Pengcheng LiuView profile

Senior Lecturer

• Robotics
• Bio-inspired Robotics
• Soft Robotics
• +14 more

Pengcheng Liu is an Associate Professor (Senior Lecturer) in Robotics and Applied Control at the Department of Computer Science, University of York, UK. He has previously held academic positions at Cardiff Metropolitan University, University of Lincoln, Bournemouth University, and visiting roles in China. He is a member of IEEE and IFAC, and serves on several IEEE Technical Committees related to robotics and automation. PhD in Robotics and Control MSc in Control Theory and Control Engineering BEng in Measurement Control and Instrumentation His research focuses on the convergence of robotics and biology, particularly in bio-inspired design, soft robotics, human-robot collaboration, rehabilitation, and embodied AI. He explores how biological systems can inform adaptive, resilient, and energy-efficient robotic systems. His work spans modeling animal/human motion, sensory-motor learning, morphological design, and control optimization. The 15 most recent publications reflect a strong interdisciplinary trend combining robotics, control theory, machine learning, and biomedical applications. Key themes include soft robotics, bio-inspired actuators, motion planning, rehabilitation systems, and wireless communication for robotic systems. The research integrates simulation, hardware prototyping, and intelligent control, often using ROS and Gazebo. Global Peer Review Awards — Top 1% in Cross-Field (2019) Global Peer Review Awards — Top 1% in Engineering (2019) Outstanding Associate Editor of 2020, IEEE Access Outstanding Contribution Awards, Elsevier (2017) Dr Liu has extensive experience in securing and managing research grants from EPSRC, Innovate UK, Horizon 2020, Erasmus Mundus, FP7-PEOPLE, HEIF, NHS I4I, and NSFC. He serves as a regular reviewer for around 30 journals and 8 flagship conferences in robotics, AI, and control. He is an Associate Editor for IEEE Access and Academic Editor for PeerJ Computer Science, and has contributed to special issues on computational learning for robotics. He leads research in bio-inspired robotics and advises PhD students in areas such as lifelong learning machine intelligence, soft robotics, motion planning, and cyber-physical systems. He is actively recruiting motivated PhD candidates with backgrounds in engineering, computer science, or applied mathematics.