جست‌وجوی استاد و پژوهشگر

Charisios Grivasمشاهده پروفایل

استادیار

• Econometrics
• Statistical Methods
• Non-parametric Econometrics
• +۷ مورد دیگر

Charisios Grivas is an Assistant Professor in the Department of Mathematical Sciences at the Faculty of Engineering and Science, Aalborg University, Denmark. His research lies at the intersection of econometrics, statistics, and environmental modeling, with a strong emphasis on methodological rigor and robust inference. His primary research interests include Econometrics , Statistical Methods , Non-parametric Econometrics , Resampling , Robust Estimation , and Measurement Error Models . He develops and applies advanced statistical techniques to address challenges in linear and time-varying models, particularly in the presence of data imperfections such as measurement errors. The recent articles demonstrate a consistent focus on improving inference in econometric models—especially through automated bandwidth selection, testing for nonlinear dependence, and robust estimation under measurement error. These works span applications in both theoretical econometrics and environmental research, notably in estimating the carbon dioxide airborne fraction. Scientific Awards: No scientific awards mentioned in the provided text. Charisios Grivas actively collaborates with researchers such as Z. Psaradakis and J.E. Vera-Valdés. While no formal advisees are listed, his publications and ongoing research output suggest active supervision and mentorship. There is no mention of specific grants, but his work on measurement error and environmental statistics may be supported by external funding. His research contributes to methodological advancements with practical implications in climate science and economics.

Ömer Gürdoğanمشاهده پروفایل

پژوهشگر ارشد

• Quantum Field Theory (QFT)
• Scattering Amplitudes
• Multi-loop Calculations
• +۳ مورد دیگر

Ömer Gürdoğan is a Senior Research Fellow at the University of Southampton, holding the Royal Society University Research Fellowship. He is affiliated with the Southampton High Energy Physics (SHEP) group and the Southampton Theory Astrophysics and Gravity (STAG) Research Centre. His research focuses on scattering amplitudes in Quantum Field Theory (QFT), multi-loop calculations using bootstrap techniques, and applications of cluster algebras to QFT. He also explores integrable QFTs, notably the Fishnet Model. Ömer's academic background includes a PhD from Queen Mary University of London and postdoctoral roles at the University of Oxford, École Normale Supérieure (Paris), and the University of Southampton. He previously held the UKRI Stephen Hawking Fellowship. His work bridges theoretical physics and advanced mathematics, with contributions to topics like tropical geometry and integrability. Research Projects: New Perspectives In Quantum Field Theory (Royal Society-funded) Cluster bootstrap beyond scattering amplitudes N=4 super Yang-Mills New Frontiers In Particle Physics, Cosmology And Gravity (Science & Technology Facilities Council) His publications span prestigious journals like Physical Review Letters and Journal of High Energy Physics , addressing themes such as form factors, bootstrap techniques, and algebraic structures in amplitudes. He actively supervises PhD students in high-energy physics and theoretical mathematics. Ömer has been honored with fellowships from the Royal Society and UKRI, reflecting his contributions to advancing fundamental physics through innovative mathematical frameworks.

Omer Bobrowskiمشاهده پروفایل

استاد

• Topological Data Analysis
• Stochastic Topology
• Applied Topology
• +۴ مورد دیگر

Omer Bobrowski is a Professor in Mathematical Data Science at Queen Mary University of London, affiliated with the School of Mathematical Sciences. His research focuses on stochastic topology, topological data analysis (TDA), and their applications in signal processing and natural language processing. He explores theoretical aspects like phase transitions in stochastic topology and noise distribution in TDA tools, while developing statistical methods for practical applications. His work has been supported by grants from the EPSRC (£396,018, 2024–2027) and the Leverhulme Trust (£330,778, 2024–2027). Collaborators include Primoz Skraba and others in the Centre for Probability, Statistics, and Data Science. Research Interests include: Random Topology, Applied Topology, Stochastic Geometry, and Probability Theory. His lab includes Research Staff Dr. Shu Kanazawa, Dr. Uzu Lim, and Dr. Duncan Parker. Bobrowski’s publications span foundational TDA theory and applied methodologies across diverse datasets.

Ibrahim Joudahمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Statistics
• +۴ مورد دیگر

Ibrahim Joudah is an Academic Casual in the School of Mathematical and Physical Sciences at Macquarie University, where he is pursuing his Doctor of Philosophy. His work bridges advanced statistical methods with practical educational applications. His research interests focus on artificial intelligence applications in education, with particular emphasis on predictive modeling of student outcomes using advanced machine learning techniques. His work combines traditional statistical methods with cutting-edge AI approaches to address challenges in educational data analysis. Key areas include generative adversarial networks for synthetic educational data generation, feature selection in high-dimensional datasets, and robust statistical estimation methods. His publication record shows a clear trajectory toward applying sophisticated AI methods to educational contexts, with recent work focusing on enhancing prediction accuracy for student learning outcomes. His research demonstrates strong technical expertise in both theoretical statistics and practical AI implementation, particularly using Python and related computational tools. Joudah has been active in academic dissemination, including an invited talk at the 26th International Conference on Computational Statistics in August 2024. His publications have garnered attention across academic platforms, with significant readership on Mendeley and mentions on professional social networks.

• Probability Theory
• Statistics
• Applied Probability
• +۵ مورد دیگر

Robin Milne is an Adjunct Senior Research Fellow in the Department of Mathematics and Statistics at the School of Physics, Maths and Computing, University of Western Australia, specializing in statistical modeling and stochastic processes. His core research domains include: Probability Theory Statistics Applied Probability Applied Statistics Stochastic Processes Point Processes His methodology integrates advanced statistical techniques with real-world applications across geology, biophysics, and public health, emphasizing practical problem-solving through probabilistic frameworks. Recent publications reveal strong interdisciplinary trends: mineral prospectivity analysis using spatial statistics and machine learning (2021-2025), biophysical modeling of ion channels via hidden Markov models (2019), and methodological innovations in Monte Carlo hypothesis testing (2017), alongside public health surveys on breast cancer chemoprevention (2020). Dr. Milne has supervised three research students and led the ARC-funded project "Ion Channels Interactions: Stochastic Modelling and Inference" (1998-2000), demonstrating sustained expertise in statistical applications to complex scientific systems.

Ida Zadehمشاهده پروفایل

مدرس

• String Theory
• Conformal Field Theory (CFT)
• High Energy Physics
• +۳ مورد دیگر

Dr. Ida Zadeh is a Lecturer in String Theory at the University of Southampton. Her research focuses on fundamental aspects of string theory and quantum gravity, including conformal field theories, AdS/CFT correspondence, and mathematical structures in string compactifications. She actively supervises PhD students and has published extensively in high-impact journals. Her work explores topics such as BPS states in holographic systems, moduli spaces of conformal field theories, and stability analyses of string vacua. Notable contributions include studies of heterotic strings on orbifolds, Mathieu moonshine phenomena linked to K3 surfaces, and non-perturbative effects in supersymmetric backgrounds. Zadeh collaborates internationally and has contributed to advancing our understanding of string dualities, quantum gravity, and the interplay between geometry and physics in high-energy systems.

Markus Pantsarمشاهده پروفایل

استاد مدعو

• Philosophy of Mathematics
• Cognitive Science
• Philosophy of Science
• +۳ مورد دیگر

Markus Pantsar is an Adjunct Professor (Docent) in Theoretical Philosophy at the University of Helsinki's Department of Philosophy, History and Art Studies within the Faculty of Arts. His research focuses on the philosophy of mathematics, cognitive science, and the foundations of logic. Central themes include the cognitive development of mathematical concepts, the role of notation systems in arithmetic understanding, and the philosophical implications of artificial intelligence in mathematical theorem proving. His work bridges interdisciplinary approaches, integrating insights from psychology, computer science, and formal logic. Notable projects include Digital Distraction: Philosophical Perspectives (2024–2026) and Dependence and Independence in Logic (2015–2019). He has delivered invited talks at institutions like VU University Amsterdam and contributed to international conferences. Pantsar's research emphasizes enculturation as a key mechanism for mathematical knowledge acquisition, arguing that cultural practices and symbolic systems shape arithmetical cognition. He critiques radical enactivist accounts and explores how computational complexity informs the cognitive foundations of mathematics. Key Collaborations: Collaborators include Kai Kärki (digital distraction studies), Gabriel Sandu (logic), and Behnam Assadian (cardinality research). Grants: Active in securing research funding, including the ongoing Digital Distraction project (€1.2M). Labs/Teams: Participates in interdisciplinary groups focused on mathematical cognition and AI ethics at the University of Helsinki.

Neil J. Hatfieldمشاهده پروفایل

عضو هیئت علمی

• Statistics Education
• Probability Education
• Stochastic Processes
• +۴ مورد دیگر

Neil J. Hatfield is an Assistant Research Professor in the Department of Statistics at Pennsylvania State University's Eberly College of Science. He holds a PhD in Mathematics Education from Arizona State University (2019), an MS in Mathematics Education from Northwest Missouri State University (2011), and a BS in Mathematics from Doane University (2009). His research focuses on Stochastics Education, emphasizing student cognition in statistics and probability, the impact of web applets on learning, and STEM diversity initiatives. He currently coordinates the Statistics and Data Science Education Working Group for the Research in Undergraduate Mathematics Education SIG of the MAA. Education: PhD in Mathematics Education (ASU 2019), MS in Education (NWMSU 2011), BS in Mathematics (Doane 2009) Affiliations: Penn State (current), University of Northern Colorado (Visiting Assistant Professor), Arizona State University (Instructor) His research explores how students develop conceptual understandings of stochastic processes, distributions, and probability. He develops educational technologies like the StatsTools JMP add-in and web-based applets (e.g., Fish Sampling, Four Rooms) to enhance statistical learning. Hatfield has received multiple teaching awards, including the Robert G. Maule Excellence in Teaching Mathematics Award (2017) and the Graduate Teaching Excellence Award (2016-2017). Professional contributions include serving on the Board of the Bootstrap Organization and co-authoring influential papers on covariational reasoning across international teacher populations. His work bridges qualitative research methods with curriculum development, emphasizing interdisciplinary approaches to stochastic literacy.

Ciprian Crainiceanuمشاهده پروفایل

استاد

• Biostatistics
• Functional Data Analysis
• Wearable and Implantable Technology
• +۱۵ مورد دیگر

Ciprian Crainiceanu is a Professor in the Department of Biostatistics at the Bloomberg School of Public Health, Johns Hopkins University. His research spans biostatistical methodology and applications in public health, with a focus on high-dimensional data from wearable devices and medical imaging. Education: PhD, Cornell University, 2003 MS, University of Bucharest, 1998 His research interests include functional data analysis, measurement error, longitudinal modeling, Bayesian inference, and nonparametric statistics, with applications in sleep, aging, multiple sclerosis, Alzheimer’s disease, and cancer. He develops statistical tools tailored to complex data from accelerometers, neuroimaging (MRI, CT, SPECT), and surgical monitoring. His recent work involves dynamic prediction models, step-counting algorithms for NHANES and ARIC data, and methods for high-dimensional functional and imaging data. The most recent publications highlight advancements in wearable data analysis, medical imaging platforms like Neuroconductor, and novel resampling methods such as the upstrap. His work integrates statistical theory, software development, and interdisciplinary collaboration. Scientific Awards: Fellow of the American Statistical Association (ASA) Chair, Statistics in Imaging Section of ASA (two terms) Chair, Biostatistics Methods and Research Design (BMRD) NIH review section Crainiceanu is actively involved in mentoring, teaching, and collaborative research. He co-founded the SMART (Statistical Methods and Applications for Research in Technology) research group and Neuroconductor, fostering interdisciplinary innovation. His work emphasizes scalable, software-backed methods and close collaboration with domain scientists. He has led methodological developments in variance components testing, functional regression, population value decomposition, and dynamic prediction, applied to real-world health challenges. Labs and Research Groups: Co-founder, SMART (Statistical Methods and Applications for Research in Technology) Co-founder, Neuroconductor (open-source platform for medical imaging in R) Wearable and Implantable Technology (WIT) group MAGIC (Methods and Applications Group for Imaging in the Clinic)

Wolfgang Polonikمشاهده پروفایل

استاد

• Mathematical Statistics
• Statistical Learning Theory
• Nonparametric Statistics
• +۶ مورد دیگر

Wolfgang Polonik is a Professor in the Department of Statistics at the University of California, Davis, where he conducts research at the intersection of mathematical statistics, nonparametric methods, and topological data analysis. His work combines deep theoretical foundations with applications in high-dimensional and complex data structures. His research interests include Mathematical Statistics, Statistical Learning Theory, Nonparametric Statistics, Shape Constraints, Modality, Nonstationary Time Series, Empirical Process Theory, Topological Data Analysis, and Random Networks . He applies these to problems involving geometric and topological inference, manifold learning, and anomaly detection. The recent publications highlight a strong focus on persistent homology, bootstrap methods for topological features, nonparametric regression on manifolds, and multiscale geometric analysis . His work often bridges theoretical statistics with computational topology and machine learning, particularly through the use of persistence diagrams, level sets, and filament estimation. Collaborations with researchers like W. Qiao and G. Chandler are frequent. Dr. Polonik has served as an Associate Editor for several top-tier journals, including: Journal of the Royal Statistical Society – Series B (2012–2015) Journal of Multivariate Analysis (since 2003) Journal of Statistical Planning and Inference (2004–2012) Annals of Statistics (2007–2012) He is a member of leading professional societies such as the Institute of Mathematical Statistics, American Statistical Association, Bernoulli Society, and International Statistical Institute . While no formal advising or grant information is listed, his editorial roles and publication record reflect significant scholarly impact. He is affiliated with the College of Letters and Science at UC Davis.

David Ruppertمشاهده پروفایل

استاد

• Functional data analysis
• Nonparametric regression
• Statistical machine learning
• +۷ مورد دیگر

David Ruppert is the Andrew Schultz Jr. Professor of Engineering at Cornell University's School of Operations Research and Information Engineering, and Professor of Statistics and Data Science. He holds dual appointments and has been a faculty member since 1987. His education includes a B.A. in Mathematics from Cornell University (1970), M.A. in Mathematics from the University of Vermont (1973), and Ph.D. in Statistics and Probability from Michigan State University (1977). Research Interests: His work spans functional data analysis, astrostatistics, neuroimaging (fMRI/ICA), environmental statistics, and semiparametric regression. He has pioneered methods in measurement error models, splines, and Bayesian statistics. His research has been continuously funded by NSF, NIH, and EPA since 1978. Publications: Over 130 refereed articles and 5 books, including foundational texts like Measurement Error in Nonlinear Models and Statistics and Data Analysis for Financial Engineering . Recent work includes astrostatistical modeling of galaxy spectral energy distributions and neuroimaging analysis. Awards/Honors: Wilcoxon Prize (1986), ASA/IMS Fellowships, Highly Cited Researcher (ISI), and Distinguished Alumni Award (2014). Teaching: Courses include Financial Engineering, Bayesian Statistics, and Functional Data Analysis. He co-developed four graduate/undergraduate courses at Cornell. Service: Editor of Journal of the American Statistical Association , Director of the MPS Program in Data Science and Statistics (DSS). Impact: 29 PhD students trained, many now leading researchers in academia and industry.

Joyce A. Chewمشاهده پروفایل

استادیار

• Mathematics of Data Science
• Geometric Deep Learning
• Low-Rank Matrix/Tensor Approximation
• +۴ مورد دیگر

Joyce A. Chew is an Assistant Professor of Mathematics & Statistics at Calvin University. She holds a PhD in Mathematics from UCLA (2025) and dual bachelor’s degrees in Mathematics and Chemistry from Calvin University (2020). Her research focuses on geometric deep learning, low-rank matrix/tensor approximation, and mitigating bias in machine learning. She has contributed to manifold-based data analysis, scattering transforms, and non-negative matrix factorization techniques. Her work bridges mathematics, data science, and applications in single-cell genomics and chemometrics. Chew has received prestigious awards including the NSF Graduate Research Fellowship and the UCLA Raymond Redheffer Prize. She actively mentors undergraduate researchers and participates in initiatives like UCLA’s Collaborative Undergraduate Research Experience. Education : PhD in Mathematics, UCLA, 2025 (Thesis: Topics in Geometric Deep Learning and Learning on Manifolds) C.Phil. & MA in Mathematics, UCLA, 2023 & 2022 B.S. (Honors) Mathematics & B.A. Chemistry, Calvin University, 2020 Research Interests : Her work emphasizes geometric structures in data science, including manifold learning, neural network architectures for non-Euclidean data, and algorithmic fairness. She develops methods for analyzing high-dimensional datasets (e.g., point clouds) using diffusion maps and scattering transforms. Recent projects address bias in word embeddings and robust uncertainty quantification in spectroscopic analysis. Awards : Pacific Journal of Mathematics Dissertation Award (2025) SIAM Student Travel Award (2024) UCLA Raymond Redheffer Prize (2022) NSF Graduate Research Fellowship (2020) Mentoring & Grants : Chew leads UCLA/Los Angeles Pierce College Collaborative Undergraduate Research Experience (2023-2024) and directs the UCLA CAM REU (2021-2022). She has advised projects in geometric deep learning and chemometric modeling. Labs/Teams : Collaborates with groups at UCLA (Deanna Needell’s lab), Stanford (Smita Krishnaswamy), and institutions globally on manifold-based data science initiatives.

• Artificial Intelligence
• Program Synthesis
• Cognitive Science
• +۵ مورد دیگر

Kevin Ellis is an Assistant Professor in the Department of Computer Science at Cornell University, affiliated with the College of Engineering. His research focuses on program synthesis, cognitive science, and neurosymbolic AI. He teaches courses such as Foundations of AI: Reasoning and Decision-Making (CS3700) and Program Synthesis (CS6172). Ellis has advised multiple PhD and master's students, including Hao Tang, Simon Alford, and Wen-Ding Li (now at Mistral). His work has been recognized with the Best Paper award at the ARCPrize 2025. Ellis's research spans applications in robotics, quantum computing, and human-like learning, often leveraging Bayesian methods and large language models. He leads teams exploring the intersection of neural networks and symbolic systems through projects like WorldCoder and DreamCoder. Education: PhD in Computer Science from MIT (advisor: Joshua B. Tenenbaum). Research interests include neurosymbolic integration, program induction, and cognitive modeling. His recent work emphasizes scalable synthesis techniques, probabilistic reasoning, and human-AI collaboration. Ellis's lab develops tools for automated program generation and interpretable AI systems. He actively contributes to open-source projects (e.g., GitHub repositories) and collaborates internationally on NSF-funded initiatives like the CAREER award for Symbolic Learning with LLMs.

Claudia de Rhamمشاهده پروفایل

استاد

• Quantum Field Theory
• Gravity
• Gravitational Waves
• +۴ مورد دیگر

Claudia de Rham is a Professor of Theoretical Physics at Imperial College London, affiliated with the Department of Physics within the Faculty of Natural Sciences. Her research focuses on quantum gravity, cosmology, gravitational waves, and particle physics. She holds additional roles such as Director of the Abdus Salam Centre for Theoretical Physics and chairs the PPGPtheory STFC panel. Dr. de Rham earned her PhD from the University of Cambridge, with MSc degrees from École Polytechnique and École Polytechnique Fédérale de Lausanne. Her work bridges quantum field theory, cosmology, and gravity, particularly exploring modified gravity theories and the nature of dark energy. Research Interests: She investigates gravitational theories beyond Einstein, including massive gravity, galileons, and the implications of gravitational wave physics. Her work addresses fundamental questions like the acceleration of the universe and the behavior of spacetime at extreme scales. Recent projects include studying decoherence in expanding universes and causality constraints in effective field theories. Publications & Media: Over 120 peer-reviewed articles, including influential works on effective field theories and gravitational wave propagation. She has given over 25 public talks annually, including at the Royal Institution and World Science Festival. Her popular science book The Beauty of Falling details her journey in theoretical physics. Awards & Grants: Recipient of the Blavatnik Award, Simons Investigator, and Royal Society Wolfson Merit Award. Her research is supported by ERC grants and collaborations with institutions like CERN and the Perimeter Institute. Labs & Teams: Leads theoretical physics groups at Imperial and Case Western Reserve University, fostering interdisciplinary research in cosmology and fundamental physics.

VG Vinod V. Vydiswaranمشاهده پروفایل

دانشیار

• Natural Language Processing
• Health Informatics
• Clinical Data Analysis
• +۳ مورد دیگر

VG Vinod V. Vydiswaran is an Associate Professor at the University of Michigan, holding dual appointments in the Medical School (Department of Learning Health Sciences) and the School of Information (Department of Information). He is affiliated with multiple centers, including the Center for Computational Medicine and Bioinformatics, Precision Health Initiative, and the e-Health and Artificial Intelligence Initiative. His research focuses on applying Natural Language Processing (NLP) to healthcare challenges, emphasizing clinical data analysis, health equity, and public health analytics. Education: Ph.D. and MS in relevant fields (not explicitly stated in text). Research interests include NLP-driven solutions for clinical notes analysis, caregiver availability assessment in Alzheimer’s patients, adverse drug event detection in social media, and equitable health informatics frameworks. He co-authored the 'Equitable Research PRAXIS' framework to promote inclusive methodologies in healthcare research. Recent work highlights NLP applications in identifying extraintestinal manifestations of inflammatory bowel disease, validating algorithms for operative notes in hip arthroplasty, and extracting medication mentions from social media using DistilBERT. He actively collaborates on grants, including a $5M NIH grant to train Learning Health System Scientists. Scientific Awards: None explicitly listed in the provided text.