جست‌وجوی استاد و پژوهشگر

Nadine Octavia Patricia Parkerمشاهده پروفایل

پژوهشگر

• Psychiatric Genetics
• Neuroimaging
• Precision Psychiatry
• +۵ مورد دیگر

Nadine Parker is a Researcher at the Centre for Precision Psychiatry within the Faculty of Medicine at the University of Oslo. She is affiliated with Oslo University Hospital HF - Ullevål, Section for Precision Psychiatry, where she conducts cutting-edge research at the intersection of psychiatric genetics, neuroimaging, and precision medicine. Her work primarily focuses on understanding the genetic architecture of mental health disorders and their biological underpinnings. Dr. Parker's research interests center on psychiatric genetics, neuroimaging biomarkers, and precision psychiatry approaches. Her work explores the genetic overlap between various mental health conditions including depression, schizophrenia, bipolar disorder, and substance use disorders. She investigates how these disorders relate to physiological markers like white blood cell counts, inflammatory markers, and brain structure. Her research employs advanced genomic techniques including genome-wide association studies, polygenic risk scoring, and cross-disorder pleiotropy analysis to uncover shared biological mechanisms. Her publication record shows a strong focus on genetic architecture of psychiatric disorders, with numerous high-impact publications in journals like Nature Communications, Biological Psychiatry, and Molecular Psychiatry. Her work often examines how mental health conditions intersect with immune function, metabolic processes, and brain structure. A recurring theme across her publications is the identification of shared genetic mechanisms across seemingly distinct disorders, suggesting common biological pathways that could inform new treatment approaches. Dr. Parker is an active collaborator within the precision psychiatry research community, frequently working with colleagues including Guy Hindley, Alexey Shadrin, and other researchers at the University of Oslo. Her research is supported by institutional resources at Oslo University Hospital and likely external funding sources given the scale of her collaborative projects involving large datasets and international consortia. She is part of the Precision Psychiatry research group at Oslo University Hospital, which focuses on developing more targeted approaches to psychiatric diagnosis and treatment through integration of genetic, neuroimaging, and clinical data. This group works at the forefront of translating genetic discoveries into clinically actionable insights for mental health care.

• Physical Activity
• Step Count
• Accelerometry
• +۳ مورد دیگر

Craig Speirs is a researcher in the Department of Computer and Information Sciences at the University of Strathclyde, specializing in physical activity monitoring through wearable sensor technology and accelerometry. He holds a Doctor of Philosophy (PhD) in Computer and Information Sciences, with research focused on translating sensor data into meaningful health metrics. His educational background centers on computational methods for human movement analysis. His primary research interests involve step-count algorithms, posture distribution modeling, and cadence analysis in free-living environments. This work bridges computer science and health sciences to develop objective measures of physical performance outside clinical settings, with applications in epidemiology and preventive healthcare. Key methodologies include accelerometer-based activity tracking and large-cohort data analysis. Recent publications demonstrate consistent trends in leveraging real-world sensor data (e.g., BCS70 cohort studies) to establish stepping patterns as biomarkers of health. His 2023-2024 work emphasizes free-living movement assessment, moving beyond controlled clinical environments to capture authentic daily activity. No scientific awards were documented in the provided profile Student advising activities and research grant details are not specified in the available information, though his collaborative publications indicate engagement with multidisciplinary teams across health and engineering domains. Research infrastructure details are limited, but his work aligns with sensor-based health monitoring initiatives, likely involving partnerships with medical institutions given the hospital and clinical context referenced in his publications.

• Statistical Learning
• Machine Learning
• Hypothesis Testing
• +۳ مورد دیگر

Peter Grünwald is a Full Professor of Statistical Learning at Leiden University's Mathematical Institute and senior researcher in the machine learning group at Centrum Wiskunde & Informatica (CWI) in Amsterdam. His pioneering work focuses on developing flexible statistical inference frameworks using e-values and e-processes, creating a novel middle ground between Bayesian and frequentist paradigms that allows optional continuation in data analysis. Grünwald's research revolutionizes statistical methodology through: Development of anytime-valid tests and confidence intervals Novel approaches to hypothesis testing with data-driven significance levels E-value based methods for safe sequential testing Applications in clinical trials, causal inference, and high-dimensional data His publications demonstrate consistent focus on developing robust statistical frameworks applicable to flexible experimental designs, with recent work appearing in PNAS, JRSSB, and Annals of Statistics. Research consistently addresses fundamental challenges in statistical evidence evaluation across medical, social, and computational domains. Major Scientific Recognition: ERC Advanced Grant (2024) for revolutionary statistical theory development NWO Top Award (2016) for outstanding contributions to statistics As primary advisor, Grünwald has supervised PhD candidates including Tyron Lardy, Yunda Hao, and Rosanne Turner on topics spanning e-statistics, conditional independence testing, and stratified data analysis. He leads methodological development for the R package 'safestats' and collaborates internationally on statistical foundations.

Kirsten Cowanمشاهده پروفایل

مدرس ارشد

• Immersive Technologies
• Consumer-Brand Interactions
• Virtual Reality
• +۷ مورد دیگر

Kirsten Cowan is a Senior Lecturer in Marketing at the University of Edinburgh's Business School, where she joined in November 2018. Previously, she was an Assistant Professor of Marketing at NEOMA Business School (Grand Ecole) in France from 2015 to 2018. She earned her PhD in Marketing with expertise in quantitative consumer research from the University of North Texas in 2015. Dr. Cowan's educational background includes a PhD in Marketing from the University of North Texas (2015). Her academic journey spans institutions in the United States, France, and the United Kingdom, reflecting her international perspective on marketing research and education. Her research primarily investigates how consumers interact with brands using immersive technologies like virtual and augmented reality, and how these interactions affect information processing and decision-making. Taking a positivist approach, her work focuses on three main domains: consumer-brand relationships, retailing, and heritage. She examines branding within augmented and virtual reality, website design, social media, and artificial intelligence, with a preference for experimental methodologies. Her research fingerprint prominently features Virtual Reality (100%), Construal (100%), Decision Making (50%), and Systematic Review (42%). Dr. Cowan's recent publications reveal a strong trend toward examining virtual reality applications in retailing and consumer behavior, with particular attention to construal level theory as a framework for understanding how consumers process information in immersive environments. Her work bridges theoretical marketing concepts with practical applications in emerging technologies. Best Paper Award (21 Sept 2019) Best Paper Award AMA, 'Toward a Typology of Virtual Reality (VR) Experiences in Retailing' (2024) Best Paper Award GBC, 'It's Almost as if I'm There Now' (2023) Honorable Mention Paper (4 Sept 2018) Most Innovative Paper (10 May 2019) Dr. Cowan currently supervises six PhD students, two of whom are in their final year. She serves as Principal Investigator for the British Academy-funded project 'Conservation through Innovative Technology: Explaining Why and When VR Influences Sustainable Behaviours' (2024-2026). She has secured multiple research grants supporting her investigations into virtual reality's impact on consumer behavior and sustainable practices. She is not accepting PhD inquiries for 2023/24 but has capacity for one PhD student in 2024/25 with a focus on immersive technologies. Dr. Cowan leads a research team focused on immersive technologies and consumer behavior, with particular emphasis on virtual and augmented reality applications in marketing contexts. Her work involves collaborations across disciplines and with industry partners to translate theoretical insights into practical applications, bridging the gap between academic research and real-world marketing strategies.

Josef Jochumمشاهده پروفایل

استاد

• Astroparticle Physics
• Dark Matter
• Neutrino Physics
• +۳ مورد دیگر

Prof. Josef Jochum is a Professor at the University of Tübingen's Physics Institute, where he leads a research group focused on experimental astroparticle physics. His group conducts experiments at underground laboratories and develops cryogenic detector technologies for fundamental physics research. Research Focus: Prof. Jochum's primary research areas include dark matter detection (notably through the CRESST and GERDA collaborations), neutrino physics, and development of superconducting tunnel junction detectors. His work bridges particle physics, cosmology, and advanced instrumentation. Publication Trends: Analysis of recent publications reveals consistent focus on dark matter detection methodologies, cryogenic sensor development, and background characterization in underground experiments. Over 75% of recent papers involve experimental validations of detector technologies for astroparticle physics applications. Research Infrastructure: Leads the Jochum working group with multiple staff members and technical facilities including cryogenic laboratories and underground measurement rooms. The group participates in international collaborations like CRESST, GERDA, and Double Chooz.

• Sports Analytics
• Sports Statistics
• Hidden Markov models
• +۲ مورد دیگر

Rouven Michels is a researcher affiliated with the Department of Statistics at the Technical University of Dortmund, located in Germany. He is part of the working group led by Prof. Dr. Andreas Groll, focusing on advanced statistical methodologies for data analysis. His primary research interests include Sports Analytics, particularly applying statistical techniques to sports data, Hidden Markov models for sequential data analysis, State-space models for dynamic systems, and the Modelling of bivariate count data in various applications. As a member of the Faculty of Statistics, Michels contributes to the development and application of statistical methods for big data challenges. His work bridges theoretical statistical modeling with practical implementations in sports science and other domains requiring complex data analysis frameworks. Though no specific articles or awards are listed here, his involvement in the department underscores his active role in advancing statistical research.

Cristian Tudorel Badeaمشاهده پروفایل

استاد

• Medical Imaging
• Biomedical Engineering
• Radiology
• +۵ مورد دیگر

Cristian Tudorel Badea is a Professor in Radiology and Biomedical Engineering at Duke University. He is a member of the Duke Cancer Institute and leads the Quantitative Imaging and Analysis Lab. His research focuses on micro-CT , photon-counting CT , and quantitative imaging biomarkers for preclinical studies. Education: Ph.D. in Electrical Engineering, University of Patras (Greece), 2001 Research Interests: Dr. Badea specializes in advanced imaging technologies, including deep learning-enabled CT reconstruction , nanoparticle contrast agents , and cardiac imaging in murine models. His work bridges preclinical research and clinical translation , particularly in cancer and cardiovascular diseases. Article Trends: His recent publications emphasize photon-counting micro-CT for cardiac perfusion , deep learning denoising algorithms , 5D imaging , and hybrid spectral CT techniques. Key subfields include tumor vasculature analysis , multi-contrast imaging , and neural network applications in preclinical settings. Labs & Collaborations: He collaborates extensively with the Duke Cancer Institute and leads the Quantitative Imaging and Analysis Lab. His work integrates multimodal imaging (CT, MRI, SPECT) and computational tools for co-clinical trials.

• Parallel Computing
• High-Performance Computing
• Computer Architecture
• +۳ مورد دیگر

Prof. Laxmikant V. Kale is a Research Professor and Paul and Cynthia Saylor Professor Emeritus at the University of Illinois at Urbana-Champaign's Department of Computer Science within the College of Engineering. He specializes in parallel computing, high-performance systems, and scientific applications. His research focuses on runtime systems like Charm++, NAMD, and OpenAtom, emphasizing scalability and efficiency in distributed environments. Education: B. Tech., Electrical Engineering, Banaras Hindu University (1977) M.E., Computer Science, Indian Institute of Science (1979) M.S. & Ph.D., Computer Science, SUNY Stony Brook (1985) Research Interests: Parallel programming models, adaptive runtime systems, distributed and GPU computing, and applications in molecular dynamics, computational biology, and astrophysics. His work bridges theory and practice, aiming to optimize performance for exascale systems. Awards: Gordon Bell Award (2002) IEEE Fellow (2010) Sidney Fernbach Award (2012) HPCC Challenge Award (2011) Grants & Projects: NIH-funded NAMD development, NSF projects on ParaTreet and OPEN ATOM, DOE's XPACC Center, and collaborations with NCSA. He advises students on HPC and molecular dynamics, with notable advisees including Maya Taylor and Jessica Williams. Labs & Teams: Director of the Parallel Programming Laboratory, leading Charm++ and NAMD development. Collaborates with teams on OpenAtom for ab initio simulations and EpiSimdemics for disease modeling.

Emmeke Aartsمشاهده پروفایل

دانشیار

• Data Science
• Longitudinal Data Analysis
• Machine Learning
• +۳ مورد دیگر

Dr. Emmeke Aarts is an Associate Professor in Statistics at Utrecht University's Department of Methodology and Statistics, within the Faculty of Social and Behavioural Sciences. She serves as Director of Education for the department and coordinates several academic programs including the Research Master Methodology and Statistics. Her research focuses on developing novel statistical methods for intensive longitudinal data, particularly multilevel hidden Markov models and real-time prediction algorithms in healthcare. Key areas include personalized latent dynamics and cardiovascular disease monitoring. She has received a 2024 fellowship for her work on depression dynamics in emerging adults. A资深的统计学家 and educator, she supervises PhD students and contributes to interdisciplinary collaborations with medical institutions like UMC Utrecht. Education: Research Master in Methodology and Statistics (cum laude, Utrecht University) and a PhD in interdisciplinary statistics and neuroscience (VU University Amsterdam). Postdoctoral roles included positions at the Max Planck Institute and TNO. Research Themes: Applied Data Science, Multilevel Analysis, Machine Learning, and Hidden Markov Models. Her work bridges methodological innovation with practical applications in mental health and cardiology. Current projects include Health-Holland grant-funded collaborations on heart failure prediction and real-time deterioration monitoring. Teaching: Coordinates courses such as 'Introduction to Multilevel Modelling' and leads summer schools on structural equation modeling. Supervises over 10 graduate students and provides statistical consultation for biomedical and social science projects. Grants & Awards: 2024 Fellowship for personalized depression dynamics research; Health-Holland grants for heart failure machine learning projects. Extensive record of interdisciplinary funding and academic service roles in education committees. Labs/Teams: Active in the Utrecht Platform for Applied Data Science and collaborates with UMC Utrecht Cardiology Department. Leads methodological development for multilevel HMM applications in behavioral and biomedical data.

Silvia Paniمشاهده پروفایل

مدرس ارشد

• Medical Imaging
• Radiation Physics
• X-ray Techniques
• +۴ مورد دیگر

Dr. Silvia Pani is a Senior Lecturer in Applied Radiation Physics at the University of Surrey, part of the School of Mathematics and Physics. She holds a Laurea in Physics from the University of Trieste (1996) and a PhD in Physics (2001). Her career includes postdoctoral work on synchrotron beamlines, a Marie Curie Fellowship at University College London (2004), and roles at Queen Mary University of London and Barts NHS Trust. She joined Surrey's Physics Department in 2008 and became Senior Lecturer in 2017. Roles: Programme Director (MSc Medical Physics), Radiation Protection Supervisor Education: Postgraduate Certificate in Academic Practice (2010) Research focuses on X-ray imaging techniques for medical and security applications, including breast imaging, quantitative spectroscopic methods, and adaptation of synchrotron techniques to laboratory settings. Key collaborations involve institutions like Rutherford Appleton Labs, University of Manchester, and the Home Office. Publications emphasize hyperspectral detectors, scatter-free imaging, and CT optimization. Her work combines detector development with clinical applications, aiming to improve diagnostic accuracy while reducing radiation dose.

Miles Lopesمشاهده پروفایل

دانشیار

• Bootstrap Methods
• High-Dimensional Inference
• Error Estimation
• +۳ مورد دیگر

Miles Lopes is an Associate Professor in the Department of Statistics at the University of California, Davis. His research focuses on developing and analyzing bootstrap methods for high-dimensional statistical problems, with particular attention to error estimation in randomized algorithms and high-dimensional inference. He holds a Ph.D. from UC Berkeley. Research interests include bootstrap approximation techniques, high-dimensional covariance estimation, spectral statistics, and applications of randomized algorithms in numerical linear algebra. His work bridges theoretical statistics and computational methods, addressing challenges in modern data analysis. Recent publications emphasize bootstrap methods for eigenvalue analysis in high-dimensional PCA, operator norm approximation, and robust statistical inference in complex models. His methodologies have applications in functional data analysis, multivariate testing, and software development for randomized algorithms.

Vincent Tabard-Cossaمشاهده پروفایل

استاد

• Biophysics
• Nanopore Science
• Nanofluidics
• +۴ مورد دیگر

Dr. Vincent Tabard-Cossa is a Full Professor in the Department of Physics at the University of Ottawa and holds a University Research Chair in Nanoscale Biophysics and Nanopore Science. He is also cross-appointed with the Department of Chemistry and Biomolecular Sciences and serves as Vice-Dean Innovation & Strategic Partnerships in the Faculty of Science. His research focuses on developing novel techniques using solid-state nanopores for single-molecule analysis in biophysical systems, with applications in medical diagnostics, data storage, and biosensing. He co-founded Northern Nanopore Instruments (acquired by Oxford Nanopore Technologies) and has pioneered methods like controlled breakdown nanopore fabrication. Education: B.Sc. and Ph.D. in Physics from McGill University (2000, 2006), followed by postdoctoral work at UBC and Stanford University. Key honors include the CAP Industrial and Applied Physics Medal (2021), Royal Society of Canada membership (2019), and Ontario Early Researcher Award (2016). Research Interests: Experimental nanobiophysics, nanopore science, nanofluidics, single-molecule force spectroscopy, and interdisciplinary applications in engineering and chemistry. His lab emphasizes translating fundamental discoveries into practical tools, such as digital immunoassays and nanopore-based diagnostics. Publications highlight advancements in nanopore fabrication, DNA/protein translocation dynamics, and biomedical applications. Over 150+ students and researchers have been mentored, with active collaborations across academia and industry. The T.-Cossa Lab fosters a multidisciplinary environment, integrating physics, chemistry, and engineering to tackle complex problems in nanotechnology.

Francisco Blasquesمشاهده پروفایل

استاد

• Econometrics
• Statistics
• Data Science
• +۵ مورد دیگر

Francisco Blasques is a Full Professor of Econometrics and Data Science at the Vrije Universiteit Amsterdam (VU) and a Research Fellow at the Tinbergen Institute. He holds leadership roles, including Co-founder of Churned (QuantIQ) and datastuff BV, and served as TI Doctoral Director for the School of Business and Economics. His research focuses on econometrics, statistics, and data science, with expertise in time-series analysis, financial econometrics, and nonlinear dynamics. Teaching includes advanced econometrics courses at VU and Tinbergen Institute, covering topics like time-series modeling and financial econometrics. His PhD supervision spans over 15 students since 2012, focusing on econometric methods and applications. Research contributions emphasize score-driven models, volatility analysis, and spatial econometrics. Key publications address topics such as natural disaster impacts on economies, robust volatility estimation, and fusion of experimental/observational data. Awards include the NWO-VIDI Grant (2019) for methodological innovations in econometrics. Professional activities include ancillary roles as Partner at QuantiQ BV and Director at Metyis, alongside academic duties. His work contributes to UN Sustainable Development Goals through economic resilience and policy analysis.

• Quantum Computing
• Spin Qubits
• Semiconductor Nanostructures
• +۵ مورد دیگر

Dominik M. Zumbühl is a Professor in the Department of Physics at the University of Basel, where he leads the Quantum Coherence Lab. His research group is part of several prestigious initiatives including the NCCR SPIN (Swiss national research program), the Swiss Nanoscience Institute (SNI), the Basel QC2 Center for Quantum Computing and Quantum Coherence, and the PhD School Quantum Computing and Quantum Technologies (QCQT). The lab maintains extensive collaborations with leading research groups worldwide including those at Harvard University, ETH Zurich, Princeton University, University of Chicago, and Aalto University. Zumbühl's research focuses on low-temperature quantum transport experiments investigating quantum coherence, electron and hole spins, as well as nuclear spins and interactions in semiconductor nanostructures. His work spans multiple cutting-edge areas including spin qubits in semiconductor nanostructures for quantum computing, microkelvin temperature experiments, and the study of novel quantum matter in nanowires and 1D systems. His group has achieved notable milestones such as record-breaking low-temperature measurements (224±7 microKelvin) and demonstrated high-temperature operation (above 4 Kelvin) of hole spin qubits in fin field-effect transistors. His recent publications reveal a strong trend toward integrating machine learning with quantum device control and characterization, while continuing fundamental investigations into spin-orbit coupling, quantum transport phenomena, and novel quantum phases. His work bridges theoretical physics with experimental implementations, with significant contributions to both fundamental understanding and practical quantum device development. Part of NCCR SPIN (Swiss national research program developing small, fast and scalable qubits in Si and Ge) Member of Basel QC2 Center for Quantum Computing and Quantum Coherence Member of PhD School Quantum Computing and Quantum Technologies (QCQT) Member of Georg H. Endress Postdoc cluster Freiburg Basel EUCOR Zumbühl actively supervises PhD students and postdoctoral researchers, with several recent PhD defenses and new students joining his group. His lab maintains strong connections with industry partners including BASPI (Basel Precision Instruments GmbH) and IBM Research Zurich. The group utilizes advanced fabrication techniques and collaborates with multiple material growth facilities to develop state-of-the-art quantum devices.

Wojciech B. Zbijewskiمشاهده پروفایل

دانشیار

• Medical Imaging
• Quantitative Imaging
• Musculoskeletal Health
• +۵ مورد دیگر

Wojciech B. Zbijewski is an Associate Professor in the Department of Biomedical Engineering at Johns Hopkins University, affiliated with the Whiting School of Engineering . As the Co-Director of the Biomedical Engineering PhD Program, his research focuses on improving quantitative diagnostic imaging techniques, particularly using x-ray modalities such as radiography, tomosynthesis, CT, and cone-beam CT. His Quantis Lab develops advanced computational models and experimental methods to optimize imaging systems for musculoskeletal and pulmonary applications, emphasizing radiomics, shape analysis, and bone health biomarkers. Education : PhD in Medical Imaging from the University of Utrecht (2006), MS in Medical Physics from the University of Warsaw (2001). Research Interests : His work spans quantitative imaging system optimization, bone and joint health biomarkers, and AI-driven image reconstruction. Key areas include photon-counting CT, dual-energy imaging for bone marrow edema detection, and robotic-assisted surgical navigation using real-time 3D-2D registration. His lab collaborates with the Carnegie Center for Surgical Innovation to advance clinical imaging solutions. Notable Contributions : Led a groundbreaking program on bone health imaging (2017), pioneered motion compensation algorithms for interventional CT, and developed ultra-high-resolution CT techniques for trabecular bone analysis. His research bridges computational methods (e.g., generative AI models) with clinical applications, aiming to improve diagnostic accuracy and surgical outcomes. Labs/Teams : Quantis Lab (focusing on advanced imaging and AI).