Find Academic Supervisors

• Cosmic-ray physics
• Dark matter direct detection
• Astroparticle instrumentation
• +3 more

Kazuyoshi Kobayashi is an Associate Professor at Waseda University’s Faculty of Science and Engineering and a researcher in the Research Institute for Science and Engineering. He is an experimental astro-particle physicist whose work spans space-borne cosmic-ray calorimetry, direction-sensitive dark-matter searches, and ultra-low-background detector R&D. His scientific output includes 149 refereed papers and an h-index of 56 (Scopus, Sept 2025). Education: 1993 – 1997: B.S. in Physics, University of Tokyo 1997 – 2002: Ph.D. in Physics (cosmic-ray and particle physics), Graduate School of Science, University of Tokyo Research interests: Kobayashi addresses fundamental questions about the origin, acceleration and propagation of cosmic rays by measuring their elemental spectra (proton to nickel) from tens of GeV to hundreds of TeV with the CALET instrument on the International Space Station. Parallel to this, he develops and operates underground detectors to hunt for weakly interacting massive particles (WIMPs) and studies neutrino properties using large liquid-xenon and water-Cherenkov observatories. His technical expertise includes low-radioactivity photomultiplier tubes, micro-pattern gas detectors (µ-PICs), radon-emanation screening, and advanced statistical inference for rare-event searches. Publication trends (2020-2023): His recent articles concentrate on (i) precision cosmic-ray spectroscopy (electrons, protons, helium, boron, carbon, oxygen, nickel, iron) revealing spectral hardenings and charge-sign dependent solar modulation, (ii) world-leading dark-matter limits from XENON1T/XENONnT and XMASS-I covering nuclear recoils, electronic recoils, inelastic scattering and bosonic models, and (iii) multi-messenger follow-ups of gravitational-wave events with CALET and Super-Kamiokande. These works combine high-statistics space data with ultra-low-background underground measurements to probe both astrophysical and particle-physics phenomena. Awards & recognition: No named prizes are listed in the supplied text; however, his membership in the CALET, XENON, XMASS, NEWAGE and Super-Kamiokande collaborations, coupled with his role as corresponding analyzer for multiple high-impact papers, signifies strong peer recognition. Advising & grants: While formal student names and grant numbers are not published on the profile page, Kobayashi is regularly designated as analysis coordinator or corresponding author for graduate-student datasets, indicating active supervision of PhD and master’s students. His projects are supported by Japanese JSPS KAKENHI grants and international collaboration funds. Labs & teams: He leads the Japanese CALET operations team at Waseda, contributes to the XENON/XMASS low-background working groups, and heads detector R&D for the NEWAGE direction-sensitive TPC. These teams operate instruments at the Kamioka Underground Observatory and aboard the ISS, with ongoing upgrades toward XENONnT and future GADZOOKS!-style neutron-tagging phases of Super-Kamiokande.

• Machine Learning Theory
• Game Theory
• Information Theory
• +5 more

Prof. Wouter M. Koolen serves as Professor of Mathematical Machine Learning in the Statistics group at the University of Twente and as Senior Researcher in the Machine Learning group at Centrum Wiskunde & Informatica (CWI). He maintains active research affiliations with INRIA-CWI associate teams 6PAC (with Inria Lille) and 4TUNE (with Inria Paris and Grenoble), and holds the distinction of ELLIS Scholar. Dr. Koolen earned both his MSc and PhD cum laude from the Institute of Logic, Language and Computation at the University of Amsterdam, completing his doctoral work titled 'Combining Strategies Efficiently: High-quality Decisions from Conflicting Advice' in January 2011. His academic journey includes being designated a Master of Logic. Prof. Koolen's research spans theoretical machine learning with deep connections to game theory, information theory, statistics, and optimization. His current work focuses on pure exploration in multi-armed bandit models, game tree search algorithms, and provably accelerated learning methods in statistical and individual-sequence settings, which he characterizes as 'learning faster from easy data.' His theoretical contributions consistently demonstrate practical relevance in sequential decision making and statistical inference. Analysis of his recent publications reveals three dominant research threads: martingale-based methods for anytime-valid statistical inference using e-values, adaptive optimization algorithms with provable guarantees, and theoretical foundations of multi-armed bandit problems. His work increasingly bridges theoretical computer science with modern statistical methodology, particularly in sequential analysis and adaptive experimentation. His notable achievements include: NWO VENI grant for innovative research QUT Vice-Chancellor's postdoctoral research fellowship Designation as ELLIS Scholar recognizing European research excellence cum laude distinctions for both master's and doctoral degrees Prof. Koolen actively mentors the next generation of researchers, having supervised multiple PhD students to completion including Hongwei Wen, Clément Lezane, and Tyron Lardy with defenses scheduled for 2025. His research program is supported by competitive grants focusing on theoretical machine learning and statistical methodology. He maintains an active presence in the international research community through conference presentations, workshop organization, and collaborations across European institutions. Within the Machine Learning group at CWI and Statistics group at the University of Twente, Prof. Koolen contributes to a dynamic research environment focused on theoretical foundations with practical applications. His work often intersects with colleagues investigating sequential decision processes, game-theoretic approaches to learning, and robust statistical inference methods.

Alan WelshView profile

Professor

• Statistical Inference
• Statistical Modelling
• Mixed Models
• +4 more

Alan Welsh is the E.J. Hannan Professor of Statistics at the Australian National University (ANU), affiliated with the Research School of Finance, Actuarial Studies & Statistics. He holds prestigious fellowships from the Australian Academy of Science, Institute of Mathematical Statistics, and American Statistical Association. His research focuses on statistical inference, mixed models, robustness, nonparametric methods, ecological monitoring, and survey analysis. Education: PhD in Statistics (ANU, 1984) and BSc (Hons) in Mathematical Statistics (University of Sydney, 1981). Research interests emphasize advanced statistical methodologies with applications in ecology, environmental science, and healthcare. He has authored over 176 publications and secured funding through grants like 'Analytics for the Australian Grains Industry' (2024–2027) and 'Statistical Machine Learning' (2019–2024). His work spans high-dimensional data analysis, copula-enhanced models for medical imaging (e.g., myopia screening via fundus images), and spatial-temporal modeling of environmental data. He has also contributed to statistical software, notably the 'rpql' and 'mplot' R packages. Awards: Moran Medal (1990), Pitman Medal (2012), E.J. Hannan Medal (2019), and Honorary Life Membership (International Biometric Society, 2018). Welsh has advised numerous research students and served as Editor-in-Chief of the Australian and New Zealand Journal of Statistics (2012–2015) and Co-Editor of Biometrics (2020–2022). His current projects address challenges in big data analysis, robust estimation, and model selection for complex datasets.

Karl Oskar EkvallView profile

Assistant Professor

• Multivariate Analysis
• Mixed Models
• Latent Variable Models
• +4 more

Karl Oskar Ekvall is an Assistant Professor in the Department of Statistics at the University of Florida and an affiliated researcher at Karolinska Institutet. He serves as an Associate Editor of Statistics and Probability Letters . His research focuses on multivariate analysis, mixed and latent variable models, statistical computing, and applications in biomedical sciences and economics. Education: PhD in Statistics (University of Minnesota), M.Sc. in Finance and B.Sc. in Economics (University of Gothenburg). Former roles include Assistant Professor in Biostatistics at Karolinska Institutet and post-doctoral researcher at TU Wien. Research interests emphasize methodological advancements in statistical theory, with a focus on high-dimensional data analysis and model estimation techniques. His work bridges computational methods with practical applications in healthcare and economic systems. Active in academic service, he contributes to editorial work and has publications spanning statistical theory, biostatistical applications, and computational methodologies. Collaborations involve interdisciplinary projects in biomedical sciences and environmental health studies.

Abdul A. HusseinView profile

Professor

• Sequential analysis
• Survival analysis
• Finite mixtures
• +3 more

Dr. Abdul A. Hussein is a Professor in the Department of Mathematics and Statistics at the University of Windsor's Faculty of Science. He holds a Ph.D. from the University of Alberta. His research focuses on sequential analysis, survival analysis, finite mixtures, statistical process control, and health outcomes research. He has supervised multiple master’s students and is actively seeking Ph.D. candidates with expertise in probability theory and stochastic processes. His work spans theoretical and applied statistics, including contributions to nonparametric methods, robust estimation, and clinical trial design. Notable collaborations include studies on pediatric safety interventions and medical device reliability. He has secured research grants supporting projects in sequential analysis and health outcomes. His publications emphasize methodological advancements in statistical testing and applications to biomedicine. Dr. Hussein teaches advanced courses and mentors students in statistical theory and computational methods.

Davide FerrariView profile

Professor

• Data Integration
• Composite Likelihood Procedures
• Model Selection
• +5 more

Davide Ferrari is a Full Professor in the Faculty of Economics and Management at the Free University of Bozen-Bolzano. He teaches statistical methods and applied statistics at undergraduate and graduate levels, and coordinates the Master's program in Data Analytics for Economics and Management. His academic career includes tenured roles as Assistant Professor at the University of Modena and Associate Professor at the University of Melbourne. Research interests include data integration, composite likelihood procedures, and high-dimensional data inference. He recently explored model selection for intractable likelihoods in econometric and environmental contexts. He actively contributes to digital education innovation through projects like EDUNEXT, focusing on AI integration in teaching methodologies. He holds a PhD from the University of Minnesota's School of Statistics (2008). Office location: BZ E2.05, Piazza Università 1, Bolzano.

Markus ÅdahlView profile

Associate Professor

• Spatial Statistics
• Ambulance Call Forecasting
• Prehospital Care Optimization
• +4 more

Markus Ådahl serves as Associate Professor in the Department of Mathematics and Mathematical Statistics at Umeå University, Sweden. His research focuses on developing advanced statistical methodologies for emergency medical services optimization, with primary office location at MIT-huset, plan 3, Matematik och matematisk statistik, MIT.B.349, Umeå. His work specializes in spatial statistics and machine learning applications for prehospital care systems. Key research areas include spatio-temporal modeling of ambulance demand, composite likelihood intensity estimation, and large-scale forecasting of emergency calls using log-Gaussian Cox processes. This research directly addresses operational challenges in northern Sweden's emergency response infrastructure through data-driven approaches. Recent publications demonstrate consistent focus on ambulance call pattern analysis, with methodological innovations in regularized semi-parametric modeling (2023) and large-scale spatio-temporal forecasting (2020). These studies establish critical frameworks for optimizing ambulance resource allocation in geographically complex regions. Ådahl currently leads the long-term research initiative Data-driven optimization of prehospital care via statistics and machine learning (2018-2028), which integrates statistical modeling with machine learning to enhance emergency medical service efficiency. While no formal advisees are listed in available materials, this project coordinates cross-disciplinary collaboration between statisticians, healthcare providers, and emergency response authorities.

Qi ZhengView profile

Associate Professor

• High-dimensional data analysis
• Semiparametric and nonparametric models
• Varying effects survival analysis
• +4 more

Qi Zheng, PhD, is an Associate Professor in the Department of Bioinformatics and Biostatistics at the University of Louisville's School of Public Health and Information Sciences (SPHIS) . Joined in Fall 2015, Dr. Zheng's methodological research focuses on high-dimensional data analysis, semiparametric models, survival analysis, personalized treatments, tree-based learning, and statistical applications in epidemiology and biomedical sciences. Postdoctoral Fellow (2015), Bioinformatics and Biostatistics, Emory University PhD (2013), Mathematical Science, Clemson University BS (2006), Mathematics, Nanjing University, China Research interests span statistical methodology development for complex biomedical data. Key areas include high-dimensional regression, quantile regression, survival analysis, and machine learning applications in public health. Recent publications (2022–2013) demonstrate expertise in statistical hypothesis testing, survival modeling, and high-dimensional data methods. Articles address observational studies, censored quantile regression, time series change-point detection, and generalized linear models with complex covariates. Contact: qi.zheng@louisville.edu | Personal Website

Lingxiao WangView profile

Assistant Professor

• Survey Methodology
• Statistical Learning
• Epidemiology
• +3 more

Lingxiao Wang is an Assistant Professor at the University of Virginia, specializing in advanced statistical methodologies for complex survey designs and epidemiologic research. His work bridges survey statistics, statistical learning, and public health applications, with a focus on improving population inference accuracy and developing representative risk models. Ph.D., Survey Statistics and Methodology, University of Maryland, College Park M.A., Applied Statistics, University of California, Santa Barbara B.S., Mathematics and Statistics, Shandong University His research centers on enhancing efficiency in regression analyses for two-phase sampling, integrating data from surveys/registries for minority subgroup risk estimation, and leveraging statistical learning to improve external validity in nonprobability samples. Recent work explores gene-environment independence in case-control studies and pharmaceutical effects on cancer risk. Key article trends include: calibration methods for complex surveys, kernel weighting techniques for nonprobability samples, haplotype-based genetic inferences, and risk modeling for public health applications. His 2025 publications highlight improved lung cancer risk models and calibration strategies for pooled samples.

Abdollah JalilianView profile

Researcher

• Spatio-temporal Epidemiology
• Epidemiology
• Biostatistics
• +3 more

Dr. Abdollah Jalilian is a Research Associate in Spatio-temporal Epidemiology at Lancaster Medical School, part of Lancaster University's Faculty of Health and Medicine. His work focuses on developing statistical methodologies for analyzing spatiotemporal patterns in epidemiological data. Research Interests: His research integrates advanced statistical techniques with epidemiological studies, particularly in understanding disease spread dynamics and public health interventions. He applies spatial and temporal modeling to address challenges in healthcare analytics and infectious disease surveillance. Publications: His recent work includes a 2025 article in Biometrics , advancing composite likelihood methods for space-time point processes, which has implications for real-time disease monitoring systems. Consultancy: He contributed to the Evaluation of Virtual Ward deployment in NHS Trusts, demonstrating practical applications of his research in healthcare optimization. Advising & Grants: No specific advising or grant information is publicly listed here. Labs/Teams: Affiliated with Lancaster Medical School's research groups focused on health data science and epidemiological modeling.

Haziq JamilView profile

Assistant Professor

• Statistical Theory
• Bayesian Statistics
• Latent Variable Models
• +7 more

Haziq Jamil is an Assistant Professor in Statistics at Universiti Brunei Darussalam (UBD) within the Faculty of Science, Department of Mathematics. He concurrently serves as a Visiting Fellow at the London School of Economics and Political Science (LSE) Department of Statistics (2024-2027) and will transition to King Abdullah University of Science and Technology (KAUST) as a Research Specialist in August 2025, taking leave from UBD. His academic journey includes a PhD in Statistics (2018) and MSc in Statistics (2014) from LSE, and a BSc & Master in Mathematics, Statistics, Operational Research and Economics (2010) from Warwick University. His research spans statistical theory, methods, and computation with strong social science applications. Core interests include latent variable models, Gaussian processes, Bayesian statistics, and spatio-temporal modeling, particularly applied to Brunei's housing market and psychometric testing. He pioneered I-prior regression methodology using Fisher information kernels and developed bias-reduction techniques for Item Response Theory models. His publication record shows consistent output in high-impact journals with increasing focus on Brunei-specific applications since 2022. Haziq's 15 most recent publications (2022-2025) demonstrate methodological innovation in Bayesian computation, latent variable modeling, and spatial statistics, with growing emphasis on real-world applications in Brunei's housing market and educational assessment. Key trends include development of sparse Gaussian process models for property valuation, spatio-temporal analysis of Brunei's real estate, and bias-adjustment methods for psychometric models – reflecting his dual expertise in theoretical statistics and practical implementation. Teaching Excellence Award in Sciences (Universiti Brunei Darussalam, 2023) Arnold Zellner Thesis Award Honourable Mention (American Statistical Association, 2020) In-Service Training Scheme Scholarship (Brunei Public Service Commission, 2013-2018) Supreme Commander of Royal Brunei Armed Forces Scholarship (2006-2010) Best Student Award 2005 (Persekutuan Guru-Guru Melayu Brunei) Haziq serves as Graduate Programme Coordinator for Mathematics (2021-2025) and Faculty Liaison Committee member at UBD. His consultancy includes defense-related data analysis for Brunei's Ministry of Defence (2021-2022). He leads the Brunei R User Group (2024-2026) and maintains active research collaborations through the Bayesian Computational Statistics and Modelling (BAYESCOMP) group at KAUST. Current projects include open-source statistical tables, I-prior methodology R packages, and quantitative text analysis of Brunei's legislative council meetings. He directs multiple ongoing research initiatives including the Brunei housing market dataset covering 30,000+ transactions across three decades, Hamiltonian Monte Carlo educational tools, and quantitative analysis of Brunei's legislative proceedings. His work bridges theoretical statistics with practical applications in urban planning, defense analytics, and educational assessment within Brunei's unique socio-economic context.

Søren HøjsgaardView profile

Associate Professor

• Statistics
• Graphical Models
• Bayesian Networks
• +7 more

Søren Højsgaard is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Faculty of Engineering and Science. His work bridges statistical theory, computational methods, and pedagogical innovation, with a strong focus on graphical models, Bayesian networks, and computer algebra systems in R. His research interests lie at the intersection of statistics, machine learning, and software development. He is particularly known for developing R packages such as caracas , gRbase , and sparta , which facilitate symbolic computation and efficient inference in probabilistic models. His work supports both advanced research and accessible teaching in data science. The recent publications highlight a consistent trend toward integrating symbolic mathematics with statistical computing, improving scalability in Bayesian network predictions, and enhancing statistics education through tools like Quarto and R. His contributions span theoretical algorithms, software implementation, and educational applications. Active contributor to open-source statistical software Focus on model-based prediction and symbolic computation Emphasis on teaching innovation using computational tools He has been involved in academic outreach through conference presentations and media engagement, discussing topics ranging from R programming to workplace safety modeling. His leadership role in the Department of Mathematical Sciences was recently highlighted in university communications. Søren Højsgaard leads and contributes to projects that combine rigorous statistical methodology with practical implementation, supporting both research and education in modern data science.

Hugo Maruri-AguilarView profile

Lecturer

• Algebraic Statistics
• Computer Experiments
• Statistical Data Analysis
• +4 more

Dr Hugo Maruri-Aguilar is a Lecturer in Statistics at Queen Mary University of London, affiliated with the School of Mathematical Sciences and the Centre for Probability, Statistics and Data Science. His research focuses on algebraic statistics, computer experiments, and statistical modeling using likelihood and penalized likelihood techniques. He explores design of experiments through algebraic methods and develops emulators for computer experiments using polynomial models regularized by smoothness. His research interests include Lasso regression, topological data analysis, and applications in music generation and healthcare. Recent work emphasizes model selection and sparse polynomial prediction. He has collaborated on projects ranging from biomarker prognostic models in heart failure to AI-driven music composition. Dr Maruri-Aguilar has contributed to over 15 peer-reviewed publications across journals like Statistical Papers , Annals of the Institute of Statistical Mathematics , and Technometrics . His methodologies address challenges in optimal design, computational topology, and interdisciplinary applications in music and medicine.

Boris Jean Eudes BerangerView profile

Lecturer

• Extreme Value Theory
• Environmental Extremes
• Big Data
• +3 more

Boris Jean Eudes Beranger is a Lecturer in Data Science at the School of Mathematics and Statistics, UNSW Sydney, and an Associate Investigator at the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). He holds a PhD in Statistics from Université Pierre and Marie Curie (Paris 6) and UNSW Sydney (2016), and an MSc in Mathematics from Université Pierre and Marie Curie (2011). His research focuses on Extreme Value Theory applied to environmental extremes (e.g., floods, heatwaves), Symbolic Data Analysis for complex datasets, and Statistical Theory for big data challenges. He has developed novel methods for analyzing histogram-valued data and max-stable processes in high dimensions, with applications in climate science and telecommunications. Key publications include work on tail density estimation using kernel methods, composite likelihood approaches for spatial extremes, and likelihood-based inference for aggregated data. His work emphasizes computational efficiency and robustness in extreme event modeling. Awards: J.B. Douglas Award (2014), Outreach Participation Award (2019) Grants: ACEMS Research Support Scheme (2018–2021), multiple industry collaborations Teaching: Courses include Data Science fundamentals, regression analysis, and extreme value theory Labs/Teams: UNSW Data Science Hub (uDASH), leading the UNSW Statistics Seminar Series

• Quality Control
• Statistical Analysis
• Artificial Neural Networks
• +3 more

Prof. Ahmet Sermet Anagün serves as Professor and Head of the Department of Industrial Engineering at Izmir University of Economics, Faculty of Engineering since September 2017, with administrative leadership beginning in 2019. Previously, he held equivalent roles at Eskişehir Osmangazi University (2015-2017) following a 35-year academic progression from Research Assistant to full Professor. His educational credentials include: Ph.D. in Industrial Engineering, Cleveland State University (1993) M.S. in Industrial Engineering, Anadolu University (1985) B.S. in Industrial Engineering, Anadolu University (1982) Research focuses on Quality Control and Statistical Analysis, with significant contributions to fuzzy logic applications in control charts and neural networks for biosignal processing. His methodology integrates experimental design, Taguchi techniques, and machine learning for industrial optimization problems spanning manufacturing, environmental engineering, and educational technology. Analysis of his 14 most recent publications reveals dominant trends in fuzzy control systems (60% of works) applied to quality assurance, with emerging interdisciplinary research in biosurfactant production and educational technology analysis using CHAID methods. His approach consistently bridges statistical theory with practical engineering solutions. No scientific awards were documented in the provided materials. Prof. Anagün actively supervises Master's theses and senior capstone projects across engineering disciplines, teaching core courses in Statistical Quality Control, Engineering Statistics, and Human Factors Engineering. His administrative service includes University Erasmus Coordination and KOSGEB representation, though specific grant funding details remain unlisted.