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Jakub StoklosaView profile

Senior Lecturer

• Capture-Recapture Modeling
• Ecological Statistics
• Measurement Error Modeling
• +4 more

Dr. Jakub Stoklosa is a Senior Lecturer at the School of Mathematics & Statistics, University of New South Wales. He holds a PhD in Applied Statistics (2012) and a BSc (Hons) in Science (2007) from The University of Melbourne. PhD in Applied Statistics, The University of Melbourne (2012) BSc (Hons) in Science, The University of Melbourne (2007) His research focuses include: Analysis of capture-recapture data Estimation of animal abundance Measurement error modeling Model selection for multivariate data Non-parametric smoothing Recent publications emphasize statistical applications in ecology, biodiversity, and environmental science, with methodological contributions to error-in-variables regression and zero-truncated models. Scientific awards: 2018 Australian Museum Eureka Prize top 3 finalist (Burramys Genetic Rescue Team) NSW Office of Environment and Heritage Eureka Prize for Environmental Research (2018) Grants: ARC Discovery Project Grant DP210101923 (2021–2023) for "Innovative statistical methods for analysing high-dimensional counts" with D.I. Warton

Boris BerangerView profile

Senior Lecturer

• Extreme Value Theory
• Environmental Extremes
• Spatial Statistics
• +3 more

Boris Beranger is a Senior Lecturer in Statistics and Data Science at the School of Mathematics and Statistics, UNSW Sydney . He is also a member of the UNSW Data Science Hub (uDASH) and previously served as an Associate Investigator at the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) . His research spans theoretical and applied statistics, focusing on Extreme Value Theory (environmental, financial, and insurance applications) and Symbolic Data Analysis (complex/non-standard data structures). Education: PhD in Statistics (Université Pierre and Marie Curie & UNSW, 2016), MSc in Mathematics (Université Pierre and Marie Curie, 2011) Research Trends are evident in: High-dimensional extremal dependence modeling (ExtremalDep package) Spatial extremes and max-stable processes Symbolic/histogram/interval-valued data analysis Composite likelihood and aggregated data methods Tail density estimation via kernel methods Scientific Awards & Grants include: J.B. Douglas Award for Postgraduate Excellence (2014) Multiple ARC ACEMS Research Support Schemes Discovery Project DP220103269 ($405,000) for modeling real-world extremes Supervision covers PhD, Masters, and Honours students in areas like Symbolic Data Analysis, Spatial Extremes, and Statistical Computing. He also co-organized workshops and served as Vice-President (2025-26) of the Statistical Society of Australia's NSW Branch.

Kaniav KamaryView profile

Researcher

• Bayesian Inference
• Bayesian Model Choice
• Prior Distribution
• +3 more

Kaniav Kamary is a researcher specializing in Bayesian inference and mixture distribution modeling, affiliated with the Laboratory of Mathematics and Computer Science for Complexity and Systems. Their work bridges computational statistics, probabilistic modeling, and applied mathematics. Research Interests Kamary's research focuses on Bayesian model choice Non-informative prior development Reparameterization techniques for mixture models Statistical validation of computational methods Scientific Contributions Recent publications address methodological challenges in Bayesian inference and applications in health informatics and environmental modeling.

Robert ZeithammerView profile

Professor

• Applied Econometrics
• Applied Game Theory
• Choice Models
• +5 more

Robert Zeithammer is a Professor of Marketing at the Anderson School of Management , University of California, Los Angeles. He is globally recognized for his expertise in pricing strategies, consumer preference modeling, and auction theory, with a focus on digital marketplaces and quantitative market research. Ph.D. in Management Science, MIT Sloan School of Management, 2003 M.A. in Mathematics, University of Pennsylvania, 1998 B.A. in Economics and Mathematics, University of Pennsylvania, 1998 His research spans participative pricing mechanisms and Bayesian preference measurement . Zeithammer's work combines analytical modeling with structural estimation techniques to analyze digital pricing innovations like eBay auctions and Priceline's NYOP model. Key themes include: Dynamic pricing in sequential auctions Consumer risk preferences in bidding Conjoint analysis methodology Policy implications of pricing algorithms Recent publications focus on algorithmic pricing regulation , abortion policy preferences , and ignition interlock device effectiveness . Scientific awards include the Paul Green Award finalist status (2016) and the German Academic Association for Business Research Best Paper Award (2015).

• Statistical Ecology
• Multiple Systems Estimation
• Illegal Wildlife Trade
• +3 more

Professor Rachel McCrea holds a Chair in Statistics at Lancaster University's School of Mathematical Sciences and is a core member of the Data Science Institute. She actively supervises PhD students in statistical methodology development for real-world ecological and conservation challenges, with current research focusing on statistical ecology, multiple systems estimation, and illegal wildlife trade applications. Her research program centers on: Advanced capture-recapture and state-space modeling for population dynamics Multiple systems estimation for hidden populations (wildlife trade/human trafficking) Integrated population modeling for conservation decision-making Bias correction in translocation management and extinction inference Bayesian methods for ecological time-series and demographic analysis Recent publications (2021-2025) reveal strong methodological innovation in ecological statistics, with key contributions to integrated population models, multi-system estimation frameworks, and conservation applications. Her work bridges theoretical statistics with urgent biodiversity challenges, particularly in extinction risk assessment and wildlife trade monitoring. Current research projects include 'New stochastic models to quantify illegal trade' (2023-2024), 'A transdisciplinary approach to inferring the end of political violence' (2022-2023), and 'Modelling removal and re-introduction data for improved conservation' (2022-2023). She supervises PhD student Lucas Howell through the STOR-i Centre for Doctoral Training. Professor McCrea collaborates extensively through Lancaster's Centre of Excellence in Environmental Data Science, Environmental and Ecological Statistics group, Social and Economic Statistics group, and STOR-i Centre for Doctoral Training, driving interdisciplinary approaches to ecological data science.

• Logic (formal and philosophical)
• Formal Epistemology
• Philosophy of Science
• +3 more

A. V. Ravishankar Sarma is Professor in the Department of Humanities and Social Sciences at Indian Institute of Technology Kanpur . He specializes in formal and philosophical logic, formal epistemology and philosophy of science, with current research centred on belief revision, causality and scientific-theory change. Education: PhD, Philosophy, IIT Bombay (2006) – Thesis on “ On Causal and Constructive Modeling of Belief Revision ” MA, Philosophy, University of Hyderabad (1998) MSc, Physics, Kakatiya University, Warangal (1996) Research Interests: His work integrates logical formalisms with philosophical questions about how scientific beliefs are revised when new evidence or causal information becomes available. Key themes include: Belief Revision & AGM Paradigms: developing constructive models that incorporate causal relevance. Causality: analysing causal counterfactuals and their semantics within logical frameworks. Philosophy of Science: investigating theory change, scientific realism (structuralist defence), and the role of abductive reasoning in discovery. Scientific Awards & Fellowships: Post-doctoral Fellow, Indian Institute of Management Bangalore (Feb–Sep 2006) GLoRiClass Fellow, Institute for Logic, Language and Computation (ILLC), University of Amsterdam (Oct–Dec 2006) Post-doctoral Fellow, Homi Bhabha Centre for Science Education, TIFR (Feb–May 2007) Contact & Resources: Office: Faculty Building FB-659, Department of Humanities and Social Sciences, IIT Kanpur Phone: +91 512 259 6137 (O) / +91 512 259 8208 (R) E-mail: avrs@iitk.ac.in Web: Homepage & CV

Manuela Karola ZucknickView profile

Professor

• Biostatistics
• Cancer Research
• Risk Prediction
• +4 more

Manuela Karola Zucknick is a Professor in the Department of Biostatistics at the University of Oslo , where she leads statistical learning research for translational and clinical cancer applications. Her work focuses on integrating multi-omics data for personalized cancer therapies, predicting drug responses, and modeling prognosis. Director of Oslo Centre for Biostatistics and Epidemiology (2023–present) Professor (2022–present) and Associate Professor (2015–2022) at University of Oslo Research Interests span high-dimensional statistical modeling, Bayesian methods for heterogeneous data integration, regularization techniques, and applications in pharmacogenomics. She develops tools for drug combination screens, survival modeling, and risk prediction incorporating prior biological knowledge. Key domains: Biostatistics, Integrative Genomics, Precision Medicine Methodological focus: Bayesian structured variable selection, Penalized Regression Publications demonstrate expertise in pan-cancer transcriptomics, proteomics for pregnancy complications, and machine learning for DNA methylation analysis. Recent work includes Tutorial on Survival Modeling (2024) and Dose-Response Prediction (2023) applied to pharmacogenomic datasets. Collaborations span clinical trials in colorectal cancer nutrition, prostate cancer screening for Lynch syndrome patients, and chronic pain research using molecular profiling.

• Statistical Learning
• Machine Learning
• Hypothesis Testing
• +3 more

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

Mario MarchandView profile

Professor

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +3 more

Mario Marchand is a retired Professor in the Department of Computer Science and Software Engineering at Université Laval, Canada. His research focuses on machine learning theory, explainable AI, and computational biology. He has contributed to foundational work in PAC-Bayesian analysis, generalization bounds, and kernel methods. Marchand is affiliated with the Computer Vision and Systems Laboratory and the GRAAL research group. Though no longer supervising graduate students, his recent work addresses challenges in algorithmic stability, meta-learning, and feature attribution consensus. His publications span topics from neural network optimization to applications in bioinformatics and drug discovery. Research Interests: Machine Learning Theory, Explainable AI, Bioinformatics, Kernel Methods, Domain Adaptation, and Statistical Learning. Key Contributions: PAC-Bayesian risk bounds, decision tree analysis, and algorithms for mixture models. His work bridges theoretical guarantees and practical applications in healthcare and computational biology. Professional Activities: Taught courses including IFT-7002 (Foundations of Machine Learning) and contributed to international conferences. His methods are used in biomarker discovery and peptide design for drug development. Recent trends in his articles emphasize explainable AI (XAI) and resolving feature attribution disagreements, alongside foundational studies on generalization in meta-learning and multi-source domain adaptation. His collaborative projects include predicting molecular properties and advancing neural network stability.

• Wildlife Ecology
• Movement Ecology
• Spatial Ecology
• +3 more

Dr. Kevin Morelle is a Researcher at the Max Planck Institute of Animal Behavior, Department of Migration, located in Radolfzell, Germany. His academic background includes a bio-engineering background in forest and nature management, followed by an MSc in wildlife ecology (focusing on post-fire habitat use of red deer) and a PhD on wild boar movement ecology across scales. He leads the Research Group Safi and has over a decade of experience in wild boar spatial ecology research. Key research interests include African Swine Fever (ASF) epidemiology in wild boar populations, movement ecology, and innovative wildlife monitoring technologies. He co-founded the Euroboar network to advance wild boar ecological knowledge and currently works on the Horizon 2020 VACDIVA project to study ASF spread mechanisms. Collaborating with IT engineers, he develops alternative ear-tagging devices for wild boar monitoring. Publications highlight his work on ASF transmission dynamics, movement syndromes, and spatial risk modeling. His research bridges ecology, technology, and public health, addressing critical issues in wildlife disease control and conservation.

• Signal Processing
• Wavelet Analysis
• Image Processing
• +3 more

Prof. Dr. Ing. Alexandru Isar is a Full Professor at the Department of Communications, Faculty of Electronics and Telecommunications, Politehnica University of Timișoara. He holds academic ranks since 1990 (Adjunct Professor), 1995 (Associate Professor), and 1999 (Full Professor). His research focuses on signal processing, wavelet analysis, and medical imaging. He has advised 5 Ph.D. students and currently supervises 2 doctoral candidates. Isar has published extensively in IEEE journals and authored books on time-frequency representations and network security. **Education**: Ph.D. in Engineering (1993), supervised by Prof. Eugen Pop. Completed postdoctoral research at Telecom Bretagne, France (2003). **Grants**: European Space Agency-funded projects (2013-2017) and national grants (IDEI 2009-2011). **Research Directions**: Introduced time-frequency representations and wavelet theory applications. **Notable Contributions**: Inventions in ultrasonic control systems (1987), development of the Hyperanalytic Wavelet Transform (2008), and SAR/SONAR image denoising algorithms. **Administrative Roles**: Deputy Dean (2001-2004), Department Director (2012-2020). **Collaborations**: European Space Agency projects, international conferences (Brest, Bordeaux), and peer reviews for IEEE journals. His work bridges theoretical signal processing with practical applications in healthcare and telecommunications.

Dimitris FouskakisView profile

Professor

• Bayesian Statistics
• Objective Bayesian Methodology
• Stochastic Optimization
• +3 more

Dimitris Fouskakis is a Professor at the Department of Mathematics within the School of Applied Mathematical and Physical Sciences at the National Technical University of Athens (NTUA), where he also serves as the Director of the Statistics Lab. His research primarily focuses on Bayesian statistics, objective Bayesian methodology, and stochastic optimization. He has developed novel approaches for prior specification, variable selection, and model comparison in statistical modeling. Professor Fouskakis maintains active research interests in: Objective Bayesian methods and prior construction Bayesian variable selection and model averaging Stochastic optimization techniques High-dimensional data analysis Applications in educational statistics and public health His recent publications demonstrate a consistent focus on Bayesian methodology development, particularly in the areas of shrinkage priors, power-expected-posterior methods, and model comparison techniques. The research spans both theoretical developments and practical applications across multiple domains including education, public health, and computational statistics. Professor Fouskakis directs the Statistics Lab at NTUA, which serves as a hub for statistical research and collaboration. While specific grant and student advising information isn't provided in available texts, his leadership role indicates active engagement in research supervision and academic mentorship.

• Federated Learning
• Machine Learning
• Cybersecurity
• +3 more

Yan Xiao is an Adjunct Professor at the University of Illinois. His research focuses on federated learning, cybersecurity, and distributed systems, with an emphasis on adversarial robustness, privacy-preserving techniques, and optimization in machine learning frameworks. He has contributed to foundational tools like FedML and explores challenges in federated learning security, energy efficiency, and causal mechanisms. Key research interests include federated learning architectures, adversarial defense mechanisms, and the integration of reinforcement learning for automated feature selection. His work spans theoretical advancements (e.g., invariant aggregators for backdoor attacks) and practical applications (e.g., thermal management in HPC systems). Recent publications highlight trends in federated learning security, distributed optimization, and causal discovery. Notable contributions address straggler-free coreset-based learning and Bayesian-optimized energy efficiency in federated setups. No scientific awards or grants are explicitly mentioned in the provided texts. Advising details are not listed, though collaborations with research groups in federated learning and HPC are implied through publication topics.

Siwei LiuView profile

Associate Professor

• Research Methodology
• Developmental Psychology
• Health Psychology
• +4 more

Dr. Siwei Liu is an Associate Professor in the Department of Human Development & Family Studies at the University of California, Davis. Their research focuses on advanced statistical methods for analyzing longitudinal data in developmental and health psychology, with an emphasis on biobehavioral synchrony in close relationships and health disparities. They lead a lab investigating multi-layer contextual factors (e.g., environment, family characteristics) affecting health behaviors, prioritizing diversity in both research subjects and student recruitment. Research Interests : Longitudinal data methodologies: multilevel modeling, time series analysis, functional data analysis Biobehavioral synchrony in parent-child and couples relationships Health disparities, environmental justice, and stress physiology Intensive longitudinal methods in health research Key Projects : GROWell randomized controlled trial for dietary interventions in prenatal/postpartum populations Multimodal assessment of biobehavioral synchrony in autism spectrum disorder Analysis of family risk dynamics and parenting stress through cortisol contagion Labs/Teams : Directs a lab prioritizing underrepresented students, focusing on innovative methods for studying emotions, well-being, and family systems. Collaborates on NIH-funded projects involving wearable devices and ecological momentary assessments.

• Evolutionary Biology
• Genetics
• Bioinformatics
• +3 more

Rui Carlos Pinto Borges is a Lecturer in Statistics at the School of Mathematics and Statistics, University of St Andrews. His research focuses on evolutionary biology, population genetics, and computational methods, with particular expertise in Bayesian statistical modeling and genomic analysis. He contributes to the UN Sustainable Development Goals through his work in these fields. Education details are not explicitly provided in the text, but his academic role suggests advanced qualifications in statistics and genetics. His research interests include cladistics, allele analysis, evolutionary adaptation, and genomic sequencing. Notable contributions include developing Bayesian methodologies for estimating selection pressures (Bait-ER software) and effective population sizes, alongside analyzing mutation patterns in bacterial populations. His work has been published in journals like Methods in Ecology and Evolution and Molecular Biology and Evolution . He collaborates internationally on topics such as phylogenetic signal testing and genomic sequence analysis. Borges has created datasets like 'mutation_models' and 'Bait-ER v1.0' available on Zenodo, highlighting his commitment to open science.