Boris Levit is a Professor in the Department of Mathematics and Statistics at Queen's University, located in Kingston, Ontario. He holds a Ph.D. from the Institute of Information Transmission in Moscow and a Dr.Sc. from Vilnius University. His research focuses on non-parametric estimation, semi-parametric models, optimal design in non-parametric regression, and optimal interpolation methods. Levit has made significant contributions to statistical theory, particularly in applying differential geometry and partial differential equations to statistical problems. Levit's academic journey includes positions at Moscow State University, the University of Utrecht in the Netherlands, and Queen's University. His work emphasizes modern non-asymptotic approaches to statistical estimation, utilizing Jacobi elliptic functions and addressing challenges in moderate or small sample sizes. He is currently recruiting graduate students. His research has been recognized through awards, including the Dr.Sc. from Vilnius University for integrating differential geometry into statistical research. His publications span over five decades, addressing topics like minimax estimation, spline interpolation, and adaptive filtering. He teaches courses in statistical inference and nonparametric methods.
Antoine MALLET is a Researcher affiliated with the Université de Technologie de Troyes (UTT). His research focuses on digital forensics, AI-generated image detection, and steganalysis. He holds a Doctorate (PhD) and is active in publishing cutting-edge work on topics such as deepfake detection, cover-source mismatch analysis, and statistical methods for image forensics. His office is located in H115, and he can be contacted at antoine.mallet@utt.fr. Key research contributions include methodologies for detecting AI-generated content through noise correlation analysis, mitigating cover-source mismatch in steganalysis, and developing universal detectors for synthesized images. His work bridges machine learning, signal processing, and cybersecurity to address modern challenges in digital media forensics. Recent publications (2023-2025) emphasize statistical approaches, matrix-based techniques, and interdisciplinary applications in forensic science. No scientific awards are explicitly mentioned, though his prolific output suggests impactful contributions to the field. Antoine’s research also explores the intersection of deepfakes and steganography, proposing systematic reviews and novel frameworks to enhance multimedia security. His work aligns with UTT’s commitment to technological innovation and data protection.
Severin Maurer is a Lecturer and Researcher at the University of Applied Sciences Wiener Neustadt , affiliated with the Institute of Market Research & Methodology . His research focuses on interdisciplinary fields including User Experience (UX) research combined with psychophysiological methods, biomedical engineering (e.g., blood pressure measurement techniques), and gender studies. Research Interests: Development of paradigms integrating traditional UX methods with psychophysiological measurements (e.g., facial expression analysis, heart rate variability). Biomedical studies on non-invasive blood pressure monitoring and its clinical applications. Market research on sensory perception and consumer behavior in food products, particularly the impact of visual and sensory cues. Gender gap analysis in modern professional contexts (Gender Gap 4.0 project). Key Projects: Paradigms for UX Research : Combining UX methods with psychophysiological techniques to assess user stress and emotional responses. Under Pressure – The Final Chapter : Evaluating blood pressure measurement techniques and their clinical relevance. Gender Gap 4.0 : Investigating gender disparities in contemporary work environments. Advising & Collaboration: Collaborates with institutions on UX and biomedical projects, contributing to teaching materials like instructional videos. Active in presenting research at conferences (e.g., 48th EMAC Annual Conference). Labs/Teams: Part of the Institute’s research teams focusing on market methodology and biomedical applications.
Christina L. Belanger is an Associate Professor at Texas A&M University, specializing in Marine Paleoecology and Climate Change research. Her work focuses on reconstructing paleoenvironments using foraminifera and geochemical proxies, studying ecological responses to environmental changes, and exploring oceanographic influences on marine biogeography. She actively engages in educational initiatives, including developing FossilSketch software for teaching micropaleontology. Educational Background: PhD in Geophysical Sciences, University of Chicago (2011) BA in College of Creative Studies, University of California, Santa Barbara (2005) Research Interests: Reconstructing paleoenvironments via benthic foraminifera and geochemical proxies Climate change impacts on marine ecosystems and fisheries Modern and ancient biogeography of benthic foraminifera Application of digital tools in fossil identification education Recent Research Focus: Low-oxygen events in the Gulf of Alaska over 60,000 years Arctic ecosystem responses to Cenozoic climate changes Benthic foraminiferal responses to Cretaceous Ocean Anoxic Events Modern ecological changes in Matagorda Bay, Texas Awards & Honors: College of Arts and Sciences Research Impact Award (2023) Montague-Center for Teaching Excellence Scholar (2021-2022) Association of Former Students College-Level Distinguished Achievement Award (2021-2022) Grants & Collaborations: NSF CAREER Grant ($506,000): Benthic foraminiferal biogeography and climate change NSF IUSE Grant ($300,000): FossilSketch educational software development Lab & Teaching: Runs a lab group addressing climate change impacts and fossil identification education Teaches courses in paleontology, geological writing, and paleobiology
Marko Heyse is the Managing Director of the Institute of Sociology at the University of Münster and leads the BEMA research group. He holds a Master’s degree in Modern History, Sociology, Political Science, and Dutch Studies from the Universities of Münster and Nijmegen. His professional roles include research positions at the University of Osnabrück and the University of Münster’s Applied Physics Institute, as well as policy work in the NRW Parliament and Brussels. He is affiliated with the Ethics Committee of the Ärztekammer Westfalen-Lippe and a member of the German Sociological Association. Research focuses on empirical social research methodologies and political sociology, with a strong emphasis on quantitative methods. Key projects include the long-running Münster-Barometer survey (1993–2026), evaluating urban policies, migration, and social dynamics. His teaching spans statistics, methodology, and sociological theory, with courses like 'Multivariate Analysis Methods' and 'Sociology of the Police'. Major grants include projects on migration and urban security (2018–2021), football fan communication (2013–2016), and environmental policy studies. Teaching contributions span over a decade, with leadership in methodological training and interdisciplinary research.
Pieter François is Professor of Cultural Evolution at the University of Oxford and a Tutorial Fellow in Human Sciences at Regent’s Park College. He is also the Academic Lead on Data Science and AI for the Arts and Humanities at the Alan Turing Institute and serves as Executive Director of the Seshat: Global History Databank. He leads major interdisciplinary projects including 'Data/Culture: Building Sustainable Scholarly Communities' and the Freedom of Religion or Belief Leadership Network. His research centers on cultural evolution , social complexity , and historical dynamics , with specific interests in ritual, warfare, inequality, and religious tolerance. He develops and applies methodologies for large-scale collaborative research in the digital humanities, leveraging structured historical data to test theories about societal development and collapse. His recent publications highlight a growing focus on artificial intelligence and historical reasoning , particularly through the HiST-LLM benchmark, which evaluates LLMs on PhD-level historical knowledge. These works reveal a trend toward integrating computational methods with deep historical analysis, emphasizing empirical validation and cross-cultural comparison. Scientific contributions and leadership roles include: Founding Director and Executive Director, Seshat: Global History Databank Principal Investigator, AHRC-funded 'Data/Culture' project Academic Lead, AI for Arts and Humanities, Alan Turing Institute Co-leader, Social Complexity and Collapse Group, Complexity Science Hub (CSH) He actively mentors researchers and leads collaborative teams across institutions, fostering sustainable scholarly communities around humanities datasets and software. His work is supported by major grants and involves extensive international collaboration, with future research likely to expand AI applications in historical sciences and deepen understanding of societal resilience and transformation. He is affiliated with key research groups such as the Social Complexity and Collapse Group at the CSH and contributes to major workshops and public-facing science communication, as seen in recent press coverage on AI and history.
Janice Scealy is an Associate Professor in the Research School of Finance, Actuarial Studies & Statistics at The Australian National University (ANU). She holds a PhD in Statistics from ANU (2011) and a Bachelor of Mathematics (Honours) from the University of Wollongong (2003). Her research focuses on compositional data analysis, directional statistics, robust statistics, and applications in geosciences like palaeomagnetism and seismology. She has held roles including ARC DECRA Fellow (2018-2022) and has supervised multiple research projects. Notable contributions include advancements in hypersphere-based statistical models and robust estimation techniques. Education: PhD (ANU 2011), BMath (Wollongong 2003) Her research interests include statistical methods for manifold-valued data, geostatistics, and model selection in linear mixed models. Recent work applies elliptical distributions to seismic moment tensor classification and integrates Fisher's palaeomagnetic theories with modern statistics. She leads projects on non-Euclidean data analysis and biologics immunogenicity assessment. Key collaborations involve geophysical applications and statistical methodologies for spatial data. Awards include ANU research grants (2018–2025) and contributions to international statistical reviews.
Matthias Neumann is an Assistant Professor at the Institute of Statistics, Graz University of Technology . He completed his PhD in 2020 at Ulm University under Prof. Volker Schmidt, earning the PhD prize of Ulm University . His research focuses on stochastic 3D modeling and statistical analysis of micro- and nanostructures for functional materials, including battery electrodes , fuel cells , and paper-based materials . He has received start-up funding from ProTrainU (2020-2022) and served as principal investigator in the POLiS Cluster of Excellence (2022-2023). Research Interests: His work integrates mathematical morphology , machine learning , and spatial statistics to develop methods for microstructure quantification , estimation of geometrical descriptors (e.g., tortuosity, constrictivity), and data-driven models linking morphology to effective physical properties . He utilizes random fields , point processes , and copulas for virtual microstructure generation and parameter estimation. Teaching: He lectures on Applied Statistics , Statistical Modeling , and Mathematical Statistics at Graz University of Technology, with prior teaching experience at Ulm University in Multivariate Stochastic Modeling , Point Processes , and Spatial Statistics . Scientific Achievements: PhD prize of Ulm University (2020) ProTrainU start-up funding (2020-2022) POLiS Cluster of Excellence grant (2022-2023) Publications: His 15 most recent articles (2023-2025) emphasize machine learning techniques for microstructure segmentation , stochastic 3D modeling of nanoporous materials , and data-driven quantification of transport-property relationships . Key topics include random forests , neural networks , and R-vine copulas applied to fuel cells , sodium-ion batteries , and polymer electrolytes .
Akil Narayan is a Professor in the Department of Mathematics and a member of the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His office is located in WEB 4666 (SCI) and LCB 116 (Math). He has previously held positions as Assistant Professor at the University of Massachusetts Dartmouth (2012-2015) and Visiting Assistant Professor at Purdue University (2009-2012). His educational background includes: Ph.D. in Applied Mathematics from Brown University (2009) M.Sc. in Applied Mathematics from Brown University (2004) B.S. in Engineering Sciences and Applied Mathematics from Northwestern University (2003) B.S. in Electrical Engineering from Northwestern University (2003) Akil Narayan's primary research interests lie in numerical analysis, scientific computing, and approximation algorithms. His work spans multiple domains including uncertainty quantification, multifidelity modeling, optimization, and computational methods for partial differential equations. He has made significant contributions to the development of numerical methods for solving complex computational problems across various scientific and engineering disciplines. His research often bridges theoretical mathematics with practical applications in fields such as biomedical engineering, ecology, and power systems. Analysis of his recent publications reveals a strong focus on uncertainty quantification, multifidelity methods, and scientific machine learning. His work increasingly integrates traditional numerical methods with modern machine learning techniques, particularly in the development of physics-informed neural networks. There's also a notable emphasis on structure-preserving numerical methods and optimization techniques for computational models. His research has significant applications in biomedical imaging, particularly in electrocardiographic imaging and cardiac modeling. While specific scientific awards are not detailed in the available information, his extensive publication record in top-tier journals demonstrates recognition in his field. His work appears regularly in prestigious journals such as SIAM Journal on Scientific Computing, Journal of Computational Physics, and SIAM Review. Professor Narayan has advised numerous graduate students through the Department of Mathematics and the School of Computing at the University of Utah. His current advisees include Filip Belik, Haoyu Chen, John Turnage, and Yinqian Yu, working on topics ranging from numerical methods for PDEs to operator learning and uncertainty quantification. His former students have gone on to positions at institutions including General Motors, Amazon, Intel Corporation, and various academic institutions. He has also secured research funding supporting his work in computational mathematics and scientific computing, though specific grant details are not provided in the available text. He is actively involved with the Scientific Computing and Imaging (SCI) Institute at the University of Utah, where he collaborates with researchers across disciplines. His work through the UncertainSCI project focuses on uncertainty quantification for computational models in biomedicine and bioengineering, particularly in cardiac applications. He frequently collaborates with researchers in the Department of Mathematics, School of Computing, and the SCI Institute on interdisciplinary projects that combine mathematical theory with practical computational applications.
Jouni Helske is an Academy Research Fellow in Statistics at the University of Turku, Finland, affiliated with the INVEST Research Flagship Centre. He leads the CAUSALTIME project and is a subconsortium-PI in the PREDLIFE consortium at the University of Jyväskylä. His work bridges statistical methodology and applied research in social sciences and epidemiology. Academy Research Fellow, University of Turku PI, CAUSALTIME Project Subconsortium-PI, PREDLIFE Consortium, University of Jyväskylä Associate Editor, The R Journal and rOpenSci Open Science Ambassador, Open Science Community Turku Education: PhD in Statistics, University of Jyväskylä, Finland (2015) Jouni Helske’s research centers on developing advanced Bayesian methods for causal inference, particularly using complex multivariate time series and panel data. His expertise includes state space models, hidden Markov models, computational statistics, and probabilistic programming. He is deeply involved in statistical software development, especially in the R ecosystem, contributing to open science and reproducible research. His applied work spans sociology, education, public health, and epidemiology, where he analyzes longitudinal and sequential data to understand causal mechanisms and life course trajectories. The recent publications highlight a strong trend in methodological innovation for causal analysis in panel data, spatio-temporal disease modeling, and R package development. His work integrates Bayesian computation with real-world applications, especially in social policy and health, using historical and contemporary data. The focus on dynamic multivariate models and sequence analysis underscores his leadership in modern statistical methodology for complex data. Scientific Awards and Recognition: Academy Research Fellow (prestigious research position funded competitively) Jouni Helske has led and contributed to major research projects such as CAUSALTIME and PREDLIFE, which aim to improve policy decisions through predictive modeling of life trajectories. He mentors and collaborates widely, evidenced by his numerous co-authored publications and software projects. While no formal students are listed, his role as a project leader and software maintainer suggests significant advisory and collaborative activity. He is a key contributor to the open-source statistical community, particularly through rOpenSci and Stan. Labs and Teams: He leads the CAUSALTIME project team and is part of the PREDLIFE research consortium. He is actively involved in the R and Stan developer communities, contributing to state-of-the-art Bayesian computational tools.
Univ.-Prof. Dr. rer. nat. Esther Florin is Professor at Heinrich-Heine-University Düsseldorf and heads the Florin AG within the Institute of Clinical Neuroscience and Medical Psychology . Her group investigates the neurophysiological basis of resting-state networks and their alterations in Parkinson’s disease using advanced electro-/magnetoencephalographic (E/MEG) techniques. Research Focus: Neurophysiology of resting-state networks (RSNs) Pathophysiology of idiopathic Parkinson’s disease Electrophysiological mechanisms of dopaminergic therapy and deep brain stimulation Development of novel time-series and Granger-causality methodologies Across her recent publications, a clear trend emerges toward understanding causal interactions within cortical–subcortical circuits in both healthy and parkinsonian brains. Studies repeatedly combine high-resolution E/MEG recordings with sophisticated multivariate analysis to link oscillatory activity to cognitive–motor symptoms and therapeutic interventions. Scientific Awards: No specific awards are mentioned in the provided text. Advising & Team: Prof. Florin leads a multidisciplinary team comprising post-docs, doctoral researchers, medical doctoral candidates, and student assistants. Current members listed include Rachel Spooner, Matthias Sure, Bahne Hendrik Bahners, and several MD and MSc candidates. Laboratory & Resources: The Florin AG operates within modern facilities at HHU Düsseldorf (Building 12.46, Room 01.18.01) equipped for simultaneous E/MEG recordings, real-time neurofeedback, and advanced computational analysis.
Hans Christian Petersen is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, where he conducts interdisciplinary research combining evolutionary biology and applied statistics. His work focuses on the variation and evolution in gibbons, sexual dimorphism, and Mesolithic populations in Northern Europe, supported by advanced statistical techniques including multivariate analysis and geometric morphometrics. Research Interests: Evolutionary Biology, particularly gibbon (Hylobatidae) evolution and sexual dimorphism Mesolithic populations in Northern Europe, including skeletal and population analysis Applied statistics in biological data, including nonparametric methods and limits of agreement Geometric morphometrics and multivariate statistical modeling His recent publications show a strong trend in both paleoanthropological studies (e.g., Maglemosian skeleton, Mesolithic burials) and methodological statistical research (e.g., simulation studies on limits of agreement), reflecting a dual expertise in empirical archaeology and statistical innovation. This interdisciplinary approach enables robust analysis of complex biological datasets. Scientific Engagement: Regular speaker on topics such as Neanderthal biology and human evolution Active peer reviewer and conference participant Contributor to public media on human evolution and societal topics He has taught courses such as BB512 Population and Evolution and has engaged in international academic activities, including guest lectures and research stays. While specific grants and advising roles are not detailed, his extensive publication and activity record suggest active research leadership. He is involved in collaborative networks across Europe, particularly in archaeological and anthropological research.
Beatriz Giraldo Giraldo is a prominent researcher at the Institute for Bioengineering of Catalonia (IBEC), where she leads the Biomedical Signal Processing and Interpretation research group. Her work bridges biomedical engineering and clinical medicine, focusing on advanced signal processing techniques applied to physiological signals. She maintains strong affiliations with the University of Barcelona and Universitat Politècnica de Catalunya, contributing to the collaborative research environment of IBEC. Dr. Giraldo Giraldo's research focuses on cardiorespiratory analysis, particularly in the context of mechanical ventilation weaning. She has developed sophisticated methods using time-frequency analysis, wavelet transforms, and machine learning to predict weaning outcomes and analyze respiratory patterns. Her work has significant clinical implications for intensive care units, helping determine the optimal timing for extubation and reducing complications from premature ventilator removal. Her recent publications demonstrate consistent research productivity with a focus on applying advanced signal processing techniques to solve clinical problems. She has published extensively on topics including cardiorespiratory phase synchronization, heart rate variability analysis, and the development of medical decision support systems using artificial intelligence. Dr. Giraldo Giraldo has received recognition through numerous publications in high-impact journals and conferences including IEEE Transactions on Biomedical Engineering, Physiological Measurement, and annual IEEE Engineering in Medicine and Biology Society conferences. Her research has been consistently funded, supporting ongoing work in biomedical signal processing and its clinical applications. She actively mentors students and collaborators, with numerous publications showing co-authorship with junior researchers. Her work involves substantial interdisciplinary collaboration between engineers, physicians, and computer scientists, reflecting the integrative nature of modern biomedical research.
Jiaming Xu is an Associate Professor of Business Administration in the Decision Sciences area at Duke University's Fuqua School of Business, where he has been a faculty member since July 2018. He is also a Faculty Network Member of the Duke Institute for Brain Sciences. His academic journey includes positions as an Assistant Professor at Purdue University's Krannert School of Management (2016-2018), a Research Fellow at the Simons Institute for the Theory of Computing at UC Berkeley (2016), and a Postdoctoral Fellow at the Statistics Department of the Wharton School at the University of Pennsylvania (2015). Ph.D. in Electrical and Computer Engineering from University of Illinois, Urbana-Champaign (2014) M.S. in Electrical and Computer Engineering from University of Texas, Austin (2011) B.S.E. in Electrical and Computer Engineering from Tsinghua University, China (2009) Professor Xu's research focuses on developing fundamental methodologies for inferring information from data to enable downstream data-driven decision-making at scale. His work spans machine learning, networks, high-dimensional statistics, and information theory. He develops algorithms for improving decision-making efficiency under uncertainties and resource constraints while addressing emerging privacy and security issues. His research has significant implications for network data privacy, where he has demonstrated how anonymized data can still be used to re-identify individuals through unique behavioral patterns and network connections. His recent publications reveal a strong focus on graph matching problems, community detection in networks, and privacy-preserving machine learning. Xu has made significant contributions to understanding information-theoretic thresholds in random graph matching, developing efficient algorithms for network alignment, and establishing fundamental limits for community detection. His work increasingly addresses the challenges of federated learning and privacy-preserving data analysis, reflecting the growing importance of these areas in both theoretical and practical contexts. Scientific Awards and Recognition: NSF CAREER Award (2022) for Federated Learning: Statistical Optimality and Provable Security Simons-Berkeley Fellowship (2016) Excellence in Teaching Award in the MQM program (awarded twice) Professor Xu has successfully mentored several students who have gone on to prestigious positions, including Sophie H. Yu (Assistant Professor at the Wharton School), Hanjing Zhu (Researcher at Amazon), Liren Yu (Researcher at Huawei), and Zhiyi Tian (Data Scientist at IQVIA). His research has been supported by multiple significant grants including an NSF CAREER award (2022-2027), a CIF Medium grant for Learning in Networks (2019-2023), and BIGDATA and CRII grants focused on network analysis and high-dimensional data (2018-2021). At Duke, Professor Xu teaches Modern Analytics (Deep Learning), Decision Analytics & Modeling in the MQM program, and Decision Models in the MBA and WEMBA programs. His work bridges theoretical foundations with practical applications, particularly in the areas of network privacy and data security, where he has demonstrated how seemingly anonymized data can still be used to identify individuals through sophisticated matching algorithms.
David Jobst is a Researcher at the Institute for Mathematics and Applied Informatics within the Faculty of Mathematics, Natural Sciences, Economics and Computer Science at the University of Hildesheim, where he has been employed since August 2020. He teaches undergraduate mathematics courses including Introduction to Analysis, Stochastics, and Advanced Seminars on Series and Infinite Products. Dr. Jobst holds multiple degrees from the Technical University of Munich: a Master of Education and First State Examination in Mathematics and Sports Education (2017-2020), a Bachelor of Science in Mathematics with a minor in Electrical and Information Technology (2015-2019), and a Bachelor of Education in Mathematics and Sports Education (2014-2017). His research focuses on advanced statistical methods for weather forecasting, specializing in distributional regression, copula modeling (particularly Vine Copulas), and probabilistic weather prediction. He develops innovative approaches to postprocess ensemble weather forecasts using machine learning techniques, with particular attention to spatio-temporal dependencies in meteorological data. Jobst's publication record demonstrates a clear progression in developing sophisticated statistical frameworks for weather forecast postprocessing, moving from traditional methods like Ensemble Model Output Statistics (EMOS) to advanced machine learning approaches including gradient-boosted models and vine copula structures. His work bridges theoretical statistics with practical meteorological applications, addressing challenges in temperature, wind speed, and cloud cover forecasting. While no specific awards are documented in the available materials, his research has been presented at numerous international conferences including the European Geosciences Union General Assembly, CMStatistics conferences, and specialized workshops on ensemble postprocessing across Europe. As a university researcher, Jobst participates in academic service through conference organization and peer review activities. His consultation hours are conducted via virtual meeting platforms, reflecting modern academic practices. He maintains a private academic website (jobstdavid.org) where additional research materials and contact information are available.
![](https://www.queensu.ca/mathstat/people/faculty/profiles/"/mathstat/sites/mastwww/files/styles/staggered_tiles_image/public/2021-01/levit-boris.jpg?h=10d202d3&itok=sloydmne")
