Subhashis Ghoshal is a Goodnight Distinguished Professor in the Department of Statistics at North Carolina State University (NCSU). He holds a Ph.D. in Statistics from the Indian Statistical Institute (1995). His research focuses on Bayesian nonparametrics, high-dimensional models, asymptotic theory, and functional data analysis. He has authored influential books like *Fundamentals of Nonparametric Bayesian Inference* (2017) and contributed to methodologies in image processing and statistical inference. Key awards include the Goodnight Distinguished Professorship (2021), Dr. Cavell Brownie Mentoring Award (2014-15), and the De Groot Prize (2019). He has held editorial roles in journals like *Statistical Science* and *Annals of Statistics*. His work bridges theory and applications, addressing challenges in modern statistical problems such as uncertainty quantification and causal inference. He advises on graduate programs and actively contributes to academic leadership at NCSU.
Prof. Gerhard Jäger holds the Chair of General Linguistics at the Faculty of Humanities, University of Tübingen . He serves as a Principal Investigator (PI) in the Clusters of Excellence Human Origins and Machine Learning for Science , and leads projects like Phylomilia (funded by Volkswagen Foundation) and CrossLingference (ERC Advanced Grant). His career spans multiple institutions, including Bielefeld University (2004-2009) and Stanford University (visiting scholar, 2004). Habilitation (2002) at Humboldt University Berlin with thesis on Anaphora and Type Logical Grammar PhD (1996) at Humboldt University Berlin on Dynamic Semantics His research bridges computational linguistics , phylogenetic analysis , and game theory , focusing on Bayesian models , language evolution , and cross-linguistic typology . Recent work explores phylogenetic inference from acoustic speech data and geographic influences on language trees . Key contributions include 15+ recent publications on topics spanning phylogenetic typology , cognate detection , and Bayesian language modeling . These works employ machine learning , statistical inference , and evolutionary game theory to analyze language change , typological variation , and linguistic stability . Honors include ERC Advanced Grant , Volkswagen Foundation funding , and DFG-Humanities Centre for Advanced Studies participation. He has taught courses in Computational Historical Linguistics , Phylogenetic Methods , and Bayesian Data Analysis across institutions like Tübingen, Bielefeld, and Stanford. He actively contributes to academic communities through workshop organization (e.g., Quantitative Theoretical Linguistics , Game Theory in Pragmatics ) and serves on the faculty council at Tübingen. His team collaborates with institutions like Max Planck Institute for Evolutionary Anthropology , University of Pennsylvania , and LMU Munich .
Duygu Uygun Tunc is a Collegiate Assistant Professor in the Department of Philosophy at the University of Chicago, where she has been teaching since September 2023. She is also affiliated with the Society of Fellows and holds the position of Harper-Schmidt Fellow. Her academic journey includes a dual PhD from Heidelberg University in Germany and the University of Helsinki in Finland, which she completed in October 2020 with her dissertation titled 'Communication and the Origins of Personhood'. Her research spans multiple dimensions of epistemic value in scientific research, investigating what makes individuals good researchers, groups good scientific collectives, epistemic systems good scientific communities, and research procedures good empirical studies. She works extensively on intellectual virtues in science, scientific communities, experimental methodology (including testability, theory choice, and underdetermination), and applied philosophy of science (addressing the replicability crisis, science policy, and scientific norms). A central question guiding her work is how the social nature of scientific inquiry can best be utilized to advance scientific knowledge. Her publications from 2019-2024 demonstrate a consistent focus on the intersections of philosophy of science, epistemology, and values in science. Her work often examines distributed cognition in scientific communities, the nature of scientific expertise in contemporary research environments, and the relationship between scientific methodology and social values. A notable trend across her publications is the exploration of how scientific knowledge production has become increasingly social and collaborative, requiring new conceptual frameworks to understand epistemic responsibility in team science. Marie Curie Actions Fellowship for 'Extended Scientific Virtue' project Dr. Uygun Tunc currently teaches 'Science and Values' (PHIL 23404/33404) at the University of Chicago, examining debates about the value-free ideal of science and the relationship between scientific objectivity and social values. She is actively working on a series of articles on values in science and her first book manuscript on scientific expertise, which develops a novel conception addressing distributed epistemic labor and technological extension of epistemic processes while maintaining notions of epistemic responsibility. She maintains a blog on the epistemology of science where she explores topics like trust and criticism in scientific communities.
Marta Halina is a University Associate Professor in the Philosophy of Cognitive Science at the University of Cambridge, affiliated with the Department of History and Philosophy of Science. She serves as a Senior Research Fellow at the Leverhulme Centre for the Future of Intelligence and is a Fellow of Selwyn College. Her academic journey began with a PhD in Philosophy and Science Studies from the University of California, San Diego in 2013, followed by a McDonnell Postdoctoral Fellowship in the Philosophy-Neuroscience-Psychology Program at Washington University in St. Louis before joining Cambridge in 2014. Halina's educational background includes a PhD from UC San Diego (2013) and postdoctoral training at Washington University in St. Louis. Her academic trajectory reflects a strong interdisciplinary foundation bridging philosophy, cognitive science, and neuroscience. Her research focuses on nonhuman animal cognition, mechanistic explanation, and artificial intelligence, with particular emphasis on comparative cognition and the philosophical foundations of cognitive science. Halina investigates how researchers design studies to address complex questions about animal minds, arguing that current methods in comparative cognition often face challenges with hypothesis underdetermination by empirical evidence. She advocates for additional behavioral constraints on theorizing, known as 'signature testing,' while emphasizing the need to incorporate neuroscience and biology more substantially into animal cognition research. Her work on major transitions in cognitive evolution proposes treating the evolution of cognition as a series of major evolutionary transitions to better comprehend cognitive complexity across species. Analysis of Halina's recent publications reveals a clear trajectory toward computational comparative cognition. Her work increasingly integrates AI and machine learning techniques with traditional comparative cognition approaches, exemplified by her development of the Animal-AI Testbed. This platform allows for direct comparison between AI systems, humans, and animals on cognitive tasks, revealing that while AI and children perform similarly on basic navigational tasks, children outperform AI on more complex cognitive tests requiring object permanence. Her research demonstrates how computational modeling can generate novel hypotheses about animal behavior that generate precise, testable predictions beyond what traditional experimental methods alone can achieve. McDonnell Postdoctoral Fellowship Halina directs research initiatives at the Leverhulme Centre for the Future of Intelligence, particularly focusing on the intersection of AI and animal cognition. Her work on the Animal-AI Environment has received significant funding and collaborative support, enabling interdisciplinary research that bridges computer science, cognitive science, and biology. She actively collaborates with researchers across multiple institutions to develop computational frameworks for understanding nonhuman animal cognition. Halina leads significant research initiatives through the Leverhulme Centre for the Future of Intelligence, where she develops the Animal-AI Environment—a research platform for conducting cognitive experiments with artificial agents, humans, and nonhuman animals in directly comparable, ecologically valid contexts. This environment facilitates interdisciplinary collaboration between computer scientists, engineers, biologists, and cognitive scientists, reducing the 'language barrier' between these fields and enabling cross-pollination of ideas and methodologies.
Sanat K. Sarkar serves as a Professor in the Department of Statistics, Operations, and Data Science at Temple University's Fox School of Business and Management. An internationally renowned expert, he has pioneered foundational work in multiple testing theory with applications spanning genomics, neuroimaging, and high-dimensional data analysis. His methodological innovations address critical challenges in false discovery rate control under complex dependency structures. Research Interests: Dr. Sarkar specializes in Multiple Testing, Statistical Methodologies, High-Dimensional Statistical Inference, and Multivariate Statistics. His work develops rigorous frameworks for hypothesis testing in modern scientific contexts where thousands of simultaneous tests are performed, ensuring reliable discoveries in fields like genetic association studies and brain connectivity mapping. Key contributions include adaptive FDR procedures and methods for structured hypothesis groups. Publication Trends: Over 2020-2025, his 11 publications demonstrate sustained leadership in refining false discovery rate methodologies. Recent work tackles correlated data (2025), knockoff variable selection (2022), and hierarchical hypothesis structures (2021-2024), reflecting his focus on real-world applicability in biomedical big data. His research bridges theoretical statistics with practical computational solutions. Honors and Awards: Fellow, Institute of Mathematical Statistics Fellow, American Statistical Association Elected Member, International Statistical Institute Musser Award for Research Excellence (Fox School) Multiple Dean's Research Honor Roll Inductions Research Support and Service: Funded continuously by NSF and NSA grants, Dr. Sarkar co-organized the NSF-CBMS conference on Multiple Comparisons and serves on editorial boards of Annals of Statistics , American Statistician , and Sankhya . He regularly delivers invited talks at international venues and mentors junior researchers in statistical methodology development.
Yinqiu He is an Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison. They hold affiliations with the School of Computer, Data & Information Sciences and the Data Science Institute at Columbia University (2021-2022 postdoc). Their research focuses on developing statistical methodologies for high-dimensional and complex data, with applications in genomics, metabolomics, and network analysis. Key areas include mediation pathway analysis, asymptotic theory for U-statistics, and functional connectivity modeling. Education includes a B.S. in Statistics from the University of Science and Technology of China (2016) and a Ph.D. in Statistics from the University of Michigan-Ann Arbor (2021), advised by Professors Gongjun Xu and Xuming He. They were awarded the ProQuest Distinguished Dissertation Award (2022) and received multiple travel grants from the Institute of Mathematical Statistics and ASA. Teaching includes core Ph.D. courses like STAT 849 (Regression Analysis) and applied courses like STAT 456 (Multivariate Statistics). Current mentoring includes MS student Yuhan Zheng (now pursuing UW-Madison Ph.D.) and Xiangyi Liao (Ph.D. in Educational Psychology). Research outputs include foundational work on adaptive U-statistics testing frameworks and scalable methods for large-scale genomic data analysis. Active in methodological contributions to biostatistics, their work bridges statistical theory and computational efficiency. Recent projects involve dynamic functional connectivity estimation from fMRI data and latent space modeling in heterogeneous networks. GitHub repository 'Adaptive-U-stats' hosts open-source implementations of high-dimensional testing algorithms developed in their research.
Elsie H. Shogren is an Assistant Professor in the Department of Biology at Wake Forest University. She earned her B.S. from Cornell University, Ph.D. from Kansas State University, and completed an NSF Postdoctoral Fellowship at the University of Rochester. Her research centers on understanding how environmental, behavioral, and genetic variation influences evolution and speciation in birds, with a focus on drivers of divergence and consequences of hybridization in closely related taxa. Using field observations, experiments, and genomic analyses, her research group tests hypotheses at multiple biological levels to determine factors defining species boundaries and evolutionary trajectories.
Daniel Baum is a Research Professor and Head of the Visual Data Analysis research group at the Zuse Institute Berlin (ZIB), which is affiliated with Freie Universität Berlin. His work spans across scientific visualization, computational biology, and image analysis, with a particular focus on developing methods for analyzing complex biological structures and neural circuits. He is actively involved in multiple interdisciplinary research projects including HFSP Chitons, Geometric Learning for Single-Cell RNA Velocity Modeling, and RobustCircuit. Dr. Baum's research interests center on visual and data-centric computing approaches to solve complex problems in biology and medicine. His work bridges the gap between computational methods and biological applications, with significant contributions to cryo-electron tomography analysis, neural circuit mapping, and geometric morphometrics. He develops innovative algorithms for 3D reconstruction, image segmentation, and visualization of biological structures, from molecular to organismal scales. His publication record demonstrates consistent contributions to visualization techniques applied to biological problems, with recent work focusing on neural circuit analysis in zebrafish and Drosophila, biomechanical studies of animal structures, and advanced methods for analyzing ancient artifacts. The research shows a clear trajectory toward increasingly sophisticated multimodal data integration and machine learning approaches. Dr. Baum leads a productive research group with several key collaborators who frequently appear as co-authors on his publications, indicating a strong mentoring relationship. His projects involve substantial funding from various sources supporting interdisciplinary collaborations across biology, computer science, and engineering. His laboratory at ZIB focuses on visual data analysis for complex biological systems, with particular strength in developing computational methods for neuroscience applications and biomaterial analysis. The group maintains strong collaborations with multiple institutions working on cutting-edge imaging technologies and biological model systems.
University of California, Los AngelesUnited States
Daniel M.T. Fessler is a Professor of Anthropology at the University of California, Los Angeles (UCLA), where he is affiliated with the Department of Anthropology in the College of Letters and Science. He serves as the Director of the UCLA Bedari Kindness Institute and is a core member of the UCLA Center for Behavior, Evolution and Culture (BEC). His research integrates anthropological, psychological, and biological perspectives to understand human behavior from an evolutionary standpoint. Fessler's educational background includes a Ph.D. from the University of California San Diego (1995). His academic journey has positioned him as a leading figure in evolutionary anthropology, with significant contributions to understanding how evolutionary processes shape human behavior, cognition, and social organization. Fessler's research spans multiple domains of human behavior and cognition, with particular emphasis on the evolutionary foundations of emotions, morality, and social behavior. His work investigates how evolved psychological mechanisms interact with cultural systems to produce the rich diversity of human experience. Key areas of focus include disease avoidance behaviors, prosociality and cooperation, aggression and risk-taking, cultural transmission processes, food-related behaviors, and reproductive strategies. His approach is characterized by methodological pluralism, incorporating experimental, ethnographic, and comparative approaches to test evolutionary hypotheses about human nature. His publication record demonstrates consistent productivity across evolutionary psychology, biological anthropology, and evolutionary medicine. His work often appears in high-impact interdisciplinary journals, reflecting the breadth of his research interests and the relevance of evolutionary perspectives to multiple domains of human inquiry. Fessler's research program has evolved to address increasingly complex questions about the interplay between evolved psychological mechanisms and cultural systems. Live fast, die young, and sleep later: Life history strategy and human sleep behavior (2021) Elevation, an emotion for prosocial contagion (2019) An evolutionary account of vigilance in grief (2018) Ectoparasite defence in humans (2018) Political orientation predicts credulity regarding putative hazards (2017) Fessler has established significant collaborations with researchers across multiple institutions including Clark Barrett, Rob Boyd, Greg Bryant, Martie Haselton, Joe Henrich, Colin Holbrook, Rob Kurzban, Joan Silk, and Stephen Stich. His work bridges disciplinary boundaries, connecting evolutionary theory with practical applications in health, medicine, and social policy. As an advisor, Fessler currently mentors graduate student Theo Samore, whose research focuses on political psychology, traditionalism, and evolutionary approaches to emotion and medicine. His FessLab provides undergraduate students with hands-on research experience in systematic experimental social science, serving as an apprenticeship model where students progressively take on greater research responsibilities. Many FessLab participants have earned co-authorship on publications through their significant contributions to research projects. Fessler leads the FessLab research group, which has operated continuously since at least 2011. The lab provides undergraduate students with hands-on experience in experimental social science research through an apprenticeship model where students begin with simple tasks and gain additional responsibilities as they demonstrate skills and potential. The lab environment emphasizes collaborative research and has produced numerous publications with student co-authors. FessLab has developed a strong community identity, as evidenced by photos of lab members spanning multiple years from 2011 through 2018.
Edsel Peña is a Professor and Chair of the Department of Statistics at the University of South Carolina, within the McCausland College of Arts and Sciences. He holds a BS (Magna Cum Laude) from the University of the Philippines Los Baños and a PhD from Florida State University. His research focuses on Mathematical Statistics, Survival Analysis, Reliability, and Biostatistics. Peña has held roles including visiting professorships at the University of Michigan and served as Executive Secretary of the Institute of Mathematical Statistics (2017–2023) and NSF Program Director (2020–2023). Education: Bachelor of Science in Statistics, University of the Philippines Los Baños (1979) PhD in Statistics, Florida State University (1986) Research Interests: Peña’s work spans foundational statistical theory, survival analysis applications, and high-dimensional inference. He emphasizes methodological development in reliability systems and nonparametric approaches for recurrent events. His recent work includes optimal correlation-based prediction and asymptotic theory for recurrent event models. Awards: Fellow, American Statistical Association Member, International Statistical Institute Fellow, Institute of Mathematical Statistics Michael Mungo Graduate Teaching Award (University of South Carolina) Severino and Paz Koh Lectureship Award Grants & Leadership: Secured funding from NSF, NIH, and EPA. Served on editorial boards of top journals including the Journal of the American Statistical Association and Scandinavian Journal of Statistics. Currently chairs the Department of Statistics at USC.
Gongjun Xu is an Associate Professor of Statistics and courtesy Associate Professor of Psychology at the University of Michigan. He leads the Master's Program in Applied Statistics and holds positions in the Department of Statistics within the College of Literature, Science, and the Arts (LSA). His research focuses on latent variable models, psychometrics, statistical learning, and high-dimensional statistics. Xu completed his Ph.D. in Statistics at Columbia University and B.S. at the University of Science and Technology of China (USTC). His research interests include advanced statistical methodologies for educational and psychological measurement, such as cognitive diagnosis models, item response theory, and network data analysis. He has contributed to high-dimensional inference, survival analysis, and machine learning applications in healthcare. Xu serves as Co-Editor-in-Chief of the Journal of Educational and Behavioral Statistics and holds editorial roles in multiple top-tier journals. Xu has been recognized with prestigious awards including the AERA Research Methodology Award (2025), ICSA President’s Citation (2024), and COPSS Emerging Leader Award (2023). His team advises numerous graduate and undergraduate students, many of whom pursue academic and industry roles in statistics, data science, and education. Key collaborations include work on educational testing, health analytics, and AI-driven scientific methods via the Schmidt AI in Science Fellowship. He oversees a vibrant research group with active projects in latent variable modeling, differential item functioning detection, and scalable Bayesian methods for large-scale assessments. His lab emphasizes interdisciplinary applications spanning education, healthcare, and social sciences.
Professor Brenda Gannon is an international interdisciplinary leader in health economics and social care systems at the University of Queensland. She holds a primary appointment as Professor in the School of Economics within the Faculty of Business, Economics and Law, and is also an Affiliate Professor at the Mater Research Institute and affiliate of multiple health research centers including the Queensland Digital Health Centre and the Centre for Hearing Research. Her work bridges economics, medicine, and social science to develop evidence-based solutions for health systems globally. Professor Gannon's research spans several interconnected domains: health economics of aging and dementia care, mental health services, health equity analysis, and economic evaluations of healthcare interventions across the lifespan from newborns to older adults. Her work particularly focuses on cognitive impairment, physical activity investments, consumer-directed care models, and the social determinants of health. She employs sophisticated econometric methods to analyze big data and test causal hypotheses in healthcare utilization and outcomes. Her 15 most recent publications (2022-2025) demonstrate a strong focus on health equity, cost-effectiveness analyses, and interventions across diverse populations. Her research spans pediatric critical care, women's reproductive health, dementia care, heart failure management, and perinatal mental health. A notable pattern is her application of health economics principles to evaluate interventions across the lifespan, with particular emphasis on vulnerable populations and health disparities. Fellow of the Queensland Academy of Arts and Sciences (2023) Council Member of Queensland Academy of Arts and Sciences (since 2024) Award-winning HDR supervisor with successful mentorship of students into academia and industry Professor Gannon has supervised multiple PhD students as principal and associate advisor, with completed theses focusing on applied health economics, aging, and microeconometrics. Her current research is supported by significant grants from the Australian Research Council, National Health and Medical Research Council, Medical Research Future Fund, EU Horizon 2020, and other international funding bodies. She serves on the International Health Economics Association Student Prize Committee (2025-28) and has advised government at senior levels, including serving on the Medical Services Advisory Committee Evaluation Sub-Committee (2017-2021). As Health Economics and Epidemiology lead for the Queensland Family Cohort Study and member of the QFC Governance Committee, she contributes to major population health initiatives. Her work with clinicians on trials for newborns and interventions for dementia care demonstrates her commitment to translational research that bridges academic findings with practical healthcare applications.
Dr. Beth A. Sanders serves as Professor and Chair of the Department of Human Services within Bowling Green State University's College of Health & Human Services. She joined BGSU in 2018 after prior academic appointments at Kent State University and public health experience at Cincinnati Children's Hospital. Her leadership oversees the Criminal Justice program, Police Integrity Research Group, and departmental operations from the Health and Human Services Building. Her educational foundation includes a Ph.D. from the University of Cincinnati. Prior professional roles encompass: Academic position at Kent State University Public health research at Cincinnati Children's Hospital Child Health Statistics Center Dr. Sanders' research centers on police officer selection methodologies and performance evaluation systems , critically examining organizational constraints in identifying quality officers. Her work on public opinion toward the death penalty explores gender disparities and sociological determinants. Through extensive consulting with police departments on selection protocols, performance metrics, and community relations, she bridges academic theory with practical law enforcement challenges. Her program evaluations for adult probation and juvenile treatment systems demonstrate applied criminological expertise. Analysis of her 25+ publications reveals sustained focus on policing integrity across three decades, with recent work expanding into social policy impacts on traffic safety. Her scholarship consistently employs rigorous quantitative methods in journals like Policing: An International Journal , Journal of Criminal Justice Education , and Deviant Behavior , addressing core tensions between organizational realities and ideal policing standards. As Department Chair and Professor, Dr. Sanders mentors students in Criminal Justice programs while leading the Police Integrity Research Group. Her consulting engagements with multiple police departments indicate active knowledge transfer between academia and law enforcement practice, though specific grant details remain unreported in available materials. The department's operational framework includes: Police Integrity Research Group examining ethical policing challenges Criminal Justice Career Fair facilitating student-practitioner connections Interprofessional Education initiatives across health disciplines
Karel A. Kroeze is a Researcher at the Behavioural Data Science Institute (BDSI) at the University of Twente, specializing in Instructional Technology. With an h-index of 52, he has established himself as a significant contributor in the fields of adaptive learning systems, educational data mining, and psychometrics. His work bridges computer science, statistics, and educational theory to develop innovative assessment and feedback mechanisms. His educational background includes a Master's degree in Methodology and Statistics for the Behavioural, Biomedical and Social Sciences from Utrecht University (2015) and a Bachelor's degree in European Studies from the University of Twente (2013). This interdisciplinary foundation supports his current research in complex data analysis for educational applications. Kroeze's research focuses on developing adaptive systems for learning environments, particularly in inquiry-based education. His work on automated hypothesis assessment, concept mapping, and computerized adaptive testing demonstrates his commitment to improving educational outcomes through data-driven approaches. He explores how adaptive scaffolds, learner models, and automated feedback can enhance the quality of student inquiry and hypothesis formation in science education. His publication record shows consistent output from 2014 through 2024, with recent work expanding into health economic modeling validation and electoral system analysis. This demonstrates both depth in his core educational technology domain and breadth across applied statistical methods. As evidenced by his numerous datasets on GitHub related to adaptive hypothesis grammars across multiple domains (Electrical Circuits, Supply and Demand, Buoyancy, Photosynthesis, and Heat transfer), Kroeze develops practical tools that parse and assess student hypotheses in various scientific contexts. His research contributes to several UN Sustainable Development Goals, particularly in the area of quality education.
Le-Yu Chen is a Research Fellow at the Institute of Economics, Academia Sinica . His research focuses on advanced econometric methods and their applications to complex economic problems. Research Interests: Microeconometrics, Econometric Theory, Applied Econometrics, Statistics Email: lychen@econ.sinica.edu.tw Chen has developed innovative methods in quantile regression, treatment effect estimation, and discrete choice modeling. His work combines theoretical rigor with computational implementation using tools like Gurobi optimization solver. Recent publications examine: Local conditional tail treatment effects Sparsity in quantile regression Dynamic programming models High-dimensional moment inequalities Nonparametric inference techniques
