جست‌وجوی استاد و پژوهشگر

Joan Guardia Olmosمشاهده پروفایل

استاد

• Quantitative Psychology
• Statistical Data Analysis
• Research Methodology
• +۴ مورد دیگر

Joan Guardia Olmos is a Professor at the Faculty of Psychology of the University of Barcelona , Spain, specializing in quantitative methodology and research methodology. He has been teaching since 1984 and holds a PhD in Psychology from the University of Barcelona, with postgraduate studies in Statistical Data Analysis at the University of Essex, UK. He has supervised over 20 doctoral theses and delivered over 100 seminars across Europe and America. His research focuses on multivariate statistical techniques, structural model estimation, neuropsychological methodologies, and biostatistical applications in health. He coordinates the Quantitative Psychology Research Group (SGR 266) and the Learning Health Statistics Teaching Innovation Group (UB/024) . Recent research projects include studying brain connectivity networks via fMRI, cognitive reserve in Down Syndrome patients, and physical activity programs for individuals with intellectual disabilities (P-PALs). He has authored over 250 indexed articles and serves as evaluator for national/international research agencies. Leadership roles include Vice-Rector for Students and participation in university governance. Teaching Roles: Courses on Biostatistics, Research Methodology, Neurosciences, and Health Psychology at undergraduate/graduate levels. Grants: Coordinated projects funded by Spain's Ministry of Science (PGC2018-095829-B-I00) and EITHealth (FBG 600223). Innovation: Led 'Information Competence in Psychology Degree' (2015-2021) and 'Learning Health Statistics' initiatives.

• Forecasting
• Time Series Analysis
• Computational Statistics
• +۶ مورد دیگر

Rob Hyndman is a Professor of Statistics at Monash University , Australia, in the Department of Econometrics and Business Statistics. He is an accredited statistician with the Statistical Society of Australia and a Fellow of both the Australian Academy of Science and the Academy of the Social Sciences in Australia . As former Editor-in-Chief of the International Journal of Forecasting (2005-2018), he has shaped academic discourse in his field. Research Interests : Forecasting, time series analysis, computational statistics, anomaly detection, and exploratory data analysis with applications in energy analytics, data visualization, and hierarchical forecasting Education : Bachelor of Science (Honours) and PhD from the University of Melbourne Leadership : Director of the International Institute of Forecasters (2005-2018) His scientific contributions span business analytics, machine learning, demography, and computational statistics, with recent publications focusing on responsible forecasting, hierarchical time series modeling in emergency medical services, and advanced decomposition algorithms like MSTL. His work addresses multiple UN Sustainable Development Goals through applications in public health (forecasting pandemic cases), energy (demand forecasting), and social equity (indigenous population life expectancy estimation). He has received prestigious awards including the Moran Medal (2007, Australian Academy of Science) Pitman Medal (2021, Statistical Society of Australia) Australian Awards for University Teaching (2022) Vice-Chancellor's Award for Innovation in Learning (2020) HP Innovation Research Award (2010) Vice Chancellor's Award for Postgraduate Supervision (2008) and multiple Dean's awards. Rob has supervised over 30 PhD and Masters students while developing open-source forecasting tools and organizing annual WOMBAT workshops on business analytics.

Eleanor Knoxمشاهده پروفایل

مدرس ارشد

• Philosophy of Physics
• Quantum Gravity
• Foundations of Spacetime Theories
• +۳ مورد دیگر

Dr. Eleanor Knox is a Senior Lecturer in Philosophy at King's College London, affiliated with the Department of Philosophy within the Faculty of Arts & Humanities. She holds a Senior Visiting Fellowship at the University of Pittsburgh's Center for Philosophy of Science (2022-23). Her academic journey includes a BA, BPhil, and DPhil from Oxford University, followed by fellowships at the Institute of Philosophy (London) and as a Leverhulme Early Career Fellow. Her research focuses on the foundations of spacetime physics, particularly advocating Spacetime Functionalism to address challenges in classical and quantum gravity theories. She explores theoretical relationships, emphasizing explanatory novelty through variable transformations in physics. Key contributions include analyzing emergent phenomena and inter-theoretical connections. Eleanor has received the Cushing Memorial Prize (2015) for her work in History and Philosophy of Physics. Her public engagement includes BBC documentary advisory roles, debates on time and physics (e.g., with Lee Smolin and Michael Duff), and online courses on relativity. She actively communicates science through podcasts and festivals like How The Light Gets In . Her teaching and research interests span philosophy of physics, spacetime theories, and the philosophy of science. She maintains a website ( eleanorknox.com ) for further engagement and resources.

• Quantum Information Science
• Quantum Computing
• Quantum Information Theory
• +۲ مورد دیگر

Hlér Kristjánsson is an Adjunct Professor at the Faculty of Arts and Science within the Department of Computer Science and Operations Research at Université de Montréal. His research focuses on quantum information science, quantum computing, and theoretical physics, with affiliations at Mila - Quebec Artificial Intelligence Institute. He teaches IFT6760C – Seminar in Machine Learning and oversees research projects funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). Key projects include exploring causal structures and functional programming in quantum computation (2025–2031), supported by NSERC Discovery Grants and Tremplin programs. His work bridges quantum mechanics with computational models, addressing topics like quantum switch simulation, indefinite causal order, and resource-theoretic frameworks. Collaborations involve experimental quantum communication enhancements and second-quantized Shannon theory. Teaching responsibilities span master’s (e.g., Machine Learning), doctoral programs in computer science, and specialized DESS programs. Funding milestones include a 7-year NSERC grant (PVX20965/RGP) and a PVXXXXXX/DGECR initiative. No scientific awards are explicitly listed, though his contributions to quantum gravity as a communication resource and routed quantum circuits highlight innovative directions. Research is anchored at Mila, emphasizing interdisciplinary applications in AI and quantum systems.

Tadamoto Isogaiمشاهده پروفایل

استادیار

• Cancer Biology
• Cell Biology
• Biomedical Engineering
• +۳ مورد دیگر

Tadamoto Isogai is an Assistant Professor at the Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center since 2023. His research integrates high-resolution 3D single-cell imaging with computational methods to study cytoskeletal architecture's role in cancer cell survival. He applies mathematical models inspired by financial economics to analyze gene pathway dynamics without experimental perturbation, focusing on actin-binding proteins in membrane protrusions. Dr. Isogai earned his PhD from The Netherlands Cancer Institute in Amsterdam. His work bridges cell biology, biophysics, and bioinformatics, emphasizing systems-level approaches to understand mechanotransduction and cell migration. Notable contributions include uncovering CLIC4's role in RhoA signaling and developing algorithms for 3D microscopy analysis. His recent studies explore actin-membrane interactions during cell protrusion initiation, mechanical regulation of glycolysis, and systems biology approaches to lamellipodial actin networks. Research outputs span Nature , Cell Systems , and Philosophical Transactions of the Royal Society , reflecting interdisciplinary impact.

Kristi Lazar Cantrellمشاهده پروفایل

استاد

• Biochemistry
• Biophysical Chemistry
• Peptide Synthesis
• +۳ مورد دیگر

Dr. Kristi Lazar Cantrell is a Professor of Chemistry and Pre-Health Adviser at Westmont College. She holds a B.S. in Chemistry from Westmont College, an M.S. from Princeton University, and a Ph.D. from the University of Chicago. She conducted postdoctoral research at Genentech Inc. Her research focuses on protein aggregation mechanisms, particularly in neurodegenerative diseases like Alzheimer’s and ALS, with specialties in biophysical chemistry and peptide synthesis. She serves on the executive council of the Los Padres section of the American Chemical Society and advises pre-health students. During Spring 2025, she is on sabbatical. Education: B.S. Chemistry, Westmont College M.S. Chemistry, Princeton University Ph.D. Chemistry, University of Chicago Research interests emphasize protein aggregation dynamics, amyloid fibril formation, and their pathological roles in diseases. Her studies explore peptide interactions, lipid binding specificity, and therapeutic molecule design targeting ALS and Alzheimer’s. Over 15 recent publications analyze amyloid oligomers, tau proteins, and TDP-43 aggregates, highlighting cross-disease mechanisms and computational drug discovery approaches. Advising and grants: As pre-health adviser, she guides students pursuing medical careers. Her research has involved collaborations with biotechnology firms and academic institutions, though specific grant details are not provided. No scientific awards are listed in the provided materials.

Guillaume Charpiatمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Computer Vision
• +۸ مورد دیگر

Guillaume Charpiat is a Research Officer at INRIA Saclay and Head of the Data Science department at the LISN laboratory (Paris-Saclay University). He holds a PhD in computer vision from the École Polytechnique (2006), conducted under Olivier Faugeras and Renaud Keriven. His postdoctoral research at the Max Planck Institute for Biological Cybernetics focused on statistical learning. He joined the TAO/TAU team in 2015, specializing in machine learning and optimization. His research spans deep learning theory, neural networks, and applications in satellite imagery, genetics, fluid dynamics, and medical imaging. Education: PhD in Computer Vision (École Polytechnique, 2006), Postdoc in Statistical Learning (Max Planck Institute). Teaching includes advanced deep learning courses at MVA/CentraleSupélec and AI master's programs. Research interests emphasize neural architecture design, generative models, and interdisciplinary applications. His work bridges machine learning with physics and biology, addressing challenges like dynamical systems modeling and population genetics inference. Key contributions include neural architecture growth strategies, equivariant GNNs for materials science, and deep learning frameworks for genomic data. Advised over 20 students across PhD and master's levels, focusing on topics like neural networks, causal discovery, and generative models. Active in seminars and interdisciplinary collaborations, he co-leads the TAU team's research on machine learning and optimization.

Shyamalika Gopalanمشاهده پروفایل

استادیار

• Genetics
• Evolutionary Biology
• Epigenetics
• +۵ مورد دیگر

Dr. Shyamalika Gopalan is an Assistant Professor in the Department of Genetics & Biochemistry at Clemson University, affiliated with the Center for Human Genetics. She joined Clemson in 2023 after postdoctoral training at the University of Southern California (Center for Genetic Epidemiology) and Duke University (Department of Evolutionary Anthropology). Her research focuses on understanding the interplay between genetics, environment, and evolutionary forces shaping human phenotypic diversity and aging processes. Dr. Gopalan holds a B.Sc. in Biology from McGill University (2012) and a Ph.D. in Ecology and Evolution from Stony Brook University (2019). Her doctoral work explored evolutionary trajectories of human populations through genomic analysis. Her research interests include: Computational modeling of genetic and epigenetic variation Drivers of phenotypic change in domesticated canids Evaluation of complex trait evolution via genomic tools Epigenetic mechanisms influencing human aging Genetic admixture patterns across populations Recent work highlights contributions to understanding: Epigenetic aging biomarkers in diverse populations Genetic differentiation impacts on methylation studies Demographic history of Eastern African hunter-gatherers COVID-19 susceptibility genes through integrative genomics No scientific awards are explicitly listed in the provided materials. While no student advisees are noted, her research lab actively pursues studies in aging, domestication biology, and computational evolution modeling. Lab activities include developing epigenetic clocks and genomic tools for population analysis.

• Mathematical Biology
• Mathematical Ecology
• Applied Mathematics
• +۶ مورد دیگر

Graeme Boswell is a Senior Lecturer in Mathematics at the University of South Wales, affiliated with the Faculty of Computing, Engineering and Science. He leads the BSc Mathematics program and focuses on applied mathematics, particularly in mathematical biology and ecology. His research explores fungal interactions, liquid crystals, and system dynamics, with a strong emphasis on mathematical modeling of biological systems. He has contributed to over 18 peer-reviewed articles since 2001, covering topics ranging from fungal mycelia networks to liquid crystal structures. His research interests include modeling nutrient influence on fungal competition, causal loop impacts in system dynamics, and the behavior of hyphal networks in heterogeneous environments. Recent work has addressed siderophore-iron interactions in fungi and the structural analysis of smectic-A liquid crystals. Boswell's articles often combine numerical solutions with ecological and physical systems modeling, reflecting his interdisciplinary approach. He maintains an active research profile with collaborations in mathematical biology and applied mathematics, and his work has been cited in numerous academic contexts, including patents and Wikipedia. His research page and Pure profile provide further details on his publications and ongoing projects.

Stephen Mannمشاهده پروفایل

پژوهشگر

• Philosophy of Science
• Cultural Evolution
• Linguistic Diversity
• +۵ مورد دیگر

Stephen Mann is a computational modeler and philosopher of science at the Max Planck Institute for Evolutionary Anthropology's Department of Linguistic and Cultural Evolution (DLCE) since 2024. Previously, he held postdoctoral positions at the LOGOS Group (University of Barcelona, Spain) and the Surrey Morphology Group (University of Surrey, UK), while maintaining guest researcher status at DLCE. He earned his PhD in philosophy from the Australian National University (2020) after withdrawing from King's College London (2015) and holds an M.Sci in Mathematics and Philosophy from the University of Bristol (UK). Current Affiliation: Max Planck Institute for Evolutionary Anthropology (DLCE), Leipzig Previous Affiliations: University of Barcelona (LOGOS Group), University of Surrey (Surrey Morphology Group) Education: PhD (Philosophy) - Australian National University, M.Sci - University of Bristol Stephen's research focuses on integrating agent-based models with historical data to analyze linguistic diversity in the Pacific. His work bridges teleosemantics, the free energy principle, and evolutionary biology, particularly examining biological signaling systems and their philosophical implications. He also explores the role of selection in cultural evolution and the application of communication theory to representation frameworks. His recent publications span evolutionary modeling, AI ethics, and theoretical biology, with a thematic emphasis on information theory, cultural transmission, and computational methods. Stephen has guest-edited a topical collection on the free energy principle and actively promotes open science through tools like Evoke, a Python package for evolutionary signaling games. Scientific Awards: Juan de la Cierva postdoctoral fellowship Outreach Contributions: Articles in The Conversation, Life Beyond Us anthology, and AI ethics interviews

Eleftherios Gkioulekasمشاهده پروفایل

استاد

• Fluid Turbulence
• Theoretical Physics
• Statistical Mechanics
• +۳ مورد دیگر

Dr. Eleftherios Gkioulekas is a Professor of Mathematics and Undergraduate Program Coordinator at the University of Texas Rio Grande Valley (UTRGV), where he teaches and conducts research in the School of Mathematical and Statistical Sciences. He holds a Ph.D. in Applied Mathematics from the University of Washington (2006), an M.Sc. from the same institution (2000), and a B.Sc. in Applied Mathematics from the California Institute of Technology (1997). His research spans fluid turbulence, theoretical physics, mathematics education, and medical research, particularly focusing on early outpatient treatment protocols for infectious diseases like COVID-19. He has authored over 2,800 pages of open-access lecture notes and contributed to groundbreaking work on multidrug therapies. Education: B.Sc., Applied Mathematics, California Institute of Technology, 1997 M.Sc., Applied Mathematics, University of Washington, 2000 Ph.D., Applied Mathematics, University of Washington, 2006 Dr. Gkioulekas' research interests include hydrodynamic and geophysical turbulence, curriculum innovations in mathematics education, and medical research on early treatment protocols for viral infections. His work on statistical analysis of multidrug therapies for COVID-19 has been published in peer-reviewed journals and highlighted in public health discussions. He is also dedicated to advancing open educational resources and has written extensive lecture materials for undergraduate and graduate mathematics courses. His recent publications emphasize causal inference in medical treatment efficacy and mental health impacts of the pandemic on nursing students. His contributions to the scientific community include collaborations with clinicians and statisticians to improve treatment outcomes and educational practices.

Kuang-Yao Leeمشاهده پروفایل

دانشیار

• Statistical machine learning
• Statistical genomics
• Graphical modeling and causality learning
• +۲ مورد دیگر

Kuang-Yao Lee is an Associate Professor in the Department of Statistical Science at the Fox School of Business and Management, Temple University, holding a tenure-track appointment. Previously, he served as an associate research scientist at Yale University’s Center for Statistical Genomics and Proteomics, where he developed statistical methods for high-dimensional data and conducted collaborative research. Dr. Lee's educational background includes: PhD in Statistics from The Pennsylvania State University Master of Science in Mathematics from National Taiwan University Bachelor of Science in Mathematics from National Taiwan University His research centers on statistical genomics and machine learning with high dimensionality as a unifying theme. He develops advanced methodologies in graphical modeling, causality learning, high-dimension inference, and semi/non-parametric statistics. His work bridges theoretical statistics with practical applications in bioinformatics, image analysis, and functional data, often through collaborations with biologists and computer scientists on genome-wide association studies, gene regulatory networks, and pathway analysis. Dr. Lee's publication trajectory reveals a concentrated focus on functional data analysis and graphical models over the past five years. His contributions include foundational work on functional directed acyclic graphs, nonparametric functional graphical modeling, and sufficient dimension reduction in metric spaces. These innovations demonstrate consistent integration of causal inference frameworks with high-dimensional genomic applications, reflecting his dual expertise in statistical theory and biological data science.

William Pennyمشاهده پروفایل

استاد

• Brain Imaging
• Bayesian Inference
• Dynamical Systems
• +۳ مورد دیگر

William Penny is a Professor in Psychology at the University of East Anglia (UEA), School of Psychology. He joined UEA in 2017 after serving as a Professor at University College London (UCL). His research focuses on brain imaging methodologies, particularly analyzing fMRI and EEG data using Bayesian Inference, Dynamical Systems, and Probabilistic Machine Learning. He holds a BEng in Electrical Engineering (Nottingham University, 1988), MSc in Communication Engineering (Imperial College, 1989), and PhD in Artificial Neural Networks (Brunel University, 1993). He has led projects including a Wellcome Trust-funded initiative on brain imaging (2019–2024) and a study on brain function during perimenopause with Braincare Limited (2025–2026). His work spans collaborations in neuroimaging analysis, cognitive modeling, and translational neuroscience. His research outputs emphasize Bayesian methods, dynamic causal models, and computational neuroscience, with 84+ peer-reviewed articles. Key areas include cortical degeneration in Huntington’s disease, diet-cognition links, and neural mechanisms of decision-making. His lab, the UEA Brain Imaging Centre (UBIC), advances multimodal neuroimaging techniques. Professional affiliations include the Wellcome Trust Centre for Human Neuroimaging at UCL and international collaborations. No specific awards are listed, but his contributions to neuroimaging methodology are widely recognized.

Matteo Bonviniمشاهده پروفایل

استادیار

• Causal Inference
• Nonparametric Methods
• Semiparametric Efficiency Theory
• +۳ مورد دیگر

Matteo Bonvini is an Assistant Professor in the Department of Statistics at Rutgers University. He holds an office in Hill Center 401, located at 110 Frelinghuysen Road, Piscataway, NJ 08854. His research focuses on causal inference, nonparametric methods, and semiparametric efficiency theory, with applications to healthcare and epidemiology. Dr. Bonvini earned his Ph.D. in Statistics from Carnegie Mellon University. His work bridges theoretical advancements in causal modeling with practical applications in observational studies and clinical research. Recent research highlights include developing sensitivity analysis frameworks for unmeasured confounding and optimizing subgroup identification in treatment effect estimation. His publications span topics such as instrumental variable methods, incremental causal effects, and causal inference in pandemic studies. He has contributed to healthcare analytics through studies on surgical outcomes and ICU predictive modeling. His methodological work emphasizes statistical rigor while addressing real-world complexities in observational data. Bonvini's affiliations include the School of Arts and Sciences (implied via Rutgers' structure) and collaborations with healthcare institutions. He maintains an active research program with grants likely supporting his work in causal inference methodologies, though specific grants are not detailed here.

Mark Greenwoodمشاهده پروفایل

استاد

• Statistics
• Data Analysis
• Biostatistics
• +۷ مورد دیگر

Dr. Mark Greenwood is a Professor in the Department of Mathematical Sciences at Montana State University, affiliated with the College of Letters & Science. He holds a B.A. in Mathematics/Statistics from Luther College (1996), and both an M.S. and Ph.D. in Statistics from the University of Wyoming (2000, 2004). His research focuses on advanced statistical methodologies including high-dimensional data analysis, longitudinal modeling, functional data analysis, and measurement error correction. Applications span environmental science, healthcare, and education. Notable contributions include developing statistical frameworks for analyzing BMI trajectories in aging populations and creating molecular diagnostic models for Multiple Sclerosis severity. Teaching encompasses advanced statistical methods courses such as Methods of Data Analysis I/II, Linear Models, and Multivariate Statistics. Awards include the 2020 Excellence in Open Education and the Dr. William A. Stannard Award for Graduate Instruction. Key grants include biomarker research funded by Washington University-St Louis and collaborative CTR-IN initiatives. Professional service includes roles as a reviewer for Plos ONE, Journal of Primary Prevention, and advisory work for BYU-Idaho's Statistics program.