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Ali MoghimiView profile

Assistant Professor

• Remote Sensing
• Artificial Intelligence
• Precision Agriculture
• +14 more

Ali Moghimi is an Assistant Professor of Teaching in the Department of Biological and Agricultural Engineering at the University of California, Davis, within the College of Engineering. He specializes in remote sensing, artificial intelligence, and digital agriculture, with a strong focus on the food-water-energy nexus. His work integrates UAVs, LiDAR, and hyperspectral imaging for agricultural innovation. Research Interests: Remote Sensing and AI in Agriculture Precision and Digital Agriculture Unmanned Aerial Systems (UAS) Machine Learning and Deep Learning for Plant Phenotyping Food-Water-Energy Nexus LiDAR and Multispectral/Hyperspectral Imaging His recent publications show a consistent trend in applying deep learning and remote sensing to crop monitoring, yield prediction, and disease detection, particularly in wheat and grapevines. The work bridges engineering, computer science, and agricultural science, emphasizing automation and data-driven decision-making in farming. Scientific Contributions: Developed machine learning models for nitrogen estimation in grapevines Applied deep neural networks for Fusarium head blight detection Advanced hyperspectral imaging for salt stress assessment in wheat Innovated in fruit quality assessment using NIR spectroscopy Dr. Moghimi teaches ABT 060, "Introduction to Unmanned Aerial Systems for Agriculture & Environmental Science," and is passionate about interdisciplinary education. He advises graduate students as part of the graduate faculty and conducts research supported by interdisciplinary collaborations. His work contributes to sustainable agricultural practices through technological innovation.

• Wine Science
• Viticulture
• Food Chemistry
• +6 more

Dr. Joanna Gambetta serves as a Lecturer in Food Science at the University of Newcastle , affiliated with the School of Environmental and Life Sciences . With academic qualifications from Montpellier SupAgro (MSc in Oenology and Viticulture) and the University of Adelaide (PhD in Wine Science), her research focuses on grape and wine quality assessment , biotic/abiotic stress impacts on aroma composition , and practical solutions for vineyard challenges in Australian contexts. Education : MSc in Oenology and Viticulture, Montpellier SupAgro PhD in Wine Science, University of Adelaide Research Interests span three primary domains: Antifungal microbial volatile organic compounds (VOCs) for biocontrol applications, particularly against Botrytis cinerea and Alternaria alternata through novel analytical approaches Climate change adaptation strategies in viticulture, including modeling vine phenology and mitigating sunburn damage via secondary metabolite analysis Food chemistry innovations in plant-based protein gels and dairy alternatives, integrating fermentation and enzymatic crosslinking Publication Trends reveal a multidisciplinary approach combining analytical chemistry (SPME-GC-MS, LC-MS/MS), chemometrics , and industrial collaboration . Her work bridges agricultural science with food sensory analysis , while recent studies tackle plant-based cheese development using pea protein emulsions.

Georg SpinnerView profile

Lecturer

• Medical Image Analysis
• Bayesian Networks
• Computational Biology
• +3 more

Georg Spinner is a Lecturer and Head of the Research Group for Medical Image Analysis & Data Modelling at the Institute of Computational Life Sciences, Zurich University of Applied Sciences (ZHAW). His work integrates computational methods with biomedical research, focusing on stroke management, intracranial aneurysm risk modeling, and quantitative medical imaging. Primary affiliation: Zurich University of Applied Sciences Research focus: Bayesian networks, medical imaging, computational epidemiology Projects: GEMINI (digital twins for stroke), Stroke DynamiX (causal networks in stroke care), IVIM muscle activation studies Using Bayesian networks and advanced imaging techniques like IVIM DWI, his research aims to develop data-driven decision support systems for neurovascular diseases. He has contributed to modeling stroke health data and intracranial aneurysm risk stratification through international multicenter collaborations. His work spans computational biology, neuroinformatics, and digital health, with publications in journals like Medical Image Analysis and conferences including the International Conference on Computational and Mathematical Biomedical Engineering. Key methodologies include causal inference, dynamic disease modeling, and quantitative image analysis.

• Biostatistics
• Health Informatics
• Machine Learning
• +4 more

Dr. Anastasia Ushakova is a Lecturer in Biostatistics (Health Informatics) at the Lancaster University Medical School Centre for Health Informatics, Computing and Statistics . Her work bridges biostatistics, health informatics, and psychology through cross-disciplinary research involving big data and mixed methods. Research Focus: Mental health analytics (NHS collaborations), digital learning supplement analysis (literacy/psychology), and time-series data modeling. Grants: Funded by ESRC, EPSRC, Medical Research Foundation, NWCR, and NIHR. Recent Article Trends span emergency response modeling (OHCA), aggression dynamics, digital literacy tools, mental health disparities, and socio-emotional development. Her 2025 work on rural cardiac emergency response and 2024 studies on ADHD and EMA methodology highlight her interdisciplinary approach. PhD Student: Yawen Ma (CHICAS group, Lancaster University). Professional Activities: Organizing Committee member, NIHR Statistics Conference; active in Royal Statistical Society and CHICAS research group.

Matthew ThorpeView profile

Associate Professor

• Machine Learning
• Data Science
• Applied Mathematics
• +4 more

Matthew Thorpe is an Associate Professor in the Department of Statistics at the University of Warwick and a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). His research focuses on applying methods from applied analysis—including partial differential equations (PDEs), calculus of variations, and optimal transport—to machine learning and data science challenges. He has organized workshops such as the 'Machine Learning in Infinite Dimensions' at ETH Zurich and the 'LMS-Bath Symposium on Inverse Problems and Artificial Intelligence in Medicine.' Thorpe currently seeks PhD students for his research projects. His work bridges theoretical mathematics with practical applications in data-driven fields. Thorpe's research interests include manifold learning in Wasserstein space, PDE-based approaches to data science, and convergence analysis of graph-based learning algorithms. He has contributed to understanding the impact of imputation quality on machine learning models and developed novel transportation distances for pattern recognition. His interdisciplinary approach integrates mathematical rigor with advancements in artificial intelligence. He has co-organized the One World Seminar Series on the Mathematics of Machine Learning and remains active in promoting collaborative research initiatives. Despite no explicitly listed awards, his prolific publication record reflects sustained academic impact. Thorpe’s advising focuses on training students in the intersection of applied mathematics and modern data science techniques.

• Respiratory Medicine
• Pulmonary Fibrosis
• Chronic Obstructive Pulmonary Disease (COPD)
• +3 more

Magnus Sköld is a Professor/Senior Physician at Karolinska Institutet, leading the Idiopathic Pulmonary Fibrosis (IPF) Research Group within the Department of Medicine, Solna. His primary affiliation is with Karolinska Institutet, a renowned medical university. He has held his professorship since 2007 and previously served as Director of Studies at KI from 2005 to 2017. Sköld's research focuses on chronic respiratory disorders, including Chronic Obstructive Pulmonary Disease (COPD) , pulmonary fibrosis , and autoimmune/inflammatory mechanisms . His group explores these conditions through clinical studies, patient cohort analyses, and translational approaches combining immunohistochemistry, omics technologies, and molecular biology. Key projects include: Establishing a national IPF registry and biobank in Sweden, Investigating COPD in never-smokers using the SCAPIS cohort, Linking upper and lower airway pathologies, Studying post-COVID-19 lung outcomes using AI-driven imaging. His work is funded by grants from the Swedish Heart-Lung Foundation, ALF Medicine, and industry collaborations (e.g., Boehringer Ingelheim). He advises PhD students like Lisa Carlson (2021) and mentors researchers in inflammation, pulmonary fibrosis, and COPD mechanisms. Sköld's research also addresses translational challenges, such as developing biomarkers and therapeutic targets for IPF. He collaborates with international teams on projects like the SCAPIS study and imaging-based AI applications for diagnostics.

Stephen RamseyView profile

Associate Professor

• Computational Systems Biology
• Bioinformatics
• Precision Medicine
• +6 more

Stephen Ramsey, an Associate Professor at Oregon State University, holds dual appointments in the School of Electrical Engineering and Computer Science (College of Engineering) and the Department of Biomedical Sciences (Carlson College of Veterinary Medicine). With a PhD in Physics from the University of Maryland, his postdoctoral training in computational genomics at the University of Washington, and professional experience at the Institute for Systems Biology and Center for Infectious Disease Research, Ramsey bridges computational methods with biomedical applications. Education : Ph.D., Physics, University of Maryland; M.S., Physics, University of Maryland; Sc.B., Mathematical Physics, Brown University Ramsey specializes in computational systems biology , focusing on bioinformatics , biomedical knowledge graphs , and precision medicine . His research integrates machine learning , gene regulatory network modeling , and multi-omics data analysis to address challenges in rare disease diagnostics , drug monitoring , and inflammatory disease mechanisms . Current work includes AI-driven biomedical translation and electrochemical biosensor development for non-invasive diagnostics . Recent publications highlight knowledge graph applications in translational biomedicine , causal network inference in clinical-environmental data integration , and cross-species cancer transcriptomics . His team develops tools like RTX-KG2 and PloverDB to standardize biomedical data sharing and semantic reasoning . Scientific Awards : 2019 Zoetis Award (Carlson College of Veterinary Medicine) 2016 NSF CAREER Award 2016 PhRMA New Investigator Award 2010 NIH K25 Mentored Quantitative Research Award Ramsey advises in computational biology courses (CS 446/546) and contributes to biomedical AI through projects like mediKanren for rare disease diagnostics . His NSF-funded research explores gene expression noise and regulatory network dynamics , while NIH and PhRMA grants support his translational medicine initiatives. He leads the Ramsey Laboratory , which develops graph-based reasoning tools for biomedical data translation and multi-omics integration . The lab's work spans comparative oncology models, electrochemical biosensors , and knowledge graph infrastructure for clinical decision support .

Yildiz YilmazView profile

Associate Professor

• Survival Analysis
• Statistical Genetics
• Genetic Epidemiology
• +4 more

Yildiz Yilmaz serves as Associate Professor of Statistics and Deputy Head of Statistics at Memorial University of Newfoundland's Department of Mathematics and Statistics within the Faculty of Science. Holding a PhD from the University of Waterloo, she maintains an active research program at the intersection of statistical methodology and biomedical applications. Education PhD in Statistics, University of Waterloo (2009) MSc in Statistics, Middle East Technical University (2004) MSc in Computer Engineering, Middle East Technical University (2004) BSc in Statistics with minor in Computer Engineering, Middle East Technical University (2002) Her research focuses on developing advanced statistical methods for survival analysis, genetic epidemiology, and causal inference. Key programs include: (1) novel methods for genome-wide prognosis studies of time-to-event phenotypes, (2) genetic association methods using joint/directional models of multiple phenotypes, (3) evaluation of response-dependent sampling designs, and (4) models for multivariate survival times. Her work addresses critical challenges in biomedical research through innovative statistical theory. Recent publications demonstrate consistent focus on colorectal cancer genomics, statistical genetics methodology, and survival analysis applications. Trends show increasing emphasis on multi-phenotype analysis, SNP interaction networks, and time-varying effects in genetic association studies, with significant contributions to copula-based joint modeling approaches. Research Support Natural Sciences and Engineering Research Council (NSERC) Canadian Statistical Sciences Institute (CANSSI) Faculty of Medicine, Memorial University Research and Development Corporation (RDC) of Newfoundland and Labrador Dr. Yilmaz actively mentors students across multiple programs including PhD, MSc, MAS, and undergraduate levels. Her current research group comprises seven students working on statistical genetics and survival analysis projects, building on a track record of successfully supervising numerous graduate students and post-doctoral fellows since 2013.

J. Richard LandisView profile

Professor

• Biostatistics
• Epidemiology
• Clinical Research Informatics
• +6 more

Dr. J. Richard Landis is a Professor of Biostatistics and Statistics at the University of Pennsylvania's Perelman School of Medicine and Wharton School, respectively. As Senior Vice Chair (SVC) of the DBEI Department, he oversees strategic initiatives and serves as a liaison between academic and clinical units. His roles include Director of the Biostatistics Unit at the Center for Clinical Epidemiology and Biostatistics (CCEB), and Director of the Clinical Research Computing Unit (CRCU), supporting translational research infrastructure. He holds advanced degrees from Millersville University (B.S.Ed., 1969), and the University of North Carolina at Chapel Hill (M.S. 1973, Ph.D. 1975). Prior to Penn, he was Professor of Biostatistics at the University of Michigan (1975–1988) and founded Penn State's Center for Biostatistics and Epidemiology (1988–1997). Dr. Landis' research focuses on statistical methodologies for longitudinal and categorical data, with applications to urological, renal, and chronic pain syndromes. He leads NIH-funded Data Coordinating Centers for networks like the MAPP Research Network (2008–2022) and the Chronic Renal Insufficiency Cohort (CRIC) study (2001–2023). His work emphasizes precision medicine, clinical trial design, and translational epidemiology. He has authored over 180 peer-reviewed articles and received prestigious awards, including the Mortimer Spiegelman Gold Medal and the Marvin Zelen Leadership Award. His current projects include the RURAL study (NHLBI) and the HOPE Consortium (NIDDK). Grants & Leadership : NIH-funded DCCs for MAPP, CRIC, and RURAL studies Collaborations : Co-investigator roles in CTSA-Hub cores and multidisciplinary teams Labs/Teams : CRCU, CCEB, and SDCC/SDRC centers

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.

• Animal Genetics
• Genomic Selection
• Livestock Breeding
• +7 more

Samuel Clark is an Associate Professor in Animal Genetics at the University of New England , affiliated with the School of Environmental and Rural Science and the Faculty of Science, Agriculture, Business and Law . His work focuses on leveraging genomic technologies to enhance livestock breeding programs. Research Interests : Dr. Clark's research spans genetic resilience in sheep , genomic selection in beef cattle , and environmental sensitivity in livestock . He investigates how genetic variation interacts with environmental factors to optimize traits like meat quality, feed efficiency, and methane emissions. Publication Trends : Recent articles emphasize genomic prediction accuracy , genotype-environment interactions , and microbial profiling for health traits . His work includes large-scale studies on Angus and Hanwoo cattle and sheep resilience metrics , often integrating bioinformatics and statistical models. Collaborative Efforts : Dr. Clark contributes to initiatives like the Southern Multi-Breed Resource Population , which addresses complex phenotypes for genomic selection, and explores the impact of reproductive technologies on breeding programs .

Andrew HemmingsView profile

Associate Professor

• Equine Behavioral Neuroscience
• Animal Welfare
• Neurophysiology
• +3 more

Dr. Andrew Hemmings is an Associate Professor in Equine Research Leadership at the Royal Agricultural University (RAU). He holds a PhD in The Neurobiology of Equine Stereotypic Behaviour from Aberystwyth University, complemented by an MSc and BSc in Equine/Animal Science. His research focuses on equine behavioral neuroscience , particularly the neurobiological basis of stereotypic behaviors, welfare indicators, and cognitive profiling. He leads collaborative projects on topics like stable lighting effects, racehorse welfare, and neurocognitive differences between horse breeds. Roles: Chair of Research Ethics Committee, Student Support Champion Education: PhD: The Neurobiology of Equine Stereotypic Behaviour (Aberystwyth University) MSc: Equine Science (Aberystwyth University) BSc (Hons): Equine/Animal Science (2:1) Research Interests: Dr. Hemmings pioneers non-invasive techniques such as blink rate analysis to study dopaminergic systems and equine welfare. His work bridges neuroscience and applied equitation science, aiming to improve animal management practices. Key areas include stress physiology, cognitive dysfunction, and the development of brain-based welfare indicators. Collaborations: His projects involve institutions like Hartpury University, Bristol Vet School, and Charles Sturt University. Notable studies include: - Environmental impact of horse grazing systems - Neurocognitive phenotypes of stereotypic behaviors - Predictors of competition performance in equine athletes Teaching & Engagement: He emphasizes knowledge transfer through workshops and public lectures, ensuring research findings inform practical horse management. His lab supports both undergraduate and postgraduate research, with over a dozen supervised students completing MScR and PhD programs. Grants & Impact: His work on blink detection technology and genetic variants in racehorses demonstrates applied research potential. Recent studies address rider weight impacts and equine stress biomarkers, contributing to evidence-based practices in equine welfare.

Robbie WilsonView profile

Professor

• Animal performance
• Behavioral ecology
• Physiological ecology
• +7 more

Professor Robbie Wilson is a Professor at the School of the Environment, The University of Queensland, and serves as Academic Director of the Hidden Vale Wildlife Centre and Director of Partnerships for the Hidden Vale Research Station. His research program spans both ecological and sports science domains, focusing on animal performance across diverse model systems including freshwater fish, crayfish, reptiles, marsupials, and humans. Dr. Wilson's research examines the mechanistic basis of physical performance and its implications for survival and reproductive success. He investigates how organisms respond to environmental variation such as seasonal or long-term temperature changes, and the costs of these responses to other traits. His work explores interactions between behavioral, physiological, and morphological traits to understand how animal performance is optimized, with applications to conservation practices in both urban and wild habitats. Current projects include studying the importance of performance to male mating success in northern quolls, the relative importance of athleticism versus skill in soccer players, and investigating when and why animals engage in deceptive behaviors. Analysis of Professor Wilson's recent publications reveals a strong interdisciplinary approach bridging wildlife conservation and sports science. His work on northern quolls focuses on habitat quality, survival probability, and semelparity, while his sports science research develops protocols for talent identification in soccer using small-sided games and examines the biomechanics of performance. This dual focus demonstrates how principles of animal performance can be applied across evolutionary and human contexts. Current funding through 2026 for manganese exposure effects on children's motor control Multiple ARC Discovery and Linkage Projects Collaborations with Aboriginal communities on Groote Eylandt International partnerships with FA Selangor in Malaysia Professor Wilson has successfully supervised numerous PhD students across diverse topics including koala movement behavior, predator-prey interactions, wildlife sociality, and talent identification in soccer. His extensive grant history demonstrates significant research impact, with current funding through 2026 for projects examining manganese exposure effects on children's motor control, bilby conservation, and koala mortality risk prediction. Leading the Performance Lab at UQ, Professor Wilson and his team investigate performance-related phenomena across the animal kingdom and sporting world. Their field work on Groote Eylandt has yielded important insights into northern quoll ecology, while their soccer research collaborations with FA Selangor in Malaysia demonstrate the global reach of their work.

Barbara FischerView profile

Lecturer

• Evolutionary Medicine
• Obstetric Selection
• Pelvic Evolution
• +4 more

Barbara Fischer is an Elise Richter Fellow and Lecturer at the University of Vienna, Department of Evolutionary Biology. Her research investigates the evolutionary biomechanics of human childbirth, combining geometric morphometrics, comparative anatomy, and evolutionary modeling to resolve the 'obstetric dilemma'. Key contributions include the cliff-edge model of obstetric selection explaining rising Caesarean rates, analyses of sexual dimorphism in hominin pelves, and methods for skeletal age estimation using pubic symphysis morphology. Comparative work extends to Darwin's finches' self-medication behaviors and stickleback adaptation plasticity. She develops statistical approaches for morphological integration studies and teaches biostatistics, mathematical biology, and evolutionary medicine. Recognized with an Elise Richter Fellowship and science communication awards.

• Genomics
• Bioinformatics
• Statistical Genetics
• +5 more

Dr. John Shaffer is an Assistant Professor at the University of Pittsburgh School of Dental Medicine, specializing in genetic research. His work focuses on applying statistical and bioinformatics methods to understand genetic contributors to complex diseases like orofacial clefts, dental caries, and craniofacial phenotypes. He explores epigenetic mechanisms, gene-environment interactions, and 3D morphological analysis. Education: PhD in Human Genetics, University of Pittsburgh (2008). Research interests include: Heritability and linkage analysis Genetic architecture of craniofacial traits Epigenomic studies in cancer and neurotrauma recovery Public health disparities in oral care Genetic epidemiology of dental diseases Publications highlight advancements in: Cranial vault morphology genetics Orofacial cleft risk loci identification Genetic basis of dental caries Environmental influences on oral health Collaborations include the GLIDE2 oral health genomics consortium and multi-ethnic GWAS initiatives.