Erik Brucken is a University Lecturer at the Department of Physics, University of Helsinki, affiliated with the Faculty of Science. He is also a supervisor for doctoral programs in Particle Physics and Universe Sciences, and Materials Research and Nanosciences. Research focuses on high energy physics and experimental particle physics, particularly in CMS experiment collaborations at CERN. Active in projects like the Euroopa hiukkasfysiikan tutki (2024–2027) and ALICE Time Projection Chamber Upgrade Project (2013–2018). His research interests include Higgs boson studies, top quark dynamics, jet physics, and detector development. He has published extensively on topics such as rare decays, muon identification, and quantum entanglement in collider experiments. Advises doctoral students in particle physics and collaborates widely, participating in conferences like the International Europhysics Conference on High Energy Physics and CMS Tracker Weeks. His work contributes to advancing detector technologies and computational tools for high-energy physics.
Professor Xu Kai is a faculty member at the School of Mathematics and Statistics of Anhui Normal University, specializing in high-dimensional data analysis and nonlinear dependency measurement. He earned his bachelor's degree in engineering before transitioning to statistics, obtaining his master's and PhD under Professors He Daojiang and Zhu Liping respectively. His research addresses challenges posed by the 'curse of dimensionality' through innovative statistical methodologies published in top journals like The Annals of Statistics . He has secured multiple grants including National Natural Science Foundation projects and won prestigious awards such as the Anhui Youth Mathematics Award. Education: Bachelor's in Engineering (top scores) Master's in Probability Theory & Mathematical Statistics (Anhui Normal University) PhD in Statistics (under Prof. Zhu Liping) Research focuses on developing statistical tools for high-dimensional data, with applications in biomedicine and economics. His testing methods address covariate significance in regression equations, exemplified by work on gene mutation analysis. Teaching emphasizes functional thinking and interdisciplinary problem-solving, mentoring students like Zhang Jialong who praised his rigorous academic standards. Awards: 2023: Anhui Natural Science Foundation Young Scholar 2022: National Natural Science Foundation General Project 2021: 5th Anhui Youth Mathematics Award Advising & Grants: Directed 5+ student research projects Main investigator for 3 National Natural Science Foundation grants Labs/Teams: Leads the High-Dimensional Statistical Analysis Research Group at Anhui Normal University.
Dr. Maike M. Burda is associated with Humboldt-University of Berlin and held academic roles including Lecturer (2004–2014) and Research Assistant at the Research Department Migration, Integration, Transnationalization (2015–2018). Her work bridges econometrics and migration studies. Education includes a PhD in Economics (2001, Humboldt University) and a Diploma in Economics (1991, Christian-Albrechts-Universität Kiel). Research focuses on econometric methodologies, migration dynamics, and transnational social processes. Her 1997 publication in Journal of Econometrics explores Wald tests under nonregular conditions, reflecting expertise in statistical hypothesis testing. No awards listed, though her contributions to econometric theory are notable. Advising and grants details are not provided, but she has been affiliated with multiple academic institutions in Berlin. Labs/teams: Former involvement with the Migration, Integration, Transnationalization research unit at Humboldt University.
Fabrizio Leisen is a Professor of Statistics at King’s College London, Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. Previously, he held positions at the University of Nottingham (Professor), University of Kent (Reader), and institutions in Spain and Italy. He earned a PhD in Mathematics from the Università di Modena e Reggio Emilia, specializing in Probability. His research focuses on Bayesian inference, nonparametric methods, objective Bayesian analysis, and foundational statistical theory, with notable contributions to predictive constructions, knockoff procedures, and stochastic processes. He serves as an Associate Editor for Bayesian Analysis , Statistics and Probability Letters , and Statistical Methods and Applications . Leisen’s work bridges theoretical and applied statistics, emphasizing Bayesian nonparametric priors for complex data structures and model selection. His recent articles address survival analysis, prior elicitation without subjective inputs, and algorithmic advancements in Bayesian computation. He collaborates globally, including co-supervising a PhD student at the Università di Bologna. His teaching includes Computational Statistics and Probability and Statistics Skills sessions. Research interests span Bayesian foundations, model selection via knockoffs, stochastic processes, and interdisciplinary applications like genomic and biostatistical analysis. His lab is affiliated with King’s Statistics group, focusing on MCMC methods, Bayesian nonparametrics, and experimental design.
Sara Giovagnoli is an Associate Professor at the University of Bologna's Department of Psychology 'Renzo Canestrari', specializing in psychometrics and cognitive psychology. Her research focuses on developmental disorders, sleep science, and educational technology. Key areas include emotion regulation in learning disabilities, motor sleep inertia mechanisms, and cognitive assessment tools like Bayesian networks for intelligence testing. Her work integrates behavioral neuroscience with digital innovation, exemplified by projects such as the PsycAssist AI system for neuropsychological assessment and the Eye-Riders game for neurodiverse children. She explores the intersection of chronobiology and mental health, analyzing circadian rhythms in adolescents and neurodegenerative patients through actigraphy. Recent studies address repetitive negative thinking in university students and the role of social support in adolescent internet use. Methodologically, she advances statistical frameworks like Bayesian informative hypotheses and comparative dimensionality reduction techniques. Her contributions span clinical applications (e.g., Multiple Sclerosis motor activity analysis) to educational interventions targeting early literacy and executive function enhancement through adaptive technologies. Collaborations emphasize cross-disciplinary approaches, linking neurophysiological metrics to psychosocial outcomes in diverse populations including teachers, parents of preterm infants, and young carers. Her research bridges theoretical cognitive science with practical tool development for clinical and educational settings.
Arthur Berg is a Professor in the Department of Public Health Sciences at Penn State, with affiliations in the Cancer Institute and multiple clinical departments. He holds concurrent professorships in Surgery, Neurosurgery, Family and Community Medicine, and Statistics. Berg earned his PhD in Mathematics from UCSD (2007) and has extensive expertise in biostatistics and bioinformatics, including computational tool development and methodological research. His research focuses on statistical methodologies for biostatistics, Bayesian analysis, and clinical trial design. Notable contributions include work on NIH-funded trials, spinal cord injury nutrition, and tumor heterogeneity in oncology. He serves as Director of the Biostatistics PhD Program since 2012 and teaches advanced courses like Bayesian Statistics and Asymptotic Tools. Berg collaborates across disciplines, with recent publications spanning spinal cord injury outcomes, cancer treatment dynamics, and diabetes care in immigrant populations. His computational methods emphasize robust statistical frameworks and interdisciplinary applications, reflecting his dual strengths in theoretical and applied statistics.
Dr. Karen Baab is a Professor of Anatomy at Midwestern University, holding cross-appointments across multiple colleges including the Arizona College of Optometry, College of Dental Medicine-Arizona, and Arizona College of Osteopathic Medicine. She specializes in paleoanthropology and anatomical morphology, with a focus on human evolution, craniofacial evolution, and hominin fossil analysis. Her research integrates geometric morphometrics to study evolutionary patterns and functional adaptations in extinct and modern humans. Education: Ph.D. in Anthropology, The Graduate Center, CUNY (2007) M.A. in Anthropology, Hunter College, CUNY (2003) B.A. in Anthropology, Muhlenberg College (2000) Research Interests: Dr. Baab explores topics such as hominin cranial variation, the evolutionary relationships between Homo erectus and modern humans, and biomechanical adaptations in the skull and larynx. Her work often involves reconstructing fossil specimens (e.g., Homo floresiensis pelvis, Gona hominin fossils) and analyzing their morphological and functional implications. Teaching: She teaches anatomy courses, including head and neck anatomy for dental and medical students, and serves as a course director for anatomical sciences programs. Outreach: Active in science education, she judges the Arizona State Science and Engineering Fair and contributes to public outreach initiatives.
Fabio Gómez-Rodríguez is an Assistant Professor in the Department of Economics at Lehigh University since 2021. Originally from Costa Rica, he holds a Ph.D. in Economics from Indiana University (2021), an MA in Economics from Indiana University (2018), and a Dipl.-Wirt.-Math. from Technische Universität Darmstadt (2011). His research focuses on time series analysis with applications to monetary and fiscal policy, particularly exploring non-stationarities and their effects on inflation expectations and economic shocks. His teaching spans multiple institutions, including Lehigh University (courses: Econometrics I, Statistical Methods II) and Indiana University (Introduction to Microeconomics, Empirical Econometrics). He has also taught at the Universidad de Costa Rica in mathematics and statistics courses. His research interests emphasize regime-dependent effects of policy, exchange rate pass-through mechanisms, and econometric methodologies for testing unit roots and regime shifts. His working papers address topics such as the impact of U.S. government decisions on borrowing costs and distributional effects of inflation expectations under different policy regimes. While his current profile does not explicitly list scientific awards or grants, his extensive publication record reflects active engagement with cutting-edge econometric techniques and macroeconomic policy analysis.
Jabed Tomal is an Associate Professor in Statistics and Data Science at Thompson Rivers University (TRU), Canada. Previously, he held positions as an Assistant Professor at TRU (2018–2023) and the University of Toronto Scarborough (2014–2018), and a Postdoctoral Fellow at the University of British Columbia (2014). He earned a Ph.D. in Statistics (2013) from UBC, specializing in statistical machine learning, and dual M.Sc. degrees in Statistics (University of Windsor, 2007) and Biostatistics (University of Dhaka, Bangladesh). His research focuses on ensemble methods, Bayesian inference, and statistical ecology, with applications in drug discovery, protein homology, and environmental modeling. Key research interests include developing ensemble models for high-dimensional data, Bayesian methods for breakpoints detection in housing markets and ecological systems, and statistical approaches in healthcare. Notable contributions include work on QSAR studies, Bayesian hierarchical modeling of pandemic impacts, and ecological threshold detection. Tomal has secured grants such as the NSERC Discovery Grant ($102,500) and TRU internal funds for projects in big data and environmental thresholds. He has advised numerous graduate and undergraduate students on topics ranging from machine learning in healthcare to single-cell RNA sequencing analysis. His teaching spans courses like Bayesian Machine Learning, Multivariate Statistics, and Theoretical Machine Learning at the graduate level, alongside foundational statistics and calculus courses at TRU and the University of Dhaka. Administrative roles include Chair of the Award and Scholarship Committee for TRU’s Master of Data Science program and membership in Senate Research Committee. Education: Ph.D., Statistics (2013), UBC Vancouver M.Sc., Statistics (2007), University of Windsor M.Sc., Biostatistics (2005), University of Dhaka Awards: NSERC Discovery Grant (2021–2026) Research Training Recognition Fund (TRU,多次) SSC 2013 Talk Honourable Mention Labs/Teams: Active in interdisciplinary projects at TRU’s Department of Mathematics and Statistics, focusing on data science applications in ecology, healthcare, and genetics.
Daniel Pressnitzer is a CNRS Director of Research and Deputy Manager of the Laboratoire des Systèmes Perceptifs (LSP) at École normale supérieure in Paris. He leads the Audition team within LSP, conducting research at the intersection of auditory perception, cognitive neuroscience, and psychophysics. His work spans from fundamental auditory mechanisms to applied research in hearing impairment and audio processing. Pressnitzer's research focuses on experimental studies of audition, integrating multiple levels of analysis from acoustic signals to non-verbal auditory cognition. His work employs diverse methodological tools including psychophysics, modeling, and physiology. A central hypothesis driving his research is the existence of 'mid-level audition' processes critical to real-world listening that can be systematically tested through psychophysical methods. Current projects investigate auditory memory, perceptual bistability, multi-modal comparisons, music perception, and natural sound recognition. Analysis of Pressnitzer's recent publications reveals a strong emphasis on auditory memory mechanisms, cross-modal perception, and the neural correlates of conscious processing. His work increasingly incorporates machine learning approaches to analyze complex auditory phenomena while maintaining rigorous psychophysical methodologies. Notable recent contributions include discoveries about sleep deprivation detection through voice analysis and the relationship between auditory perception and conscious processing. Fyssen Fellowship Wellcome Trust Postdoctoral Fellowship Pressnitzer teaches in several prestigious programs including the masters of cognitive sciences from École Normale Supérieure, EHESS, and Universités Paris 5, where he coordinates the Auditory Perception course. He also teaches in the ATIAM and IMA masters programs. As the leader of the Audition team at LSP since 2008, he co-manages the Neuroscience track of the Cogmaster, recruiting students annually. His laboratory investigates fundamental auditory processes with potential applications in hearing impairment and audio processing technologies.
Michael Heiss is an Honorary Professor affiliated with the Data Science department at Vienna University of Technology (Technische Universität Wien). His research focuses on practical applications of data science in enterprise systems, environmental monitoring, and entrepreneurship. 2023 publication on metaverse business trips 2016 case study on enterprise social networks (Siemens TechnoWeb) 2016 feasibility study for forest monitoring drones 2015 work on hypothesis-driven entrepreneurship His work demonstrates strong industry collaboration through case studies with organizations like Siemens. Key research areas include: Enterprise social network implementation Drone-based acoustic surveillance Entrepreneurship methodology Metaverse applications He has supervised multiple student theses on these topics, indicating active academic mentorship.
Robert Schoen, MD, MBA, is a Clinical Professor in the Department of Internal Medicine at Yale School of Medicine, specializing in Rheumatology. His clinical expertise includes rheumatoid arthritis, psoriatic arthritis, and Lyme disease. With a career spanning over four decades, he holds an MD from Columbia University (1976), an AB in Biochemical Sciences from Harvard (1972), and an MBA from the University of Connecticut (2001). His research focuses on viral arthritis (particularly chikungunya and Lyme disease), postinfectious inflammatory conditions, and therapeutic interventions for chronic arthritic conditions. Dr. Schoen’s research has produced over 30 peer-reviewed publications, including seminal works on chikungunya arthritis pathogenesis, Lyme disease management, and methotrexate efficacy. Notable contributions include a 2025 systematic review linking chikungunya fever to rheumatoid arthritis and a 2024 randomized trial evaluating methotrexate/dexamethasone treatment for chikungunya arthritis. His work bridges clinical practice and translational research, emphasizing patient-centered care and global health implications. Recognitions include designation as a Master of the American College of Rheumatology (2023) and inclusion in multiple ‘Top Doctors’ lists. His academic leadership includes roles in residency training at Yale New Haven Hospital and fellowships at Brigham and Women’s Hospital (1981). Ongoing efforts focus on improving diagnostic accuracy and therapeutic strategies for emerging and chronic rheumatologic conditions. Education: AB Harvard (1972), MD Columbia (1976), MBA UConn (2001) Board Certifications: Rheumatology and Internal Medicine (ABIM) Awardees: Master of the American College of Rheumatology, 2023 ‘Top Doctors’ (Yale Medicine)
Nolan Cole holds the position of Professor and Chair in the Department of Biostatistics at the University of Washington. His research focuses on developing statistical methods for high-dimensional data, selective inference, and addressing measurement error in compositional datasets, with applications in genomics and epidemiology. He collaborates with Dr. Daniela Witten and Dr. Arkajyoti Saha on hypothesis testing methodologies in feature-selected datasets. Education includes a PhD in Biostatistics from the University of Washington and a Bachelor’s in Statistics from Brigham Young University. His prior research spans meta-analysis methodologies for zero-event clinical trials, mediation analysis in breast cancer genetics, and spatial transcriptomics simulations during his time at Harvard Medical School. Key projects include investigating distal non-coding RNA regulation in breast cancer using eQTLs, analyzing electronic health records for multiple myeloma patients, and studying epigenetic differences in monocytes based on sex. His work bridges statistical theory with biomedical applications, aiming to improve disease understanding through advanced data analysis techniques.
Dr. Jennifer Boer is a Postdoctoral Research Fellow at RMIT University's Research & Innovation Capability department in Bundoora West, Australia. Her research focuses on immunology, clinical sciences, oncology, nanotechnology, and medical health sciences, with a particular emphasis on vaccine development, nanomedicine, and infectious disease mechanisms. She is actively involved in supervising Masters and PhD students, including projects like 'Metagenetic Approach to Analyse Vaccine Immunomodulation in the Elderly' and 'Gold-based drugs for the effective treatment of ovarian cancer'. Her work spans interdisciplinary areas, such as vaccine delivery systems using liposomes and polymeric nanoparticles, and the study of SARS-CoV-2 mutations affecting protein flexibility. She also explores ethical and statistical aspects of animal experimentation design. Dr. Boer collaborates widely, contributing to fields like environmental microbiology, drug discovery pedagogy, and glaucoma immunotherapy. Her recent publications highlight advancements in vaccine adjuvants, anaerobic sewage sludge analysis, and the role of tumor necrosis factor receptors in disease. She is open to mentoring students in these and related areas.
Jianjun Miao is a Professor recognized for his significant contributions to economic theory, evidenced by his 2025 election as Fellow of the Society for Advancement of Economic Theory. His academic work bridges theoretical economics with practical policy applications across multiple domains. His research spans economic theory, asset pricing, monetary and fiscal policy, rational inattention, and asset bubbles. Miao's work demonstrates particular expertise in modeling decision-making under uncertainty, financial market dynamics, and macroeconomic policy interactions. His research integrates advanced mathematical techniques with real-world economic phenomena, focusing on how information frictions and ambiguity affect market outcomes and policy effectiveness. Analysis of his 2022-2025 publications reveals three dominant research trajectories: (1) asset bubbles and their macroeconomic implications, (2) rational inattention frameworks in discrete choice and asset pricing, and (3) fiscal-monetary policy coordination in constrained environments. His work consistently addresses methodological challenges in modeling uncertainty while providing policy-relevant insights for financial stability and economic growth. Scientific Awards: Fellow of the Society for Advancement of Economic Theory (2025) Professor Miao maintains an active research program with substantial scholarly output. His work appears in leading economics journals and influences both academic discourse and policy discussions regarding financial stability, economic growth, and optimal policy design under uncertainty. He contributes significantly to academic knowledge through theoretical model development and empirical applications.
