Dingjing Shi is an Assistant Professor in Psychology at Georgia Institute of Technology, specializing in quantitative methods. Her research integrates Bayesian statistics with psychological measurement, focusing on longitudinal data analysis, network modeling, and ecological momentary assessment using wearable technology. She employs advanced statistical approaches to address methodological challenges in behavioral research. Her publications demonstrate expertise in developing novel psychometric approaches, including dimensionality reduction techniques for psychological networks and Bayesian generalizability theory for mixed-format assessments. Research addresses practical challenges in mobile health data collection and causal inference with incomplete data.
Frank Papenmeier is a Professor in the Department of Psychology at the University of Tübingen, within the Faculty of Science. His research focuses on event cognition, human-robot interaction, visual working memory, and visual attention. He coordinates the 'Coordination Cognitive Psychology and Research Methods' research group. His work explores how people perceive and interact with dynamic environments, including studies on event segmentation, cognitive offloading, and aesthetic judgments. He has contributed to over 100 peer-reviewed articles, with recent work addressing topics like the impact of framing on art perception and the role of AI in education. Papenmeier's research integrates experimental methods with interdisciplinary approaches, including collaborations on teleoperation systems and AI-based tutoring. He has presented at major conferences such as the European Society for Cognitive Psychology and the Psychonomic Society. His lab emphasizes methodological rigor, evidenced by contributions to replication databases and open science initiatives. Education: Not explicitly stated in the text, but his titles include Dr. rer. nat. (Doctor of Natural Sciences) and Diplom-Psychologe (Psychology Diploma). Research Interests: His primary areas include event cognition, human-robot interaction (e.g., helping behavior toward robots), visual working memory (e.g., spatial configuration processing), and cognitive offloading (e.g., impact on memory and performance). He also investigates aesthetic judgments and narrative comprehension through eye-tracking and experimental paradigms. Articles Trends: Recent work addresses applied topics like cookie consent interfaces, AI in education (e.g., R programming tutors), and perceptual effects in 3D cinema. His studies often bridge cognitive theory with real-world applications, such as usability design and social robotics. Labs/Teams: Leads the research group 'Coordination Cognitive Psychology and Research Methods' at the University of Tübingen. Collaborates with interdisciplinary teams on projects involving robotics, AI, and human-computer interaction.
Jesús Arroyo is an Assistant Professor in the Department of Statistics at Texas A&M University, specializing in statistical network analysis, machine learning, and neuroimaging applications. He holds a PhD in Statistics from the University of Michigan (2018), advised by Professor Liza Levina, and dual B.Sc. degrees in Applied Mathematics and Computer Engineering from ITAM, Mexico. Education : PhD in Statistics (University of Michigan, 2018); B.Sc. in Applied Mathematics and Computer Engineering (ITAM). Postdoctoral Experience : Center for Imaging Science at Johns Hopkins University and Department of Mathematics at University of Maryland, College Park (2018–2021). His research focuses on developing statistical methods for analyzing network data, particularly in neuroimaging and high-dimensional settings. Recent work spans multilayer community detection, anomaly detection in dynamic networks, and integrating covariates into network structure learning. He has secured NSF funding (DMS-2413553) for projects on statistical network integration and also explores causal inference and prediction in network data. Key trends in his publications include applications of network analysis to neuroscience, methodological advancements in high-dimensional statistics, and leveraging machine learning for complex network structures. Collaborators include prominent researchers like Carey E. Priebe, Joshua T. Vogelstein, and Vince Lyzinski. Jesús actively participates in academic conferences, including the International Indian Statistical Association 2025 Conference and workshops on causal inference and network learning at IMPA and other institutions. His work bridges theoretical statistics, computational methods, and real-world neuroimaging challenges.
Matthew Pase is an Associate Professor at Monash University, leading the Epidemiology of Dementia Lab, the Aging and Neurodegeneration Research Program at the School of Psychological Science, and the Aging Well Pillar at the Turner Institute. He holds an adjunct position as Associate Professor of Epidemiology at Harvard University and has conducted postdoctoral training at the Framingham Heart Study and Boston University School of Medicine. Education: PhD in Neuropsychology/Neuroscience (2014), specializing in modifiable hemodynamic predictors of cognitive aging Research focuses on modifiable risk factors for dementia, validation of non-invasive biomarkers, and sleep-dementia relationships. Notable projects include the Brain and Cognitive Health (BACH) cohort, NIH-funded Sleep and Dementia Consortium, and the Vascular Contributions to Dementia (VCD-CRE) initiative. Key themes in recent articles include sleep architecture’s impact on dementia risk, neurovascular integrity in aging, and biomarkers like YKL-40 and plasma tau. His work emphasizes translational research to reduce cognitive disorder burdens. Grants/Projects: ARC Training Centre for Optimal Ageing (2023–2028) Poor Sleep and Hypertension combined Alzheimer’s risk (2023–2025) Vascular Contributions to Dementia (2022–2027) Labs/Teams: Leads multi-institutional collaborations, including the Framingham Heart Study and International Stroke Genetics Consortium’s Cognitive Working Group.
Kangjie Zhou is a Founder's Postdoctoral Fellow in the Department of Statistics at Columbia University. He completed his PhD in Statistics at Stanford University under the advisement of Professor Andrea Montanari, with additional collaborative work with Professor Tselil Schramm. He holds a Bachelor's degree in Mathematics from Peking University's School of Mathematical Sciences. His research focuses on high-dimensional statistics, non-convex optimization, probability theory, and deep learning. Notable contributions include work on computational-to-statistical gaps, theoretical aspects of deep learning, and high-dimensional probability. He has been recognized with the 2024 Department Dissertation Award. Key research themes include analysis of tensor decompositions, optimization landscapes in neural networks, and the interplay between overparametrization and computational tractability. His work bridges statistical theory with algorithmic development, often addressing foundational questions in machine learning and data science.
Zijun Gao is an Assistant Professor at the USC Marshall School of Business. Previously, they completed a Ph.D. in Statistics at Stanford University under Prof. Trevor Hastie, and served as a Research Associate at the University of Cambridge. Their research focuses on causal inference with heterogeneity, machine learning applications in statistical problems, and methodological advancements in conditional density estimation and batched bandit problems. Key contributions include the LinCDE package for conditional density estimation and validation frameworks for heterogeneous treatment effect estimation. They received the Ric Weiland Graduate Fellowship in 2020. Major research areas include developing efficient methodologies for estimating and validating heterogeneous causal effects using large-scale healthcare databases, as well as addressing real-world data challenges like conditional density estimation and batched decision-making problems. Their work bridges theoretical statistics and practical applications in healthcare and machine learning.
Guang Cheng is a Full Professor of Statistics and Data Science at the University of California, Los Angeles (UCLA), and serves as the Graduate Vice Chair. He leads the Trustworthy AI Lab, focusing on generative data science, privacy-preserving synthetic data, and the theoretical foundations of machine learning. His research explores generative AI, trustworthy synthetic data, and high-dimensional statistics. Education : Ph.D. in Statistics, University of Wisconsin-Madison (2003-2006) B.A. in Economics, Tsinghua University (1998-2002) Research Interests : Generative Data Science, trustworthy AI, machine/deep learning theory, privacy-preserving techniques, high-dimensional statistics, and business intelligence applications in finance, healthcare, and marketing. His lab develops tools like artificially generated tables for privacy-preserving data sharing. Publications : Recent work includes advancements in generative models (e.g., TimeAutoDiff, MissDiff), fair classification algorithms, and theoretical analyses of deep learning (e.g., attention mechanisms, minimax analysis). Key themes include synthetic data utility/privacy trade-offs and adversarial robustness. Awards : IMS Fellow (2020), Adobe Data Science Award (2020), NSF CAREER Award (2012). Advising & Grants : Supervises PhD/Master’s/postdoc researchers in trustworthy AI and generative data science. Alumni hold roles at Meta, Amazon, and academia. Active in editorial roles for JASA - Theory & Methods and Canadian Journal of Statistics . Labs & Teams : Leads the Trustworthy AI Lab at UCLA, organizing workshops on synthetic data in finance and healthcare. Collaborates with Amazon as an Amazon Scholar.
Glenn Shafer is a University Professor and Board of Governors Professor at Rutgers University, teaching in the Department of Accounting and Information Systems at the Rutgers Business School. He has been a university educator for 50 years, renowned for pioneering work on the Dempster-Shafer theory of evidence and co-developing the game-theoretic framework for probability. His research spans probability, statistics, causality, and finance, with notable contributions to foundational theories and applications in artificial intelligence and engineering. Education: A.B. in Mathematics (Princeton University, 1968), Ph.D. in Statistics (Princeton University, 1973). Roles: Former Dean of Rutgers Business School (2011–2014), Guggenheim Fellow, Fulbright Scholar, and recipient of the Gorenstein Award for Research and Service. Research Interests: Glenn's work focuses on foundational probability theories, including the Dempster-Shafer theory and game-theoretic probability. He has contributed to the history of probability and statistics, publishing extensively and translating historical works. His recent focus includes the game-theoretic foundations for finance and the historical analysis of martingales. His research bridges philosophical, mathematical, and applied domains, influencing fields like artificial intelligence, finance, and engineering. Awards and Recognition: Honorary doctorate from the University of Economics, Prague (2009); Kampé de Feriet Award (2018); Sarton Medal (2018); Special issue of International Journal of Approximate Reasoning (2022). Grants and Service: Principal investigator on multiple NSF grants, editorial roles at journals like Statistical Science , and service on academic committees at Rutgers. His work has been supported by organizations including the National Science Foundation and the National Endowment for the Humanities.
Steve Goodman is a Professor of Epidemiology and Population Health and Medicine at Stanford University, serving as Associate Dean of Clinical and Translational Research. He co-founded and co-directs the Meta-research Innovation Center at Stanford (METRICS) and leads the Stanford Program on Research Rigor and Reproducibility (SPORR). His work focuses on improving research methodology, reproducibility, and evidence synthesis, particularly in clinical trials and biomedical research. Goodman has held leadership roles in editorial boards, including Annals of Internal Medicine and Clinical Trials , and chairs the Methodology Committee at PCORI. Education: AB in Biochemistry and Applied Math (Harvard), MD (NYU), Pediatrics residency (Washington University), MHS in Biostatistics and PhD in Epidemiology (Johns Hopkins). Former faculty at Johns Hopkins from 1989-2011, where he directed the Division of Biostatistics and Bioinformatics. Research interests include meta-research, Bayesian methods, clinical trial reporting standards (CONSORT/SPIRIT guidelines), and educational initiatives to promote rigorous scientific practices. His articles emphasize reproducibility, transparency, and methodological improvements in biomedical research. Awards: 2016 Spinoza Chair in Medicine, 2019 Abraham Lilienfeld Award, National Academy of Medicine (2020) Grants/Initiatives: CTSA KL2/TL1 training programs, SPORR curriculum development Labs/Teams: METRICS, SPORR, and collaborations in global research integrity networks
Lorenz Linhardt is a Researcher and PhD candidate at the Machine Learning Group of Technical University of Berlin, affiliated with the Berlin Institute for the Foundations of Learning and Data (BIFOLD). His research focuses on robustness of deep neural networks, spurious correlations, and representation learning, with applications in explainable AI and medical domains. Educational Background: M.Sc. in Computer Science, 2019 – ETH Zürich B.Sc. in Computer Science, 2016 – University of Vienna Research Interests: Robust Machine Learning : Addressing vulnerabilities in neural networks caused by spurious correlations and adversarial examples. Human Alignment : Bridging gaps between AI outputs and human judgments via representation learning and similarity metrics. Medical Applications : Leveraging machine learning for counterfactual inference in healthcare and dose-response modeling. Article Trends: Recent work emphasizes alignment between human judgments and AI systems, with studies on latent diffusion models, adversarial robustness, and forensic analysis of malware classifiers. Earlier contributions span astronomy surveys and decision tree optimization. Labs/Teams: Active in BIFOLD and the TU Berlin Machine Learning Group, focusing on foundational AI research.
Liang Hu is a Professor at De Montfort University's School of Computer Science and Informatics, with extensive research in machine learning, feature selection, and Internet of Things applications. His work bridges theoretical advancements in multi-label learning with practical implementations in IoT security and edge computing. PhD from Jilin University (1999) Active researcher with 178 publications (2005-2025) Key collaborator with Hongtu Li, Feng Wang, and Wanfu Gao His research focuses on multi-label feature selection , graph neural networks , and IoT security , developing novel frameworks for heterogeneous information networks, privacy-preserving federated learning, and threat detection in smart environments. His recent work integrates large language models with trigger-action programming systems. Analysis of his 15 most recent publications reveals strong emphasis on multi-view learning (40% of papers), IoT security applications (33%), and graph-based representation learning (27%), demonstrating consistent innovation in handling complex label correlations and heterogeneous data structures. His scientific contributions include novel feature selection methodologies that balance personalized and shared features while minimizing redundancy across multiple views and labels. Liang Hu leads research in edge intelligence and secure IoT programming, with recent projects developing conflict detection frameworks (CCDF-TAP) and privacy-preserving federated graph learning for smart home ecosystems. His work bridges theoretical machine learning with practical cybersecurity implementations.
Wynne Chin is a Professor of Decision & Information Sciences at the C.T. Bauer College of Business, University of Houston. He holds a Ph.D. from the University of Michigan, MBA from the University of Michigan, and degrees from Northwestern University and UC Berkeley. His research focuses on IT adoption, structural equation modeling (SEM), and predictive analytics. He developed the PLS-Graph software, widely used in PLS-based SEM, and has contributed to cross-cultural analysis of IT sourcing and workplace assessment. Education: Ph.D. (Computers & Information Systems, U. Michigan), M.S. (Chemical Engineering, Northwestern), MBA (U. Michigan), B.S. (Biophysics, UC Berkeley). Research interests include IT decision-making, end-user satisfaction, group process measures, and methodological innovations in SEM. He has been recognized globally, ranking 3rd in first-authored articles in MISQ/ISR. Awards include the AIS Fellow, Esther Farfel Award, and AIS Technology Legacy Award. His work bridges IS and statistics, influencing both academic and applied fields. His articles emphasize technology acceptance models, causal modeling, and PLS techniques. Grants and advising contributions are significant but not explicitly detailed here. He has held visiting roles at institutions like the Australian School of Business and is a leader in the PLS research community.
Rachel Melamed is an Assistant Professor of Biology at the University of Massachusetts Lowell, affiliated with the Kennedy College of Sciences and the Center of Biomedical and Health Research in Data Sciences (CHORDS). Her research focuses on linking health and molecular data to understand disease mechanisms and identify interventions for conditions like cancer. She employs computational methods from computer science and epidemiology, integrating diverse datasets including health records, biobanks, and drug experiments. Education: BA in Computer Science (Brown University), PhD in Biomedical Informatics (Columbia University), Postdoctoral training in Biomedical Data Science (University of Chicago). Research Interests: Disease biology & genomics, data science, epidemiology, causal inference. Projects include studying drug impacts on cancer pathways, genetic overlaps between diseases, and leveraging big data for personalized medicine. She leads a lab with graduate and undergraduate researchers exploring computational biology, bioinformatics, and pharmacology. Lab Team: Supervises students specializing in computational biology, statistics, and machine learning. Current members include Anugrah Vaishnav, Jianfeng Ke, Panagiotis Lalagkas, and others. Past researchers include Aylinh Eng and Bishoy Sargius. Key Focus Areas: Cancer etiology, drug repurposing, genetic risk stratification, and translational bioinformatics. Her work bridges computational methods with clinical and biological datasets to uncover actionable insights in disease prevention and treatment.
Bryan T. Karazsia is a Professor of Psychology at the College of Wooster (on leave for AY 2024–25). He holds a B.S. in Psychology from Denison University and advanced degrees (M.A./Ph.D.) in Clinical Psychology from Kent State University, with a minor in Quantitative Methods. His research focuses on clinical psychology, quantitative methodologies, and body image issues, with recent work addressing climate change anxiety and temporal trends in mental health. Education: B.S., Denison University (Psychology) M.A., Kent State University (Psychology) Ph.D., Kent State University (Clinical Psychology; Minor in Quantitative Methods) Research Interests: Dr. Karazsia explores the intersection of psychological well-being and societal factors, particularly through quantitative research methods. His work addresses topics such as body image dissatisfaction, climate change anxiety, and methodological advancements in mediation/moderation analysis. He emphasizes cross-temporal comparisons to understand evolving mental health trends. Teaching & Advising: He teaches courses in psychopathology, statistics, clinical psychology, and personality theory. His advisees have contributed to studies on body appreciation and environmental anxiety measurement tools. Labs/Teams: No specific lab mentioned, but his research often involves collaborations with student researchers and interdisciplinary teams.
Claudia Klüppelberg is a Professor and Chair of Mathematical Statistics at the Center for Mathematical Sciences, Technische Universität München (TUM). Her academic journey includes positions at ETH Zurich, University of Mainz, and TUM since 1997. She holds a Carl von Linde Senior Fellowship at TUM-IAS, focusing on Risk Analysis and Stochastic Modeling. Her research bridges applied probability, statistics, and their applications in finance and insurance, emphasizing extreme value theory, risk processes, and stochastic networks. She has received prestigious awards such as the New Frontiers in Risk Management Award (2007) and Cross of Merit (2001). Her work addresses real-world challenges in financial risk management, including systemic risk in networks and operational risk modeling. Her recent publications explore causal analysis of extreme risks in networks, max-linear models, and Bayesian networks for extreme events. She contributes to academic leadership as an editorial board member and advisor, promoting interdisciplinary stochastic sciences.
