Ying MacNab is an Associate Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver Campus. She holds an additional affiliation as an Associate Member in the School of Population and Public Health (SPPH). Her research focuses on Bayesian hierarchical modeling, spatial epidemiology, and disease mapping with applications to public health surveillance and aging populations. She has contributed extensively to methodological advancements in Gaussian Markov random fields and spatiotemporal modeling frameworks. Her work bridges statistical theory and practical health challenges, including pandemic-related stress in older adults, opioid treatment outcomes, and infectious disease forecasting. MacNab has collaborated on projects involving mental health assessments (e.g., sleep dysfunction, anxiety/depression in iOAT patients) and has developed novel statistical tools for analyzing spatially and temporally correlated health data. Her research also addresses methodological gaps in coregionalized multivariate models and constrained Bayesian estimation. MacNab's publications reflect a multidisciplinary approach, integrating epidemiological theory with advanced computational methods. Recent trends in her work emphasize dynamic modeling of infection risks, mediation analysis in aging populations, and validation of psychometric scales for health-related stress. She has maintained an active research agenda since the early 2000s, with notable contributions to neonatal health outcomes, injury surveillance, and healthcare quality improvement.
Sherry Tongshuang Wu is an Assistant Professor at Carnegie Mellon University's School of Computer Science, with primary appointments in the Human-Computer Interaction Institute (HCII) and secondary affiliation with the Language Technology Institute (LTI) . Trained at the University of Washington under Jeffrey Heer and Dan Weld, she bridges HCI and NLP to study human interactions with AI systems across diverse user groups. Her educational background includes a Ph.D. (2016-22) and M.S. (2016-18) in Computer Science and Engineering from the University of Washington, and a B.Eng. (2012-16) from Hong Kong University of Science and Technology. Industry experience includes research internships at Google Brain, Microsoft Research, and Apple. Wu's research focuses on three interconnected pillars: Real-world AI Evaluation (developing frameworks like SPHERE for systematic assessment), Task-specific AI Test & Distill (optimizing general-purpose models for specific use cases), and Human-AI Task Delegation (designing optimal collaboration between humans and AI). Her work emphasizes practical deployment, user-specific net gains, and error recovery mechanisms. Analysis of her 15 most recent publications reveals a strong trend toward evaluation frameworks (35%), human-AI collaboration systems (30%), and specialized model distillation (25%), with growing emphasis on educational applications (10%). Key methodological themes include checklist-based evaluation, perspective-aware retrieval, and structural analysis of AI outputs. Google Academic Research Award (2024) Amazon Research Awards (2024) AIED 2024 Best Paper Award ACL 2020 Best Paper Award Rising Stars in EECS Workshop (2020) Wu actively mentors 19 students across PhD, Master's, and undergraduate levels, with notable projects including synthetic data generation, LLM literacy tools, and retrieval system optimization. She leads significant grant-funded work through Amazon Research Awards and Google Academic Research Awards, focusing on deployable model generation and human-AI collaboration frameworks. Her lab develops practical tools like Promp2Model and SPHERE that bridge theoretical research with industry applications.
Andrew Lan is an Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, where he also serves as the CS Undergraduate Program Director. He was granted tenure by the UMass Board of Trustees in June 2025 and is currently on leave through Spring 2026. His research focuses on developing human-in-the-loop machine learning methods to enable scalable, effective, and personalized learning experiences in education. Dr. Lan received his BS in Physics and Mathematics from the Hong Kong University of Science and Technology, followed by his MS (2014) and PhD (2016) in Electrical and Computer Engineering from Rice University. He completed postdoctoral research at Rice University (2016) and Princeton University's EDGE Lab (2017-2018). His research spans artificial intelligence for education, with particular expertise in educational data mining, knowledge tracing, personalized learning systems, and human-AI collaboration in educational contexts. Dr. Lan's work leverages massive and multimodal learner and content data collected from both traditional classrooms and online learning platforms to develop systems that deliver high-quality, affordable, and personalized learning experiences. He has made significant contributions to areas including computerized adaptive testing, math word problem generation, student affect detection, and automated grading systems. His recent work increasingly focuses on leveraging large language models for educational applications while maintaining rigorous scientific validation of these approaches. Best Student Paper Award at the 2024 AIED Conference (with Alexander Scarlatos) Best Paper Nominee at LAK 2021 Best Student Paper Award at IEEE Big Data 2020 NAEP Math Automated Scoring Challenge Grand Prize Winner Dr. Lan actively mentors graduate students and postdoctoral researchers, with several of his advisees receiving recognition for their work. He has secured substantial funding from the National Science Foundation, including a $90M grant for the SafeInsights project, a secure cyberinfrastructure for educational research. His research group collaborates with institutions including Worcester Polytechnic Institute, University of Pennsylvania, and Rice University. He teaches undergraduate and graduate courses including COMPSCI 240 (Reasoning under Uncertainty) and COMPSCI 590OP (Applied Numerical Optimization), with a focus on the practical application of theoretical concepts in machine learning and artificial intelligence. His educational philosophy emphasizes bridging the gap between theoretical foundations and real-world implementation in educational technology.
Dr. Han Du is an Associate Professor in the Department of Psychology at the University of California, Los Angeles (UCLA). He holds a PhD from the University of Notre Dame and leads the Du Research Lab. His methodological expertise includes Bayesian statistics, longitudinal data analysis, structural equation modeling, meta-analysis techniques, and machine learning applications in psychological research. Dr. Du's substantive research applies quantitative methods to developmental, clinical, cognitive, educational, and health psychology. His recent publications focus on transgender adolescent stress assessment, LGBTQ+ mental health in military contexts, social network interventions for HIV prevention, and minority stress theory applications. He teaches advanced statistical methods and supervises graduate students in quantitative psychology.
Tatjana Schnell is a Professor of Existential Psychology at MF Norwegian School of Theology, Religion and Society, where she has worked since October 2020. She also holds a fellowship at the Humanistic University Berlin (since 2024). Her professional journey includes academic training in psychology, theology, religious studies, and philosophy across Germany, the UK, and Austria, with a doctorate from the University of Trier (Germany) and habilitation from the University of Innsbruck (Austria), where she founded the Existential Psychology Lab. Education : Psychology, Theology, Religious Studies, and Philosophy from the University of Göttingen, University of London, University of Heidelberg, and University of Cambridge Degree : Doctorate in Psychology (University of Trier, Germany) Habilitation : Psychology (University of Innsbruck, Austria) Tatjana Schnell's research focuses on existential themes including meaning in life, existential health, death attitudes, alienation, and religious/spiritual/secular worldviews. Her work bridges individual psychology with societal and environmental implications, with extensive international collaborations and publications. She has authored key works like "The Psychology of Meaning in Life" (2nd ed., 2025) and "Sinn finden" (2024). Her recent publications examine pandemic psychology, existential resources in gifted adults, workplace spirituality, and meaning-based interventions for chronic pain and cancer rehabilitation. Her research projects include "Making Sense of Volunteering," "Sinnmacher - The Meaning App," and studies on existential resources in palliative care. The Tatjana Schnell website provides further details about her work. Fellow : Humanistic University Berlin (2024) Tatjana Schnell has developed innovative tools like the Sources of Meaning Card Method (SoMeCaM) and Meaning in Work Inventory (ME-Work), with validated versions across multiple cultures. Her work emphasizes the practical application of existential psychology in mental health, organizational behavior, and ecological engagement. She founded the Existential Psychology Lab at the University of Innsbruck and continues to lead impactful research at MF Norwegian School of Theology, Religion and Society.
Cornelia Betsch serves as Director of the Institute for Planetary Health Behaviour (IPB) at the University of Erfurt, where she holds a professorship in Health Communication within the Faculty of Philosophy. She is responsible for the Master's program in Health Communication through the Department of Media and Communication Studies and leads the Health Communication Working Group at the Bernhard Nocht Institute for Tropical Medicine in Hamburg as an external position. Her interdisciplinary work bridges behavioral science, psychology, and public health to address critical global challenges. Habilitation (2006), University of Erfurt: 'The role of risk perception and risk communication in prevention decisions – the example of vaccination decisions' PhD (Dr. phil., summa cum laude, 2006), University of Heidelberg: 'Preference for intuition and deliberation– measurement and consequences of affect- and cognition based decision making' Diplom in Psychology (2002), University of Heidelberg Betsch's research focuses on understanding health and planetary health behaviors, particularly examining vaccination behavior, prudent antibiotic use, and climate-friendly actions. Her work extends globally, with investigations in various African countries on vaccination and antibiotic practices. She pioneered the influential COVID-19 Snapshot Monitoring (COSMO) and subsequently developed the Planetary Health Action Survey (PACE), large-scale studies that regularly track public knowledge, risk perception, protective behaviors, and trust during crises. Her research emphasizes applying behavioral and cultural insights to design effective policy frameworks and explanatory communication that promotes positive health behaviors. The 15 most recent articles showcase Betsch's research evolution toward integrating behavioral science with planetary health, climate action, and vaccine communication. Her work increasingly examines the psychological foundations of climate behavior while maintaining her established expertise in vaccine hesitancy and antimicrobial resistance. Recent publications demonstrate methodological diversity, including systematic reviews, meta-analyses, survey experiments, and large-scale monitoring datasets that bridge academic research with practical policy applications. German Psychology Prize (2021) Thuringian Research Prize (2022) Betsch has secured research funding from independent research organizations, ministries, and foundations to support her work on health communication and planetary health behavior. She serves as a scientific advisor to multiple organizations including the WHO Technical Advisory Group on Behavioral and Cultural Insights, Science Media Center Germany, and Museum für Naturkunde Berlin. At the Bernhard Nocht Institute for Tropical Medicine, she established the WHO Collaborating Center for Behavioral Research in Global Health, demonstrating her commitment to translating research into global health practice. Her engagement extends to policy advising, having served on the Corona Expert Council of the Federal Chancellery during the pandemic. As Director of the Institute for Planetary Health Behaviour, Betsch leads an interdisciplinary team applying social and behavioral science perspectives to planetary health and the climate crisis. The institute serves as a hub for research, education, and science communication at the intersection of human health and environmental sustainability. Her work through the IPB emphasizes Open Science principles and aims to understand the comprehensive factors influencing climate-friendly behavior to identify effective intervention points for policy development.
Barbara Plank is a full professor and chair for AI and Computational Linguistics at Ludwig Maximilian University of Munich (LMU), where she heads the Munich AI and NLP (MaiNLP) lab and co-directs the Center for Information and Language Processing (CIS). She additionally serves as a visiting full professor at the IT University of Copenhagen, maintaining active dual institutional affiliations in computational linguistics and NLP research. Her research focuses on human-centric natural language processing challenges, particularly learning under sample selection bias (domain adaptation, transfer learning) and annotation bias, learning with limited data through continual/semi-supervised/weakly-supervised methods, multimodal learning at language-vision-speech interfaces, and fortuitous supervision for variety-space aware language understanding. She pioneers methodologies addressing human label variation as a critical factor in model robustness rather than mere noise. Recent publications (2024-2025) reveal dominant trends in modeling human label variation across NLP tasks, especially natural language inference and entity recognition, alongside dialectal language processing and LLM evaluation frameworks. Her work systematically investigates how human disagreement in annotations can be leveraged to build more robust, adaptable systems rather than treated as errors. Scientific recognition includes: ERC Consolidator Grant for the DIALECT project advancing natural language understanding for non-standard languages and dialects ACL 2024 Area Chair Award for the paper 'VariErr NLI: Separating Annotation Error from Human Label Variation' Leading the MaiNLP lab at CIS (LMU), she directs research integrated with MCML (Munich Center for Machine Learning), Munich Intelligent Robotics, ELLIS Unit Munich, UniDive, and COST action. Current projects include ERC-funded DIALECT and KLIMA-MEMES, focusing on human-facing NLP solutions for real-world language diversity challenges. She actively shapes the field through ACL leadership as VP-Elect and numerous keynotes emphasizing human-centric approaches. The MaiNLP lab at Akademiestr. 7, 80799 Munich, drives innovation in computational linguistics through interdisciplinary collaboration, maintaining strong ties with European research networks while developing practical applications for language variation and robust NLP systems. The lab's work directly informs her teaching in LMU's Computational Linguistics programs, bridging research and education in cutting-edge NLP methodologies.
Mikkel N. Schmidt is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on statistical modeling, Bayesian methods, and their applications in science and industry. He has held visiting roles at Columbia University (2007) and Cambridge University (2008-2009). His work integrates probabilistic modeling with computational inference to address complex problems in diverse fields such as molecular discovery, optical communication, and brain connectivity analysis. Education highlights include visiting scholar and postdoctoral experiences at top-tier institutions. Research interests span statistical methodology development, machine learning applications, and interdisciplinary problem-solving. Current projects involve Bayesian neural networks for molecular discovery and federated learning optimization. Advising efforts include supervising multiple PhD students in areas like molecular discovery and denoising diffusion models. Notable collaborations involve work on materials science, quantum communication, and medical signal processing. His contributions bridge theoretical advancements with practical industrial applications, emphasizing interdisciplinary innovation.
Dr. Jason Rights is an Associate Professor in the Department of Psychology within the Faculty of Arts at the University of British Columbia. His office is located in Kenny Room 2017 at 2136 West Mall, Vancouver, BC. He leads The Rights Lab, a quantitative methods research group dedicated to improving statistical practice in scientific research. His educational background includes: B.S. in Psychology and Mathematics from the University of North Carolina at Chapel Hill (2011) M.S. in Psychology (Quantitative Methods) from Vanderbilt University (2015) Ph.D. in Psychology (Quantitative Methods) from Vanderbilt University (2019) Dr. Rights' research focuses on addressing methodological complexities in multilevel/hierarchical data contexts where observations are nested (e.g., patients within clinicians, students within schools). His work spans several interconnected programs including developing R-squared measures for multilevel models, addressing issues with level-specific effects, exploring connections between multilevel and mixture models, and advancing latent variable model selection techniques. Analysis of his publication record reveals a consistent focus on methodological innovations in quantitative psychology, with particular emphasis on improving statistical techniques for hierarchical data structures. His work bridges theoretical statistical development with practical applications across psychology and related fields. Dr. Rights actively develops open-source software in R to implement his methodological contributions, making advanced statistical techniques accessible to researchers. The Rights Lab serves as the hub for his ongoing research program in quantitative methods development.
Dr. Mark Thompson is a Senior Lecturer in Psychology and Researcher of Sport Psychology at London Metropolitan University's School of Social Sciences and Professions. He holds a PhD from the University of Hull and is a Fellow of the Higher Education Academy (FHEA). His academic work bridges sport psychology, healthcare rehabilitation, and youth athlete development. Dr. Thompson's education includes a PhD in Psychology from the University of Hull. His research focuses on emotional processes in elite athletes, doping propensity in youth sports, and post-COVID-19 patient rehabilitation. Notable projects include NHS-funded studies on telerehabilitation and collaborations with the International Olympic Committee and World Anti-Doping Agency. Research Interests: Psychophysiological responses to stress in sports Emotional regulation strategies among athletes Anti-doping education and youth athlete behavior Telehealth applications in post-hospitalization recovery His publications emphasize qualitative methodologies, exploring topics like athlete performance under stress, doping prevention programs, and healthcare professional perspectives on self-management approaches. Awards: Fellow of the Higher Education Academy (FHEA). Dr. Thompson has delivered presentations at major conferences such as the American College of Sports Medicine and contributed to media discussions on doping in elite sport via LoveSport Radio. His teaching spans foundational psychology to advanced modules like cognition and behavior, often serving as module leader.
Jesse Cougle is a Professor of Psychology at Florida State University, focusing on anxiety disorders , obsessive-compulsive and related disorders (OCRD), and problematic anger . He leads the Cougle Lab, which develops computerized treatments and investigates distress intolerance , courage , and transdiagnostic factors . Education: University of Texas at Austin (2008), Oxford University (postgraduate) Research: Cognitive-behavioral mechanisms in anxiety disorders, technology-based interventions for social anxiety and BDD, and biological processes in problematic anger Students: Mentoring Tapan Patel, James Zech, Victoria Swaine, and others His work spans interpretation bias modification , safety behavior reduction , and 'not just right' experiences in OCRD. Recent publications emphasize digital interventions for mental health and distress tolerance as a key transdiagnostic factor. Scientific Awards: President’s New Researcher Award (ABCT) Graduate Faculty Mentor Award (FSU) He serves as Editor-in-Chief of the Journal of Obsessive-Compulsive and Related Disorders and holds editorial roles in major journals. The Cougle Lab explores anxiety , perfectionism , and appearance-related psychopathology through multimodal research .
Li Cai is a Professor and Director at the National Center for Research on Evaluation, Standards, and Student Testing (CRESST) within the Graduate School of Education and Information Studies at the University of California, Los Angeles (UCLA). His work focuses on quantitative methods in education, particularly psychometrics and statistical modeling. Ph.D. in Quantitative Psychology from the University of North Carolina – Chapel Hill Research and teaching interests center on psychometrics, latent variable models, item response theory, nonlinear mixed models, and statistical computation. His methodological work addresses advanced techniques for educational assessment and model evaluation. His representative publications include studies on covariance structure models, item response theory, bifactor analysis, and goodness-of-fit testing. These works often emphasize computational algorithms and practical applications in educational measurement. Li Cai is affiliated with CRESST at UCLA, a leading center dedicated to rigorous research, assessment design, and evaluation methodology across diverse educational contexts.
Peter F. Halpin is an Associate Professor in the Department of Learning Sciences and Psychological Studies at the University of North Carolina at Chapel Hill School of Education. He holds a PhD in Psychology (Theory and Methods) from Simon Fraser University and completed postdoctoral research at the University of Amsterdam. His research focuses on psychometric methodology, educational measurement, and statistical approaches to analyzing collaborative learning and teacher practices. Halpin has been recognized with awards including the National Academy of Education/Spencer Fellowship and NYU's High Merit Distinction in Research. Key research areas include developing statistical models for small group collaborations, analyzing educational technology data, and improving measurement tools for early childhood development (e.g., IDELA assessments). His work bridges theoretical psychometrics with applied educational research, addressing challenges in global education measurement and program evaluation. Halpin has authored over 20 peer-reviewed articles and contributed to open-source software projects like the scirt and hawkes R packages. He has advised numerous graduate students and led grants totaling over $2 million, including IES-funded studies on collaboration assessment and UNESCO-linked projects measuring educational outcomes in low-resource settings. Halpin also serves on editorial boards for journals like Psychometrika and Journal of Educational Measurement , and has presented globally at venues including the Psychometric Society and NCME conferences.
Affiliations & Roles Professor of Computer and Information Science at University of Pennsylvania Faculty in Graduate Groups: Bioengineering (School of Engineering) Genomics & Computational Biology (School of Medicine) Operations, Information & Decisions (Wharton School) Psychology (School of Arts & Sciences) Research Affiliations: Annenberg Public Policy Center (Distinguished Fellow) Center for Cognitive Neuroscience Institute for Translational Medicine Research Interests Focuses on explainable AI, natural language processing (NLP), and machine learning applications in psychology and medicine. Key areas include: Language analysis for well-being and mental health Spectral methods for NLP (e.g., Eigenwords) Forecasting and decision-making models Bioinformatics and genomics Teaching Teaches advanced courses in Machine Learning, Deep Learning, and AI ethics, including: CIS 5200: Machine Learning CIS 5220: Deep Learning CIS 6200: Advanced Topics in Deep Learning Key Collaborations Works with interdisciplinary teams on projects like the Good Judgment Project (forecasting) and WWBP (Well-Being and Language). Collaborators include Martin Seligman (positive psychology), Dean Foster (statistics), and Michael Collins (NLP).
Laurie Ford is an Associate Professor at the University of British Columbia , affiliated with the Faculty of Education and the Department of Educational and Counselling Psychology, and Special Education . She serves as the SACP Admissions Coordinator , Director of Training , and Director of Early Childhood Education . Her work bridges Child and Adolescent Development with Family-School-Community Partnerships , emphasizing culturally responsive practices. Dr. Ford’s research focuses on Early Childhood Assessment , Multi-disciplinary Teams , and Supporting Vulnerable Populations , including immigrants, refugees, and children with chronic health conditions like IBD. She leads the Children, Families, and Communities Lab , where students explore topics such as Cognitive Assessment , Family Engagement , and Community-level Child Development . Her recent publications analyze psychometric tools in diverse populations, assessment feedback communication, and community factors influencing child development. She has presented nationally and internationally on topics like Indigenous Family Engagement and School Belonging for Refugee Students . Dr. Ford has supervised over 75 graduate theses and collaborates with global institutions, including the University of Melbourne on the KIDS in Communities Study (KICS) . Current projects examine school belonging for immigrant/refugee families , supporting children with inflammatory bowel disease , and cross-national assessment norms . She advocates for collaborative approaches to address Early Childhood Mental Health and Multi-cultural Considerations in educational environments.
