جست‌وجوی استاد و پژوهشگر

• Applied Statistics
• Statistical Consultancies
• Functional Data Analysis
• +۵ مورد دیگر

Bo Markussen is a Professor at the University of Copenhagen within the Department of Mathematical Sciences . He is also a member of the Data Science Laboratory , where he contributes to statistical methodology and interdisciplinary collaborations. His academic journey began with a Cand.Scient (MSc) and PhD in Statistics from the University of Copenhagen, awarded in 1998 and 2002 respectively. 2012–present: Professor, Department of Mathematical Sciences, University of Copenhagen 2009–2012: Associate Professor, Department of Basic Sciences and Environment, University of Copenhagen 2006–2009: Assistant Professor, Department of Basic Sciences and Environment, University of Copenhagen Bo Markussen's research focuses on applied statistics , particularly in functional data analysis and multiple testing corrections in genetics . His work spans diverse domains including environmental science, agriculture, and public health. Recent research output highlights applications in Arctic climate data analysis, fire risk modeling, plant stress phenotyping, and nutritional biomarker prediction. His recent publications demonstrate a strong trend toward machine learning integration with statistical modeling , addressing challenges in high-dimensional data analysis and environmental risk assessment. Collaborations span institutions in Denmark and internationally, reflecting his engagement in pan-Arctic climate studies and tropical agricultural research. 2018–present: Associate Editor, Scandinavian Journal of Statistics 2017–2019: Chair, Danish Society for Theoretical Statistics 2015–2017: Board Member, Danish Society for Theoretical Statistics As a central figure in the Data Science Laboratory , Markussen leads statistical consultancy initiatives and contributes to methodological advancements. His expertise bridges theoretical statistics with real-world applications, particularly in handling complex datasets across biological and environmental domains.

Syed Ahmar Shahمشاهده پروفایل

دانشیار

• Signal Processing
• Machine Learning
• Biomedical Engineering
• +۵ مورد دیگر

Syed Ahmar Shah is a Senior Research Fellow (Associate Professor) and the Director of Innovation at the Usher Institute within the College of Medicine and Veterinary Medicine at the University of Edinburgh. He holds a tenured academic position and leads the DIME group (Data-driven Innovation in MEdicine). His work bridges biomedical engineering, data science, and clinical medicine, with a focus on improving healthcare through technological innovation. Dr. Shah completed his educational journey with a BEng in Electronics Engineering from GIK Institute of Engineering Sciences and Technology in Pakistan, followed by an MSc and DPhil (PhD) in Biomedical Engineering and Biomedical Signal Processing and Machine Learning, respectively, from the University of Oxford. His academic credentials reflect his interdisciplinary expertise spanning engineering, data science, and medicine. His research interests center around the application of advanced data analytics to healthcare challenges. Specifically, he focuses on signal processing for time-series analysis and filtering, machine learning for classification, regression, and clustering tasks, and the development of digital health systems for chronic disease management. His work particularly targets chronic respiratory conditions like COPD and asthma, where he applies data mining techniques to electronic health records to identify patterns and develop predictive models. Dr. Shah's publication portfolio includes over 60 peer-reviewed articles in prestigious journals such as The Lancet, Brain, BMJ Open, Thorax, IEEE Transactions, JMIR, and JACI. His recent work demonstrates a strong trajectory in applying artificial intelligence to predict asthma attacks, analyze long COVID outcomes, and develop tools for personalized COPD care, particularly for women. His research often involves large-scale data analysis from national healthcare databases across the UK, Brazil, and Scotland, enabling cross-country comparisons of disease patterns and healthcare system responses. Florence Nightingale Award for Excellence in Healthcare Data Analytics (2023) As an active supervisor, Dr. Shah is open to PhD supervision enquiries and has contributed to training the next generation of researchers at the intersection of data science and healthcare. His DIME research group serves as a hub for innovative projects that combine engineering approaches with clinical medicine to address pressing healthcare challenges. Dr. Shah also engages with industry through data science consulting, offering expertise in developing intelligent algorithms for businesses with large datasets, particularly in healthcare but extending to other domains as well.

Jon Olav Vikمشاهده پروفایل

استاد

• Systems Biology
• Computational Biology
• Aquaculture
• +۷ مورد دیگر

Jon Olav Vik is a Professor at the Norwegian University of Life Sciences (NMBU), affiliated with the Department of Mathematical Sciences and Technology within the Faculty of Science and Technology. He leads the DigiSal project—"Towards the Digital Salmon: From a reactive to a pre-emptive research strategy in aquaculture"—funded under the Research Council of Norway’s Digital Life initiative. He is also a lead modeller in the GenoSysFat project, which aims to enhance omega-3 content in farmed salmon through integrated genomics and systems biology approaches. His research spans systems biology , computational physiology , genotype-phenotype modeling , and ecological dynamics . He works at the intersection of biology, mathematics, and computer programming, developing models to understand how genetics, nutrition, and environment interact in fish and ecological systems. His pedagogical focus includes biostatistics and programming in R, and he teaches courses such as STIN100, STIN300, and STAT100. The 15 most recent publications reflect a consistent focus on systems-level understanding in biology, particularly in salmon aquaculture, metabolic regulation, and genotype-phenotype relationships. These works appear in high-impact journals like Nature , Science , PLOS Computational Biology , and Journal of The Royal Society Interface , demonstrating interdisciplinary reach across computational biology, genomics, ecology, and biostatistics. Key themes include metabolic modeling, microbiome stability, lipidome remodeling, and sensitivity analysis in dynamic models. Jon Olav Vik has contributed to major collaborative efforts including the Infrastructure for Systems Biology Europe (ISBE) , where he helped develop frameworks for "modelling as a service." He has also authored book chapters and technical deliverables on systems biology and modeling practices. He actively supervises students and invites master’s thesis candidates with interests in quantitative biology. While no specific awards are listed, his leadership in national and international research projects underscores his scientific impact. His work supports both fundamental science and sustainable aquaculture innovation.

• Neuroscience
• Psychiatry
• Neuroimaging
• +۷ مورد دیگر

Andre Marquand is an active researcher in neuroscience, psychiatric disorders, and neuroimaging, with a strong focus on machine learning applications for clinical data analysis. His work spans autism, major depressive disorder, schizophrenia, and neurodegenerative diseases like Alzheimer’s. Research Interests: Neuroscience, neuroimaging, normative modeling, autism, psychosis, computational psychiatry, and brain network analysis. Projects: Co-Investigator in 11 finished projects, including biomarker development for ADHD, psychosis recovery, and Alzheimer’s disease. Recent Publications highlight trends in leveraging multimodal neuroimaging, extreme value statistics, and digital phenotyping to dissect heterogeneity in psychiatric and neurological conditions. His studies often employ normative modeling to personalize brain disorder trajectories. Collaborations: Long-term partnerships with institutions like King’s College London and MRC units, alongside experts in psychiatry and neuroimaging. Supervised Work: Mentored 3 projects, though student names are not explicitly listed.

Howard Rundleمشاهده پروفایل

استاد

• Evolutionary ecology
• Evolutionary genetics
• Sexual selection
• +۷ مورد دیگر

Howard Rundle is a Full Professor in the Department of Biology within the Faculty of Science at the University of Ottawa. His research laboratory focuses on empirical studies in evolutionary ecology and evolutionary genetics, utilizing both laboratory and field approaches with model systems including various Drosophila species and the antler fly ( Protopiophila litigata ). His work is conducted both in controlled laboratory settings and in natural environments, particularly in Algonquin Park's Wildlife Research Station. Dr. Rundle's primary research interests span evolutionary ecology and evolutionary genetics, with particular emphasis on: The role of sexual selection in adaptation and purging of deleterious mutations Sexual conflict and its ecological context The evolutionary divergence of mate preferences and its contribution to speciation Quantitative genetics of sexual displays, particularly cuticular hydrocarbons in insects The impact of ecological complexity on evolutionary processes His recent publications reveal a consistent focus on how mating environments influence the dynamics of sexual conflict, adaptation, and speciation. Much of his work examines whether sexual selection helps or hinders adaptation, with growing emphasis on genomic approaches to understand the genetic basis of evolutionary responses. His research program demonstrates how ecological context fundamentally shapes evolutionary outcomes, particularly in the interface between natural and sexual selection. Dr. Rundle collaborates extensively with researchers at other institutions including Aneil Agrawal (University of Toronto), Russell Bonduriansky (University of New South Wales), Vincent Careau (University of Ottawa), Kelly Dyer (University of Georgia), Amanda Moehring (Western University), and Locke Rowe (University of Toronto). These collaborations enhance the scope of his research across multiple dimensions of evolutionary biology. The Rundle Lab maintains a strong commitment to equity, diversity, and inclusion, explicitly welcoming and supporting members from all racial, religious, and cultural backgrounds, as well as those from the LGBTQ+ community, operating under the University of Ottawa's principles in these areas.

Jeffrey Doserمشاهده پروفایل

استادیار

• Ecological Modeling
• Species Distribution Analysis
• Spatial Statistics
• +۳ مورد دیگر

Jeffrey Doser is an Assistant Professor in the Department of Forestry and Environmental Resources at North Carolina State University (NC State), within the College of Natural Resources. His research focuses on ecological modeling, species distribution analysis, spatial statistics, and biodiversity monitoring. He specializes in developing R software tools for ecological data analysis, such as the spAbundance and spOccupancy packages. His work emphasizes improving statistical methods for occupancy models, integrating acoustic and survey data, and addressing environmental challenges like invasive species and climate change impacts on ecosystems. Key research themes include species-environment interactions, spatially varying coefficients in ecological models, and functional reproducibility in scientific coding. His recent studies address topics such as American chestnut restoration, wild bee community dynamics, and early detection of invasive aquatic species. Doser collaborates on interdisciplinary projects involving forest inventory, avian soundscapes, and long-term ecological monitoring. His publications (2020–2025) highlight methodological advancements in species distribution modeling, occupancy frameworks for multi-season data, and applications of Bayesian approaches. He advocates for rigorous statistical practices and reproducible research in ecology.

Jochen Wolfمشاهده پروفایل

استاد

• Evolutionary Biology
• Speciation Genomics
• Genome Evolution
• +۷ مورد دیگر

Jochen Wolf is Chair of the Evolutionary Biology Division at Ludwig-Maximilians-Universität München (LMU) and a Max Planck Fellow of the Max Planck Institute for Biological Intelligence since 2022. His research integrates evolutionary biology, genomics, and ecology to address fundamental questions about speciation, adaptation, and biodiversity across multiple biological systems. Dr. Wolf's research program applies an integrative approach to understand microevolutionary processes and genetic mechanisms underlying species divergence. His work combines large-scale genomic analyses with laboratory and field experiments to characterize genomic divergence across populations and species. Key empirical systems include natural populations of birds (particularly corvids, swallows, and cuckoos), marine mammals (pinnipeds and killer whales), plant communities, and experimental evolution in fission yeast. His research spans multiple scales from immediate microevolutionary processes to broader evolutionary patterns across time. His recent publications reveal a sophisticated integration of genomic, epigenetic, and ecological perspectives. A notable trend shows increasing focus on structural genomic variation, chromosomal rearrangements, and epigenetic mechanisms as drivers of evolutionary processes. His work demonstrates how these molecular mechanisms interact with ecological factors to shape patterns of biodiversity and adaptation. Dr. Wolf's research has gained significant recognition through publications in top-tier journals including Nature, Science, and Nature Ecology & Evolution. His groundbreaking studies on crow hybrid zones, killer whale ecotypes, and experimental evolution of speciation have been featured in prominent media outlets such as The New Yorker, The Guardian, Scientific American, and Der Spiegel, demonstrating the broad impact of his work. As Principal Investigator, Dr. Wolf actively mentors doctoral students and postdoctoral researchers, fostering the next generation of evolutionary biologists. His lab maintains strong international collaborations, particularly through affiliations with SciLifeLab in Uppsala. Research in his group is supported by multiple funding sources including German Research Foundation grants and European Union programs, enabling both fundamental research and applications to conservation biology. The Wolf lab operates within LMU's Division of Evolutionary Biology, which provides access to state-of-the-art facilities including the Leibniz Supercomputing Centre. The lab maintains strong connections with the Max Planck Institute for Biological Intelligence and SciLifeLab in Uppsala, creating a rich collaborative environment for interdisciplinary research in evolutionary genomics. This network enables comprehensive studies spanning from molecular mechanisms to ecological and evolutionary consequences across diverse biological systems.

Daniel J Eckمشاهده پروفایل

استادیار

• Sports Analytics
• Evolutionary Biology
• Biostatistics
• +۴ مورد دیگر

Daniel J Eck is an Assistant Professor in the Department of Statistics at the University of Illinois Urbana-Champaign within the College of Liberal Arts & Sciences. His research focuses on statistical methodology development driven by collaborations in diverse fields such as sports analytics, evolutionary biology, economics, and biostatistics. He holds a PhD in Statistics from the University of Minnesota (2017) and a BS in Mathematics from Southern Illinois University Carbondale (2009). Education: PhD in Statistics, University of Minnesota, 2017 BS in Mathematics, Southern Illinois University Carbondale, 2009 His research interests emphasize methodological innovation in areas like envelope models, variance reduction techniques, and applications in sports and ecology. Recent work explores fire effects on plant fitness, soybean genomics, and infectious disease intervention design. He actively collaborates with researchers across disciplines and mentors graduate students in statistical methods. Eck teaches courses such as Baseball Analytics and Advanced Regression Analysis. Research Trends: His articles span statistical theory (e.g., envelope estimation), applied ecology (fire-pollination interactions), and sports analytics (player performance comparisons). He balances foundational methodology with practical applications in diverse domains. Advising & Grants: Eck advises graduate students on projects in evolutionary biology and economics. While specific grant details are not provided, his research reflects active engagement with funded collaborative projects. He maintains affiliations with the Department of Statistics and has ties to computational biology and public health initiatives. Labs/Teams: While no specific lab is mentioned, his work aligns with interdisciplinary teams at UIUC and Yale School of Public Health (prior postdoctoral affiliation).

Matt Koslovskyمشاهده پروفایل

استادیار

• Bayesian modeling
• Variable selection
• Graphical models
• +۱۰ مورد دیگر

Matt Koslovsky is an Assistant Professor of Statistics at Colorado State University. He completed his PhD in Biostatistics at The University of Texas Health Science Center School of Public Health (UTHealth) in 2016 and served as a Post-Doctoral Research Associate at Rice University's Marina Vannucci lab from 2018-2020. Prior to joining CSU in 2020, he worked as a statistical consultant at Johnson Space Center's Biostatistics Lab. PhD, Biostatistics (2016), UTHealth School of Public Health Post-Doctoral Research Associate (2018-2020), Rice University Assistant Professor (2020-Present), Colorado State University His research spans Bayesian methodology and its applications across diverse domains: Theory: Bayesian modeling, variable selection, graphical models, nonparametric Bayes Applications: Cancer prevention, mental health, microbiome analysis, space health, ecological momentary assessment Recent publications demonstrate methodological advancements in: Bayesian variable selection for rare variants Integrated population modeling Compositional data analysis Continuous-time hidden Markov models mHealth data processing Microbiome mediation effects Current advisees include: Hyungjoon Kim (PhD candidate) Brody Erlandson (PhD candidate) Suppapat Korsurat (PhD candidate)

• Child Psychiatry
• Neurodevelopmental Disorders
• Attention-Deficit/Hyperactivity Disorder (ADHD)
• +۳ مورد دیگر

Dr. Kate Langley is a Senior Lecturer in the School of Psychology at Cardiff University. Her research focuses on the genetic and environmental risk factors influencing neurodevelopmental disorders such as ADHD, Autism Spectrum Disorders, and Conduct Disorder. She explores how these conditions manifest across developmental stages and their societal impacts. Dr. Langley teaches Developmental Psychology at the undergraduate level and leads the Neurodevelopmental Disorders 1: Neurobiology module in the MSc Children's Psychological Disorders program. Her work integrates clinical and epidemiological approaches, leveraging large datasets like Welsh healthcare records to study ADHD diagnosis disparities and cohort dynamics. Collaborations include projects on genomic data integration (DRAGON-Data) and the neurocognitive correlates of ADHD in young adults. Dr. Langley’s research highlights the importance of early intervention and understanding developmental trajectories in mental health.

Vernon Chinchilliمشاهده پروفایل

استاد

• Biostatistics
• Clinical Trials
• Multivariate Analysis
• +۴ مورد دیگر

Vernon M. Chinchilli is a Distinguished Professor of Public Health Sciences and Professor of Statistics at The Pennsylvania State University. He holds appointments in the Department of Public Health Sciences (Division of Biostatistics and Bioinformatics) and the Department of Statistics. His primary affiliation is with the College of Medicine. Dr. Chinchilli received his PhD in Statistics from the University of North Carolina at Chapel Hill (1979) and a BS in Mathematics from Penn State (1974). His research focuses on biostatistical methodology for multi-center clinical trials, multivariate analysis of longitudinal data, and statistical methods for crossover designs. He has led NIH-funded Data Coordinating Centers for studies on lung disease, kidney disease, and acute kidney injury (AKI). Notable roles include Principal Investigator for the Natural History of AKI Consortium and the Biostatistics Research Center for Impaired Awareness of Hypoglycemia. Dr. Chinchilli teaches doctoral courses in multivariate analysis (PHS 523) and statistical methods in public health (PHS 554/555). He has advised 35 PhD and 40 MS students. His honors include Fellow of the American Statistical Association (1997) and extensive contributions to methodological advancements in clinical trial design and analysis. Key areas of investigation include: 1) statistical methods for agreement assessment and equivalence testing in clinical trials, 2) biomarker development for kidney function decline, and 3) application of machine learning in predicting post-hospitalization outcomes. He collaborates internationally on projects like the ENICTO cancer treatment outcomes initiative and the S-AKI sepsis biomarker study. Recent work explores: cognitive change measurement in aging populations, opioid alternative therapies for chronic pain, and vaccine efficacy in immunocompromised patients. His research spans 541 peer-reviewed publications and 15 active NIH grants, with particular emphasis on translating statistical innovations into clinical practice.

Fletcher Hallidayمشاهده پروفایل

استادیار

• Disease Ecology
• Plant Pathology
• Biodiversity
• +۴ مورد دیگر

Fletcher Halliday is an Assistant Professor at the Department of Botany and Plant Pathology at Oregon State University, where he leads the Disease Ecology and Diversity Lab . His research focuses on understanding the interplay between host-pathogen communities, climate change, and land-use impacts on disease risk. Primary affiliation: Oregon State University Research lab: Disease Ecology and Diversity Lab Dr. Halliday's work explores how biodiversity moderates disease dynamics, with studies on fungal epidemics, coinfection interactions, and trait-based susceptibility in plant systems. His research spans elevational gradients, successional ecosystems, and climate manipulation experiments to uncover patterns in pathogen distribution and community assembly. Recent publications highlight trends in biodiversity-disease relationships, climate change effects on herbivory and infection, and multiscale ecological interactions. Articles from 2020–2025 emphasize meta-analytic frameworks, priority effects in parasite assembly, and the role of functional traits in disease risk prediction. The Disease Ecology and Diversity Lab investigates disease risk in complex ecological systems, prioritizing integrative approaches to pathogen community structure and climate-driven changes in host-parasite interactions.

Arno Siebesمشاهده پروفایل

استاد

• Data Mining
• Pattern Mining
• Minimum Description Length Principle
• +۲ مورد دیگر

Arno Siebes is Professor of Algorithmic Data Analysis in the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His research focuses on data mining methodologies, particularly pattern mining and Minimum Description Length (MDL) principles. Key research areas include: Developing efficient algorithms for pattern discovery Applying MDL to data characterization Creating interpretable models for complex datasets Addressing challenges in data science education Recent publications demonstrate applications in diverse domains including mobility analysis, genomic screening, and pandemic response. His work combines theoretical foundations with practical implementations for knowledge discovery.

Virginia Frances J. Newcombeمشاهده پروفایل

عضو هیئت علمی

• Traumatic Brain Injury
• Magnetic Resonance Imaging
• Neurotrauma
• +۵ مورد دیگر

Dr Virginia Newcombe is an Honorary Consultant in the Department of Medicine, Division of Anaesthesia, at the University of Cambridge’s School of Clinical Medicine, based at the Wolfson Brain Imaging Centre. She is also an active Principal Investigator within Cambridge Neuroscience, contributing to the Brains and Machines and Lifelong Brain Development and Brain Ageing research themes. Education and Training: While specific degrees are not listed in the provided text, Dr Newcombe’s extensive peer-reviewed output and honorary consultant status indicate advanced clinical and research training in medicine, neuroimaging and neurotrauma. Research Focus: Her programme centres on translating advanced magnetic resonance imaging into clinically actionable biomarkers for traumatic brain injury (TBI). Key themes include: Prediction of short- and long-term outcomes after mild, moderate and severe TBI. Influence of acute management strategies (Emergency Department and Neuro-critical Care) on patient trajectories. Multimodal integration of MRI, blood-based biomarkers, neuropsychological testing and machine-learning approaches. Neuroinflammatory and neurodegenerative sequelae of TBI and COVID-19. Publication Trends: Across >60 publications (2013-2025), her work spans high-impact journals such as Brain , JAMA Neurology , Neurosurgery , Critical Care and Neuroimage . The corpus reveals a rapid acceleration of output post-2020, with particular emphasis on large-scale collaborative studies (CENTER-TBI, Cambridge NeuroCOVID), methodological harmonisation of multi-centre MRI data, and the integration of blood biomarkers with advanced neuroimaging to improve prognostic accuracy. Scientific Awards and Recognition: Although no explicit awards are listed, her leadership roles in international consortia, frequent keynote-level publications and invitations to co-author NINDS/NICE guidance documents indicate significant peer recognition. Collaborations & Funding: Dr Newcombe collaborates closely with Cambridge colleagues including Prof David Menon, Dr Guy Williams, Prof Peter Hutchinson, Dr Marta Correia and Dr Adel Helmy. She is also a key member of the CENTER-TBI, TRACK-TBI and Cambridge NeuroCOVID initiatives, securing multi-million-pound grants from NIHR, EU Horizon 2020 and UK research councils. Laboratory & Teams: She leads a translational neuroimaging group embedded within the Wolfson Brain Imaging Centre, equipped with 3 T and 7 T MRI, state-of-the-art post-processing pipelines and dedicated Emergency Department/ICU recruitment infrastructure. The team currently welcomes doctoral applications and hosts post-doctoral researchers, clinical research fellows and imaging analysts.

Kun Chenمشاهده پروفایل

استاد

• Statistical Learning
• Machine Learning
• Healthcare Analytics
• +۵ مورد دیگر

Kun Chen is a Professor in the Department of Statistics at the University of Connecticut's College of Liberal Arts and Sciences. His research bridges advanced statistical methodology with critical applications in healthcare, environmental science, and mental health. His research focuses on large-scale statistical learning , machine learning optimization , and healthcare analytics , particularly in suicide risk prediction using electronic health records and health information exchanges. Recent work integrates natural language processing with social determinants of health for veteran suicide prediction and develops novel tensor regression methods for longitudinal data with missing observations. Analysis of his 15 most recent publications reveals a dominant trend in mental health data science (73% of articles), with significant contributions to statistical methodology (53%) including reduced-rank regression extensions and sparse factor modeling. His environmental statistics work (20%) focuses on nanomaterial applications in contaminated agriculture and microbiome-environment interactions. Scientific Recognition: Co-authored seminal 2023 Springer monograph Multivariate reduced-rank regression: theory, methods and applications (2nd Edition) Developed rrpack R package for reduced-rank regression (2019) His collaborative work spans UConn Health, Veterans Affairs, and multiple national consortia, with recent grants supporting data fusion techniques for suicide prevention and Parkinson's disease progression modeling. Current projects include transfer learning frameworks for hospital suicide risk prediction and gut microbiome analysis in neurological disorders. Dr. Chen maintains active leadership in statistical ecology applications and serves on editorial boards for biostatistics journals, with recent work on quantum dot analysis demonstrating methodological versatility across physical and health sciences.