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Tom BrittonView profile

Professor

• Epidemic modeling
• Biostatistics
• Network analysis
• +5 more

Tom Britton is a Professor in Mathematical Statistics (Holder of the Cramér chair) at the Department of Mathematics, Stockholm University. He leads the SU-Infection spread modeling research group and serves as chairman of the Stockholm Mathematics Center (SMC). His work primarily focuses on developing and analyzing mathematical/statistical models for infection spread and applying statistical methods to infection spread data. His research interests span epidemic modeling, biostatistics, network analysis, and mathematical statistics with applications toward genetics and molecular biology including phylogenetics. Britton's work encompasses applications of biostochastics and biostatistics, discrete random structures, and finance and insurance. He has made significant contributions to understanding disease transmission dynamics, particularly during the COVID-19 pandemic. Britton's recent publications demonstrate a strong emphasis on practical applications of mathematical models to public health challenges. His work examines contact tracing strategies, vaccine impact analysis, immunity waning dynamics, and optimal intervention strategies. There is a clear pattern of translating theoretical mathematical models into actionable public health insights, with particular focus on the interplay between population heterogeneity, immunity levels, and intervention effectiveness. As an academic leader, Britton serves as academic editor for PLoS Comp Biol and associate editor for Journal of Mathematical Biology and JRSS A (Statistics in Society). He is also a board member of the European Society for Mathematical and Theoretical Biology (ESMTB). Britton actively mentors the next generation of researchers, currently supervising PhD student Fanny Bergström and Post Doc Martina Favero. Throughout his career, he has successfully guided 13 PhD students and multiple postdoctoral researchers. His current research is supported by significant grants from the Swedish Research Council (3.6 MSek, PI 2021-2024) and the KA Wallenberg Foundation (0.7 MSek, PI 2023-2025). The SU-Infection spread modeling research group, which Britton leads, develops and analyzes mathematical/statistical models for the spread of infection and applies statistical methods to adapt these models to infection spread data. This work has been particularly relevant during recent pandemics and continues to inform public health decision-making.

Kevin HeView profile

Associate Professor

• Survival Analysis
• Healthcare Provider Profiling
• Machine Learning
• +7 more

Kevin He is an Associate Professor in the Department of Biostatistics at the University of Michigan School of Public Health. He serves as Associate Director of the Kidney Epidemiology and Cost Center (KECC) and leads statistical innovations in survival analysis, healthcare provider profiling, and data integration for biomedical applications. PhD in Biostatistics (University of Michigan, 2012) MS in Biostatistics (University of Michigan, 2008) BS in Statistics (Queen’s University, 2006) MS in Epidemiology (Queen’s University, 2004) BM in Clinical Medicine (Dalian Medical University, 2002) His research focuses on survival analysis for large-scale datasets, machine learning for healthcare provider profiling, and statistical genetics in organ transplantation and chronic disease. He develops data integration frameworks for polygenic risk scores and statistical optimization algorithms for time-varying effects in national registries. The 15 most recent articles highlight his work in survival modeling with time-varying coefficients, federated learning for privacy-preserving data integration, and genomic applications in inflammatory diseases. His methodological contributions span Kronecker product algorithms , proximal optimization , and penalized partial likelihood . He mentors a team including software developers and graduate researchers working on deep learning , frailty models , and distributed computing . His lab maintains the surtvep R package for scalable survival analysis.

Ramin MoghaddassView profile

Associate Professor

• Predictive Analytics
• Machine Learning
• Industrial Engineering
• +4 more

Ramin Moghaddass is an Associate Professor in the Department of Industrial Engineering at the University of Miami's College of Engineering. He serves as Director of both the Industrial Research and Assessment Center and the Building Training, Research, and Assessment Center. His work bridges machine learning with industrial engineering to solve complex system monitoring and maintenance challenges. University of Miami, College of Engineering Director, Industrial Research and Assessment Center Director, Building Training, Research, and Assessment Center His research focuses on: Deep state-space modeling for dynamic systems Graph neural networks for smart grid and network anomaly detection Thermal-RGB sensor fusion for manufacturing plant efficiency Image processing for vegetation risk analysis in power networks Bayesian filtering techniques with stochastic neural networks Recent publications highlight trends in sensor-driven system modeling (2025), thermal-RGB fusion for HVAC optimization (2025), anomaly detection in smart grids (2025), and adaptive inspection protocols for large-scale networks (2024). His work combines recurrent neural networks with dynamic Bayesian layers for predictive analytics while exploring graph topology integration. Contact: rxm991@miami.edu | (305) 284-9505

Steven CulpepperView profile

Professor

• Psychometrics
• Latent Class Models
• Statistical Computing
• +3 more

Steven Culpepper is a Professor at the University of Illinois at Urbana-Champaign with joint appointments in the Department of Educational Psychology (College of Education), Department of Statistics, Department of Psychology, and Beckman Institute for Advanced Science and Technology. He holds a PhD in Educational Psychology from the University of Minnesota (2006) and BS in Economics from Bowling Green State University (2001). His research program focuses on developing advanced psychometric models and statistical methods for educational and psychological measurement. Primary research areas include: Latent class and hidden Markov models for cognitive diagnosis Large-scale testing and assessment methodologies Bayesian inference for restricted latent class models Statistical computing applications for psychometrics Longitudinal modeling of learning trajectories His recent publications demonstrate consistent focus on improving parameter estimation for diagnostic classification models, with methodological innovations in Bayesian computation, model identifiability, and handling of polytomous response data. Research consistently addresses fundamental challenges in educational measurement through rigorous statistical frameworks.

Tatjana PavlenkoView profile

Professor

• high-dimensional statistical inference
• statistical machine learning
• sparse signal detection
• +4 more

Tatjana Pavlenko is a Professor in Statistics at Uppsala University, affiliated with the Department of Statistics. Her research bridges mathematical statistics, probability theory, and computational methods, focusing on high-dimensional data analysis in biomedical and machine learning contexts. Key research areas include: High-dimensional statistical inference and Bayesian graph structure learning Sparse signal detection and adaptive thresholding methods Statistical machine learning with applications to biomedical datasets Her recent publications demonstrate expertise in: Bayesian model averaging and junction tree sampling Asymptotic theory for high-dimensional classifiers Testing independence and covariance structures L2-type statistics and empirical process theory She supervises PhD students working on: High-dimensional causal inference in media Bayesian graphical models Sparse classification algorithms Currently active in Uppsala University's AI4Research initiative, Pavlenko develops adaptive data-driven procedures for statistical learning problems with complex sparsity patterns.

• Gravitational Waves
• Pulsar Timing Arrays
• Astrophysics
• +5 more

Ann Marie Schmiedekamp is a Professor in the Division of Engineering and Science at Penn State University Abington campus. With an h-index of 23 and 2951 citations, she is a prominent researcher in gravitational-wave astronomy and pulsar timing arrays. Her work primarily focuses on using pulsar timing to detect gravitational waves in the nanohertz frequency range. Dr. Schmiedekamp's research interests center around gravitational-wave detection through pulsar timing arrays, particularly with the North American Nanohertz Observatory for Gravitational Waves (NANOGrav). Her expertise spans gravitational-wave background analysis, pulsar timing techniques, statistical methods for signal detection, and the astrophysical implications of gravitational-wave observations. She has made significant contributions to understanding supermassive black hole binaries and cosmic string signatures through gravitational-wave data. Her recent publications (2023-2025) demonstrate a strong focus on the NANOGrav 15-year data set, with multiple papers analyzing different aspects of the gravitational-wave background detection. These works cover spectral index analysis, pulsar selection methods, data validation techniques, and the properties of the detected signal. The research shows a progression from detection to detailed characterization of the gravitational-wave background. 23 h-index with 2951 citations Active participant in NANOGrav collaboration Significant contributions to gravitational-wave background detection Dr. Schmiedekamp serves as Co-Principal Investigator on two major National Science Foundation projects: the NANOGrav Student Teams of Astrophysics Researchers Undergraduate Pathways (STARS-UP) focusing on community college transitions in astrophysics, and the Sustainable Summer Bridges project for underrepresented engineering students. These projects highlight her commitment to education and diversity in STEM fields. She is actively involved in the NANOGrav collaboration, contributing to the infrastructure for undergraduate research in gravitational-wave astronomy. Her work bridges cutting-edge research with educational initiatives, particularly focusing on creating pathways for students from two-year to four-year institutions.

Bertalan PolnerView profile

Assistant Professor

• Cognitive Psychology
• Schizotypy
• Sleep and Psychosis
• +4 more

Bertalan Polner is an Assistant Professor at the Institute of Psychology , Eötvös Loránd University, affiliated with the Department of Clinical Psychology and Addiction . He serves as a Member of the Research Ethics Committee and maintains an active research profile at Radboud University’s Donders Centre for Cognition as a PostDoc researcher. Academic Rank: Assistant Professor Current Institutions: Eötvös Loránd University (ELTE), Radboud University Research Focus: Schizotypy-psycho sis interactions, computational psychiatry, sleep-cognition links, memory mechanisms, and emotion regulation. Dr. Polner’s research explores how positive schizotypy interacts with sleep disturbances and visual perception anomalies , using network analysis to map relationships between personality traits and mental health outcomes. His work often integrates computational modeling (e.g., drift diffusion models, predictive coding) to understand cognitive deficits in psychotic disorders. Recent publications highlight his contributions to: Mapping schizotypy’s role in stress-reactivity (2025) Investigating REM sleep’s impact on mind-wandering (2023) Analyzing reward processing in borderline personality disorder through schema modes (2023) He also develops educational tools like the introduction_to_R and Structural Equation Modeling repositories on GitHub, supporting psychological research methodology.

CLINTView profile

Research Fellow

• Neuroscience
• Neurology
• Psychiatry
• +3 more

CLINT leads the Lab for Clinical & Integrative Neuroscience (Lab CLINT) at Trinity College Dublin , focusing on brain disorders through multimodal imaging and neuromodulation. The lab investigates predictive coding theory in phantom perceptions like tinnitus, Parkinson's, and chronic pain, while developing treatments through peripheral nerve stimulation. Research Pillars : Predictive coding, thalamocortical dysrhythmia, locus coeruleus noradrenergic pathways Key Disorders : Tinnitus, Alzheimer's, chronic pain, depression Scientific Contributions include: Developing TIDE consortium for tinnitus biomarkers Implementing BRAI3N clinic for neuromodulation therapies Creating Tinnitus Éire community for peer support Recent publications analyze: Deep learning applications for tinnitus prognosis HD-tACS effects on attention networks Bayesian models of chronic pain comorbidities CLINT's work bridges laboratory research with clinical translation, emphasizing patient-centered approaches and interdisciplinary collaborations across Europe and North America.

Julius GoesView profile

Researcher

• Bayesian Statistics
• Actuarial Science
• Epidemiological Modeling
• +3 more

Julius Goes, M.Sc., is a Researcher at the Professorship for Mathematics in Economics within the Faculty of Social and Economic Sciences at Otto-Friedrich-University Bamberg. Since October 2020, he has contributed to academic research in statistical modeling and demographic forecasting. Education 2012: Abitur, Albert Einstein Gymnasium Böblingen 2017: Bachelor of Science in International Business Administration, University of Bamberg 2020: Master of Science in Survey Statistics, University of Bamberg His research focuses on Bayesian mortality modeling, particularly in small-area forecasting and pandemic-related demographic impacts. He has presented his work at conferences including Statistische Woche 2022, Longevity 18, and SurvConf 2025. Recent publications include methodological advancements in spatiotemporal Bayesian models for mortality rate prediction, appearing in Demography and the Journal of the Royal Statistical Society Series C: Applied Statistics . His work bridges statistical innovation with real-world demographic challenges. Contact information: Room F21/00.82, Feldkirchenstraße 21, 96052 Bamberg. Phone: 0951/863 2632. Consultation hours: Wednesdays 10:00-11:30 AM by appointment via email.

• Machine Learning
• Probabilistic Machine Learning
• Artificial Intelligence
• +5 more

Tim G. J. Rudner is a Faculty Fellow at New York University , an AI Fellow at the Georgetown Center for Security & Emerging Technology (CSET) , and an incoming Junior Research Fellow at Trinity College, University of Cambridge . He holds a PhD in computer science from the University of Oxford (advised by Yee Whye Teh and Yarin Gal), a master’s in statistics from Oxford, and a bachelor’s in mathematics and economics from Yale University . Rudner’s research focuses on robust and transparent machine learning models for safety-critical and high-stakes applications. His work spans probabilistic machine learning , uncertainty quantification , generative LLMs , Bayesian neural networks , AI safety , and biomedical discovery . He has developed methods like group-aware priors for subpopulation robustness, function-space regularization techniques, and uncertainty-aware fine-tuning approaches. His recent publications include work on 2025 papers about AI governance frameworks, 2024 studies on robust generative models and uncertainty quantification, and influential studies on vision-language control and diabetic retinopathy detection . His research has been recognized with awards such as the AISTATS Notable Paper Award and the GenAI4DM Outstanding Paper Award . Scientific Awards: 2024 Rising Star in Generative AI Qualcomm Innovation Fellow Rhodes Scholar AISTATS Notable Paper Award (2024) GenAI4DM Outstanding Paper Award (2024) Grants: $30,000 Apple Seed Grant (2024) $700,000 Foundational Research Grant to improve LLM trustworthiness (2024) Rudner actively mentors first-generation low-income students and collaborates with institutions like University of Oxford , Yale University , and New York University . His work bridges theoretical advancements with real-world deployment challenges, emphasizing reliable uncertainty estimation and robust generalization in high-stakes domains.

Michele CaprioView profile

Lecturer

• Probabilistic Machine Learning
• Imprecise Probability
• Uncertainty Quantification
• +3 more

Michele Caprio is a Lecturer (Assistant Professor) in Machine Learning and Artificial Intelligence at The University of Manchester. He holds a PhD in Statistics from Duke University (2022) and completed a postdoctoral fellowship at the University of Pennsylvania’s Department of Computer and Information Science. His research focuses on probabilistic machine learning, particularly imprecise probabilistic techniques for uncertainty quantification. He is actively accepting PhD students for projects such as 'Multimodal Credal Learning Theory' and is a member of the London Mathematical Society. Education: PhD in Statistics, Duke University (2022) Postdoctoral Researcher, University of Pennsylvania (Computer and Information Science) Research Interests: Dr. Caprio’s work bridges theoretical foundations and practical applications in machine learning, emphasizing uncertainty quantification through imprecise probabilities. Key areas include conformal prediction, distributionally robust optimization, and the ergodicity of Markov processes. His methods address challenges in ambiguous ground truth, distribution shifts, and epistemic uncertainty in AI systems. Scientific Awards: IJAR Young Researcher Award (2023) IMS New Researchers Travel Award (2023) IMS Hannan Travel Award (2022) Aleane Webb Dissertation Research Fellowship (2021) Advising & Projects: As PI of the MCAIF: Centre for AI Fundamentals project, he collaborates with researchers and students on AI fundamentals. His advisees include over 15 PhD candidates focusing on topics like distribution shift recovery, imprecise neural networks, and continual learning. Labs/Teams: Central to his work is the MCAIF initiative, a multidisciplinary team advancing theoretical and applied AI research through collaborative projects.

• Asymptotic Statistics
• Bayesian Variable Selection
• Nonparametric Inference
• +3 more

Minerva Mukhopadhyay is an Assistant Professor in the Department of Mathematics and Statistics at the Indian Institute of Technology Kanpur. She earned her PhD in Statistics from the Indian Statistical Institute (ISI), Kolkata, and completed postdoctoral work at Duke University under Professors David Dunson and Sandeep Dave. Her research focuses on Asymptotic Statistics, Bayesian Variable Selection, and Nonparametric Inference, with applications to high-dimensional data analysis. Her academic work includes developing distribution-free tests for high-dimensional data, consistency analysis of Bayesian variable selection methods, and novel approaches for sparse linear models. She has contributed to journals like Biometrika , Annals of the Institute of Statistical Mathematics , and Statistica Sinica , reflecting her expertise in theoretical and computational statistics. Research Trends: Her recent articles emphasize scalable Bayesian methods for ultra-high-dimensional data, random projections for predictive modeling, and nonparametric extensions using Gaussian processes. Scientific Awards: Best Student Paper Award (IISA 2015) Pillar Iglesias Travel Award (ISBA 2016) O’Bayes Travel Award (2017) Academic Roles: Previously taught at Bethune College (Kolkata) and Duke University, with experience in interdisciplinary statistical research at ISI. Skills: C, R, MATLAB, and journal reviewing for TEST , JSPI , Stat , and CSDA .

Omid ChatrabgounView profile

Assistant Professor

• Uncertainty Quantification
• Bayesian Machine Learning
• Gaussian Processes
• +5 more

Dr. Omid Chatrabgoun serves as an Assistant Professor in Data Science and AI at the CEES School of Science, holding a PhD in Data Science from Shahid Chamran University (Iran, 2015). Previously affiliated with Malayer University, he teaches undergraduate and postgraduate courses including Statistical Learning, Data Science, and Machine Learning while actively supervising PhD students. His research focuses on uncertainty quantification and Bayesian machine learning for complex systems. Education: PhD in Data Science, Shahid Chamran University, Iran (2015) Dr. Chatrabgoun specializes in Modelling, Optimization, and Uncertainty Quantification (UQ) using (Deep) Gaussian processes, with applications spanning bioinformatics (Gene Regulatory Networks, Protein-Protein Interactions) and environmental engineering (coastal protection, flood modeling). He develops novel approaches for machine learning of linear operational equations to quantify uncertainty in highly complex systems, emphasizing computational efficiency through sparse approximations and kernel methods. His work bridges theoretical statistics with practical implementations in healthcare, environmental science, and industrial applications. Analysis of his 29 publications (2016-2025) reveals a consistent trajectory in Gaussian process methodologies, evolving from foundational graphical models to advanced deep Gaussian processes and probabilistic surrogate modeling. Key thematic clusters include bioinformatics network inference, environmental risk assessment, and computational optimization techniques, demonstrating interdisciplinary impact across computer science, statistics, and domain-specific applications. Dr. Chatrabgoun has secured research funding and conducted consultancy projects with the Iran National Science Foundation (INSF) and Research Institute for Grapes and Raisin (RIGR). His collaborative network spans international institutions, as evidenced by co-authorships across civil engineering, bioinformatics, and environmental science domains. Current projects involve developing machine learning frameworks for linear differential operators and expanding applications of pair-copula Bayesian models in high-dimensional data analysis. His research group actively explores uncertainty propagation in PDE-based models and spatio-temporal environmental simulations, maintaining strong industry-academia partnerships for knowledge exchange in data-driven decision systems.

• Machine Learning
• Probabilistic Modeling
• Neural Networks
• +4 more

Carl Allen is a Laplace Junior Chair in Machine Learning at École Normale Supérieure, Paris, working in the research group of Stéphane Mallat, Giulio Biroli and Garbiele Peyré. Previously, he was a postdoctoral fellow at ETH Zurich and completed his PhD in Machine Learning in 2021 at the University of Edinburgh under the supervision of Professors Tim Hospedales and Iain Murray. His educational background includes a BSc in Mathematics & Chemistry from the University of Southampton, an MSc in Mathematics and the Foundations of Computer Science (MFoCS) from the University of Oxford, and MScs in Artificial Intelligence and Data Science from the University of Edinburgh. Before transitioning to AI/ML research, he spent several years in Project Finance. Allen's research focuses on mathematically understanding mechanisms behind successful machine learning methods, particularly neural networks. He investigates how machine learning models exploit aspects of data distribution from a probabilistic perspective. His current topics include explaining how VAEs disentangle independent factors of data, identifying mathematical models behind self-supervised learning, and deriving probabilistic interpretations of softmax classification. His PhD work investigated neural representations of discrete objects and their relationships, with a main result explaining how word embeddings can seemingly be added and subtracted (e.g., queen ≈ king - man + woman), which received Best Paper (honorable mention) at ICML 2019. His research spans theoretical foundations of machine learning, with particular emphasis on representation learning, disentanglement, and probabilistic modeling of neural networks. His work connects mathematical principles with practical machine learning applications, aiming to develop more interpretable and reliable algorithms. Allen has received notable recognition including a Best Paper honorable mention at ICML 2019 and a research grant from the Hasler Foundation. He has delivered invited talks at prestigious institutions including Harvard Center of Mathematical Sciences & Applications and Astra-Zeneca. His collaborative work spans multiple institutions, including a notable internship at Samsung AI Centre, Cambridge, where he worked at the intersection of representation learning and logical reasoning. His research has significant implications for developing more interpretable, reliable, and theoretically grounded machine learning systems.

David GlassView profile

Senior Lecturer

• Artificial Intelligence
• Bayesian Reasoning
• Explanatory Reasoning
• +5 more

David Glass is a Senior Lecturer in the School of Computing at Ulster University, where he has been a faculty member since 2000. He is an active member of the Artificial Intelligence Research Centre and teaches courses in both computer science and mathematics, including specialized modules in artificial intelligence. Dr. Glass holds a BSc in Mathematics, an MA in Philosophy, and a PhD in Theoretical Physics, all from Queen's University Belfast. Before joining Ulster University, he worked as a post-doctoral research associate at Queen's University Belfast. His research spans the intersection of computer science, mathematics, and philosophy of science. A key focus is on explanatory and Bayesian reasoning, with applications in artificial intelligence and the mathematical modeling of complex systems. His work has practical applications in healthcare, including COVID-19 modeling, epilepsy diagnosis, and dementia research. He has also explored the relationship between science and religious belief, applying Bayesian reasoning to topics like 'explaining away' and design arguments. Analysis of his recent publications shows a strong emphasis on conjunctive explanations, neural network modeling of chaotic systems, and healthcare applications of machine learning. Ulster University Research Excellence Award - Business Category (Under 50 employees), May 2019 Dr. Glass has supervised numerous PhD students and secured external funding for research projects, including "Conjunctive Explanations: how science and religion can work together" (2018-2021), "Extraction and Modelling of Alzheimer's Disease Data for Patient Stratification" (2015-2018), and "Explaining Away in Science and Religion" (2013-2015). His work demonstrates a strong commitment to interdisciplinary research that bridges technical computing with philosophical inquiry. As a member of the Artificial Intelligence Research Centre, Dr. Glass collaborates with colleagues on various AI applications in healthcare and complex systems modeling. He is actively involved in academic community building, having co-organized the 23rd Annual UK Workshop on Computational Intelligence in 2024 and planning "The Future of AI and Humanity" event for the NI Science Festival in February 2025.