Find Academic Supervisors

• Wine Marketing
• Customer Experiences
• Value Creation
• +7 more

Barry Babin is the Phil B. Hardin Professor of Marketing and Chair of the Department of Marketing, Analytics and Professional Sales at the University of Mississippi (Ole Miss) School of Business Administration. He also serves as Executive Director of the Academy of Marketing Science®. Education: B.S. in Industrial Engineering, Louisiana State University (1982) M.B.A. in Business, University of Central Florida (1987) Ph.D. in Business Administration, Louisiana State University (1991) Barry's research focuses on customer experience design , wine marketing , value creation , and marketing analytics . He has published over 200 works, including books like Multivariate Data Analysis and CB: A Consumer Value Framework , with an h-index of 88. His meta-analyses and sensory marketing studies have significantly influenced service and retail marketing practices. Recent publications highlight trends in augmented reality marketing , robotic shopping assistants , and sustainable packaging . His work spans consumer behavior , salesforce management , and digital innovation . Awards: AMS Harold W. Berkman Distinguished Service Award Barry has directed over 20 dissertations/theses and consults on graduate program structures. He is a sought-after speaker in France and globally on marketing research topics.

Nicola BallhausenView profile

Assistant Professor

• Developmental Psychology
• Cognitive Aging
• Prospective Memory
• +12 more

Nicola Ballhausen is an Assistant Professor in the Department of Developmental Psychology at the Tilburg School of Social and Behavioral Sciences, Tilburg University, Netherlands. Her research focuses on cognitive and psychological aspects of aging, particularly prospective memory, executive function, and social influences on cognitive health in older adults. Her research interests include prospective memory , cognitive aging , metacognition , problem-solving in older adults , social cognition , and longitudinal studies of aging . She investigates how factors such as intergenerational contact, sense of purpose, and technology use impact cognitive performance and well-being in later life. Her work integrates experimental, survey-based, and qualitative methods, often in cross-national datasets like the Health and Retirement Study (HRS) and the English Longitudinal Study of Ageing (ELSA). The most recent articles highlight a strong focus on prospective memory across the lifespan , the role of social engagement in cognitive functioning , and designing accessible cognitive interventions for older adults. Her publications span top journals in gerontology, psychology, and cognitive science, reflecting interdisciplinary collaboration and methodological rigor. Dr. Ballhausen contributes to research aligned with the UN Sustainable Development Goals, particularly those related to healthy aging and well-being. She collaborates extensively with researchers across Europe and has contributed to major reference works such as The Oxford Handbook of Human Memory . Her work includes both empirical studies and methodological advancements, such as exploratory structural equation modeling. While no formal students or awards are listed, her role in supervising research and contributing to academic training is implied through her position and course involvement. Dr. Ballhausen is involved in the design and evaluation of web-based cognitive tools, such as the Shared, Web-based, Intelligent Flexible Thinking Training (SWIFT), emphasizing user-centered design and ecological validity. Her work bridges fundamental cognitive research with practical applications for aging populations.

• Psychometrics
• Log Data Analysis
• Missing Data Methods
• +4 more

Prof. Dr. Steffi Pohl holds the Chair of Methods and Evaluation/Quality Assurance at the Faculty of Education and Psychology, Freie Universität Berlin since 2019. Previously, she was a Junior Professor (2013-2019) and researcher at institutions including Friedrich-Schiller University Jena and University of Bamberg. She earned her PhD in Psychometrics from Friedrich-Schiller University Jena (2010) and holds a Diplom in Psychology (2004) from Freie Universität Berlin. Her research focuses on advanced statistical methods in educational and psychological testing, including response time modeling, missing data mechanisms, and causal inference in assessment. She has pioneered work on test engagement detection via response patterns and log数据分析. Awards include the 2020 Psychometric Society Early Career Award and 2011 Gustav A. Lienert Dissertation Prize. Pohl serves on editorial boards of Psychometrika , Journal of Educational and Behavioral Statistics , and Zeitschrift für Psychologie . She chairs the Berlin School of Mind and Brain faculty and holds governance roles in academic senates. Her research projects include the National Educational Panel Study (NEPS) and collaborations on test design innovations. Current teaching includes advanced courses in empirical research methods, multivariate statistics, and educational measurement. She actively develops methodologies for analyzing log数据 from digital testing platforms and improving assessment reliability in large-scale studies.

Seth KaplanView profile

Professor

• Industrial/Organizational Psychology
• Employee Well-being
• Emotions at Work
• +4 more

Seth Kaplan is a Professor in the Department of Psychology at George Mason University. His research focuses on employee well-being, team effectiveness, virtual work, and occupational health through projects like NSF-funded emotion regulation interventions and Army Research Institute collaborations on affective forecasting. He directs the KA-Lab, studying team resilience, metaperceptions, and statistical methodologies. Recent publications analyze job boredom, cognitive reappraisal interventions, and personality measurement innovations. His book Crisis-Ready Teams (2024) synthesizes data from high-risk environments. Courses taught include Occupational Health, Organizational Change, Psychometrics, and Multivariate Statistics. Grants: National Science Foundation (Co-PI): Just-in-Time Adaptive Interventions for Emotion Regulation Army Research Institute (PI): Affective Forecasting Errors Recent Presentations: Personality and generative AI use at work (SIOP 2025) Work situation identification via NLP (SIOP 2025) Helicopter helping in teams (SIOP 2025)

• Housing Finance
• Mortgage Markets
• Residential Property Investment
• +5 more

Dr. María Yanotti serves as a Senior Lecturer in Economics at the Tasmanian School of Business and Economics (TSBE) within the University of Tasmania. Her academic appointment is based at the Rivers Edge Building, Inveresk campus in Launceston, Tasmania. With a specialization in housing finance, banking, and household economics, Dr. Yanotti has established herself as an expert in mortgage markets and residential property investment, frequently sought for media commentary on these subjects. Dr. Yanotti's research interests span housing finance, mortgage markets, residential property investment, banking economics, and household financial decision-making. Her work frequently examines the intersection of financial systems and housing markets, with particular attention to Australian market dynamics. She has published extensively on mortgage choice determinants, financial inclusion, and the relationship between housing finance systems and economic outcomes. Her research methodology often combines economic theory with sophisticated statistical analysis to address practical questions in housing finance. Analysis of Dr. Yanotti's publication record reveals a strong focus on housing economics and mortgage markets, with significant contributions to understanding residential investment patterns, mortgage choice determinants, and housing finance systems. Her research spans both theoretical and applied economics, with numerous publications in leading domestic and international journals. She has also expanded her research interests to include environmental economics topics such as marine plastic pollution valuation and climate change impacts, demonstrating interdisciplinary reach while maintaining her core expertise in housing finance. Dr. Yanotti has received research funding from the Australian Housing and Urban Research Institute (AHURI) and is affiliated with the Economic Society of Australia and the Women in Economics Network. Her work on residential property investment, particularly the phenomenon of investors favoring properties near their own residence, has received significant attention in both academic and policy circles. At the University of Tasmania, Dr. Yanotti teaches across multiple economics and finance subjects including microeconomics, statistics and quantitative methods, economics for managers, finance for managers, and data analysis for managers. Her teaching reflects her research expertise, connecting theoretical economic concepts with practical applications in housing and financial markets.

Andrea GarzelliView profile

Professor

• Remote Sensing
• Image Processing
• Signal Processing
• +4 more

Andrea Garzelli is a Full Professor at the University of Siena's Department of Information Engineering and Mathematical Sciences. His research focuses on remote sensing image processing, particularly in optical and SAR sensor technologies, image fusion, and spatial resolution enhancement. He holds teaching roles in 'Fundamentals of Signal Processing and Telecommunications' and 'Statistical Signal Processing.' He earned his Ph.D. in Computer Science and Telecommunication Engineering from the University of Florence. Notably, he was recognized as a World's Top 2% Scientist by Stanford University for 2019–2023 and his career-long contributions. He served as President of the University of Siena's Quality Assurance Committee (2016–2021) and currently coordinates the graduate program in Computer and Information Engineering. Research interests include satellite data analysis (e.g., Sentinel-2, PRISMA), hypersharpening techniques, and environmental monitoring. His work bridges theoretical advancements (e.g., pansharpening algorithms) and practical applications like urban land classification and vegetation index enhancement. Recent articles emphasize reproducibility, meta-analysis, and synthetic data generation through GANs. Awards: World's Top 2% Scientists (2019–2023 & career). Grants/Advising: Supervises remote sensing theses; no specific grants mentioned. Labs/Teams: Leading research in the department's remote sensing and signal processing groups.

Vassilis ChristophidesView profile

Professor

• Databases
• Web Information Systems
• Big Data Processing
• +9 more

Vassilis Christophides is a Professor of Computer Science at the University of Crete and holds an advanced research position at Inria Paris, where he leads work in the MiMove team. His research spans databases, web information systems, big data processing, and IoT analytics, with a strong emphasis on entity resolution, data integration, and scalable data mining. He has supervised numerous research projects funded by the European Union and the Greek State, and has published over 130 articles in top-tier conferences and journals. Research Interests: His primary research areas include Databases, Web Information Systems, Big Data Processing and Analytics, and Information Systems for the Internet of Things. He also focuses on entity resolution, knowledge graphs, streaming data, and explainable AI, particularly in the context of anomaly detection and fairness-aware data systems. His recent work explores hybrid attention models for entity alignment and causal analysis in time series classification. Recent Research Trends: Analysis of his recent publications (2021–2025) reveals a strong focus on entity resolution with fairness constraints, explainable anomaly detection, and adaptive scheduling in IoT edge analytics. He also investigates deepfake detection, crop type mapping using satellite data, and structural bias in knowledge graphs, demonstrating a broad and impactful research portfolio at the intersection of data management and machine learning. Scientific Awards: 2004 SIGMOD Test of Time Award Best Paper Award, ISWC 2003 Best Paper Award, ISWC 2007 Advising and Grants: While specific student names are not listed in the provided texts, Christophides has co-authored numerous papers with researchers such as Vasilis Efthymiou, Ioannis Tsamardinos, and Nikolaos Myrtakis, suggesting active mentorship. He has been the scientific coordinator of multiple EU and national research projects, indicating substantial grant leadership and project management experience. Labs and Teams: He is affiliated with the MiMove team at Inria Paris, a research group focused on mobility and data-intensive systems. His work bridges academic and applied research, leveraging Inria’s infrastructure for large-scale data experimentation and innovation in IoT and edge computing environments.

Yi LiView profile

Professor

• Survival Analysis
• Data Science
• High-dimensional Inference
• +11 more

Yi Li is the M. Anthony Schork Collegiate Professor of Biostatistics at the University of Michigan School of Public Health. With a PhD in Biostatistics from the University of Michigan (1999) and postdoctoral training at Harvard (1999-2000), Dr. Li has established himself as a leading researcher in statistical methodology with applications across multiple biomedical domains. Dr. Li's research spans survival analysis, data science, high-dimensional inference, machine learning, deep learning, spatial data analysis, random-effects models, clinical trial design, and infectious disease modeling. His methodological work finds application in cancer genetics/genomics, radiomics, racial disparity analysis, chronic disease research, and opioid overuse studies. With over 230 publications in major statistical journals including JASA, Biometrika, JRSSB, and Biometrics, as well as premier subject matter journals like PNAS, JAMA, and JCO, Dr. Li's work has significantly impacted both statistical theory and biomedical applications. His research portfolio demonstrates consistent evolution from foundational methodological work in survival analysis and spatial statistics to cutting-edge applications in high-dimensional data, machine learning, and deep learning approaches for complex biomedical problems. The recent publications reveal increasing focus on integrating multiple data sources, causal inference in observational studies, and developing interpretable machine learning models for clinical applications. Dr. Li's work has been continuously supported by NIH funding since 2003, including multiple National Cancer Institute grants (R01 CA95747, 1P01CA134294-010002, R21CA157219, R01CA249096, R01CA269398) and a National Institute on Aging grant (R21AG058198). He actively collaborates with researchers from the University of Michigan and Harvard University on clinical and observational studies. As an educator, Dr. Li has taught advanced courses in survival analysis and statistical methods, mentoring the next generation of biostatisticians. His methodological contributions have been widely recognized through invitations to serve on NIH study sections (BMRD 2008-2012, EPIC 2015-2019) and as Associate Editor for leading statistical journals including Journal of the American Statistical Association, Biometrics, and Scandinavian Journal of Statistics.

• Sequential Decision Making
• Bandit Problems
• Theoretical Reinforcement Learning
• +6 more

Claire Vernade is a Group Leader at the University of Tübingen in the Cluster of Excellence Machine Learning for Science. She leads an active research group focused on theoretical aspects of sequential decision making, with particular expertise in bandit problems and reinforcement learning theory. Her work bridges theoretical foundations with practical applications in scientific discovery. Her research interests span sequential decision making, bandit problems, theoretical Reinforcement Learning, Learning Theory, and principled learning algorithms. She has made significant contributions to understanding non-stationary environments, lifelong learning frameworks, and the theoretical foundations of bandit algorithms. Her work on "Eigengame: PCA as a Nash Equilibrium" received an Outstanding Paper Award at ICLR 2021. Dr. Vernade has been awarded prestigious grants including an Emmy Noether award (2022) for her FoLiReL project and an ERC Starting Grant (2024) for her ConSequentIAL project. Her current ERC project explores the role of Reinforcement Learning in developing Continual Learning agents, with applications to scientific domains like drug discovery and micro-chemistry. Emmy Noether award under the AI Initiative call (2022) ERC Starting Grant (2024) Outstanding Paper Award at ICLR 2021 She currently supervises three PhD students and actively recruits postdocs and PhD candidates through the IMPRS-IS and ELLIS doctoral programs. Her group collaborates extensively with the broader machine learning community, organizing workshops like FoRLaC at ICML 2024 and serving as co-chairs for tutorials at major conferences. Dr. Vernade is also deeply committed to diversity and inclusion in machine learning, co-leading initiatives like Women in Learning Theory and Tübingen Women in Machine Learning.

Scott E. CrouterView profile

Professor

• Physical Activity Measurement
• Wearable Sensors
• Machine Learning Applications
• +5 more

Scott E. Crouter is a Professor in the College of Education, Health, and Human Sciences at the University of Tennessee, Knoxville. He serves as director of the Applied Physiology Laboratory and is a Fellow of the American College of Sports Medicine. His research focuses on improving physical activity and energy expenditure measurement using wearable monitors and machine learning algorithms, with applications in youth, adults, and special populations like pregnant individuals and cancer survivors. Education: Postdoctoral in Nutritional Sciences (Cornell, 2005-07), PhD in Exercise Physiology (University of Tennessee, 2005), MS in Cardiac Rehabilitation/Adult Fitness (University of Wisconsin, 2000), BS in Exercise Science (Linfield College, 1998) Dr. Crouter's work spans physical activity assessment , sedentary behavior analysis , and technology validation . He has developed machine learning models for energy expenditure estimation using ActiGraph GT9X and Cosmed K5 devices. His recent studies examine pregnancy hyperglycemia interventions, urban environment impacts on activity patterns, and robotic exercise aids. He has secured significant NIH funding for projects including digital health weight management , pediatric obesity treatment translation , and fall risk factors in the elderly . As associate editor for Medicine and Science in Sports and Exercise and Journal for the Measurement of Physical Behaviors , he contributes to methodological advancement in activity monitoring. Scientific Awards: Fellow, American College of Sports Medicine Editorial Leadership National Physical Activity Plan Alliance Participation CDC/National Collaborative on Childhood Obesity Research Contributions National Academy of Sciences Committee Member Dr. Crouter's student collaborations include works with Natalie Butte on youth compendiums, Allan Opotowsky in cardiac rehabilitation trials, Patrick Hibbing in metabolic equivalent research, and Sara LaMunion in consumer monitor validation. His lab has produced over 100 publications and 15 recent articles focusing on sensor technology and population health.

Chiara SabattiView profile

Professor

• Statistical Genetics
• Computational Biology
• Bayesian Statistics
• +2 more

Chiara Sabatti is a Professor of Biomedical Data Science and Statistics at Stanford University, with affiliations to the Stanford Center for Computational, Evolutionary and Human Genomics (CEHG), Bio-X, and the Stanford Cancer Institute. She serves as Associate Director for Stanford Data Science and has led the development of the Data Science Major curriculum since 2012. Research Focus: Statistical models for high-throughput genomics data, causal inference in genetic studies, false discovery rate control, and knockoff methods for variable selection. Key Leadership: Associate Chair for Education and Training (2020-present), Vice Chair of Biomedical Data Science (2018-2019). Her work bridges statistical genetics with data science education, emphasizing robustness and interpretability in scientific findings. Recent publications highlight innovations in genome-wide association studies (GWAS), causal variant localization, and cost-effective sequencing techniques for underrepresented populations. Current projects include developing knockoff-based methods to address population structure and multi-resolution hypothesis testing. Scientific Awards: Institute of Mathematical Statistics (IMS) Fellow (2022) NSF CAREER Award (2003-2008) She mentors doctoral and graduate students in Biomedical Data Science, collaborates with the Data Studio on interdisciplinary projects, and actively recruits curious researchers to her lab. Her outreach efforts focus on expanding data science education and increasing research participation from underrepresented groups.

• Behavior Genetics
• Family and Twin Studies
• Causal Modeling
• +7 more

Miriam A. Mosing, PhD, is currently the Head of the Behavior Genetics unit at the Max Planck Institute for Empirical Aesthetics in Frankfurt am Main, Germany, a position she has held since August 2021. She also serves as an Associate Professor at the Department of Medical Epidemiology and Biostatistics and the Neuroscience Department at the Karolinska Institutet in Stockholm, Sweden, and holds a Develop Research Momentum (DRM) Senior Research Fellowship at the Melbourne School of Psychological Sciences, University of Melbourne, Australia. Dr. Mosing's primary research focuses on behavior genetics, specifically examining how genetic and environmental factors interact to influence individual differences in quality of life throughout the lifespan and expertise acquisition. Her work frequently uses musical expertise as a model domain to study these interactions. She investigates the complex relationships between cultural engagement (particularly musical and creative activities), mental health, social isolation, loneliness, stress, flow experiences, and overall well-being. Her research employs advanced methodologies including twin studies, causal modeling, and genome-wide association studies to disentangle the relative contributions of genetic and environmental factors. Her publication record demonstrates consistent contributions to understanding the genetic architecture of human behavior, with recent work examining dancer personalities, musical ability, cognitive aging, mental health outcomes, and gene-environment interactions in expertise development. Her research has significant implications for identifying causal pathways that could inform interventions to improve quality of life and mental health outcomes. Develop Research Momentum (DRM) Fellowship from the University of Melbourne (2020-2025) Marcus och Amalia Wallenbergs Foundation grant for 'Healthy learning' research (2018-2022) National Institute of Aging research project grant (2018-2022) Multiple Karolinska Institutet Fellowships for research on gene-environment interplay Dr. Mosing leads a research program that integrates large-scale twin studies with advanced genetic methodologies to understand the complex pathways through which genetic predispositions and environmental exposures jointly shape human development, health, and expertise. Her Behavior Genetics unit at the Max Planck Institute serves as a hub for international collaboration on these questions, working with twin registries and research groups across multiple countries.

• Child Psychiatry
• Neurodevelopmental Disorders
• Attention-Deficit/Hyperactivity Disorder (ADHD)
• +3 more

Dr. Kate Langley is a Senior Lecturer in the School of Psychology at Cardiff University. Her research focuses on the genetic and environmental risk factors influencing neurodevelopmental disorders such as ADHD, Autism Spectrum Disorders, and Conduct Disorder. She explores how these conditions manifest across developmental stages and their societal impacts. Dr. Langley teaches Developmental Psychology at the undergraduate level and leads the Neurodevelopmental Disorders 1: Neurobiology module in the MSc Children's Psychological Disorders program. Her work integrates clinical and epidemiological approaches, leveraging large datasets like Welsh healthcare records to study ADHD diagnosis disparities and cohort dynamics. Collaborations include projects on genomic data integration (DRAGON-Data) and the neurocognitive correlates of ADHD in young adults. Dr. Langley’s research highlights the importance of early intervention and understanding developmental trajectories in mental health.

Jingchao NiView profile

Assistant Professor

• Machine Learning
• Time Series Analysis
• Cross-Modal Learning
• +9 more

Jingchao Ni is an Assistant Professor in the Department of Computer Science at the University of Houston. He previously worked as a researcher at NEC Labs America (2018-2022) and AWS AI Labs (2022-2024). He earned his Ph.D. in Computer Science from The Pennsylvania State University's College of Information Sciences and Technology in 2018 under Prof. Xiang Zhang. Research Interests: Machine Learning, Time Series Analysis (Cross-Modal/Multimodal Integration, LLM Reasoning), Graph Learning, Anomaly Detection, Generative Models, and applications in Healthcare (personalized systems, Cyber-Physical Systems, AIOps). His recent publications focus on multimodal time series analysis, vision models for temporal data, and interpretable graph neural networks, with deployments in AWS cloud systems. He has advised students on projects involving LLM agents, causal discovery, and robust forecasting. Awards include a AAAI 2019 Most Influential Paper (PaperDigest) and an ICLR 2022 Spotlight Presentation. He leads the Data-Driven Intelligence (D2I) Group and has contributed to tutorials at KDD 2025 and IJCAI 2025.

Akira EndoView profile

Assistant Professor

• Infectious Disease Modelling
• Epidemiology
• Mathematical Modelling
• +3 more

Dr. Akira Endo is an Assistant Professor at the National University of Singapore (NUS), holding a primary affiliation with the Saw Swee Hock School of Public Health and a joint appointment at the Yong Loo Lin School of Medicine. He also served as a Visiting Research Fellow at Nagasaki University and the London School of Hygiene & Tropical Medicine. Dr. Endo earned his MD & BSc in Medicine from the University of Tokyo (2011–2017) and a PhD in Infectious Disease Epidemiology from the London School of Hygiene & Tropical Medicine (2017–2021). His research focuses on mathematical modelling of infectious disease dynamics, particularly examining transmission patterns within households, schools, workplaces, and sexual networks. He has contributed to understanding optimal control strategies for outbreaks like mpox (monkeypox) and influenza, emphasizing network interventions and public health policy. His work bridges epidemiology and mathematical modeling, addressing challenges such as overdispersion in disease transmission and the impact of superspreading events. Key areas of interest include: Analysis of heavy-tailed sexual contact networks in mpox outbreaks Assessment of border control and vaccination strategies for pandemic management Quantitative design of interventions to mitigate disease spread Dr. Endo has been recognized with awards including the Japan Society for Mathematical Biology Early Career Award (2022) and the Japan Society for the Promotion of Science Overseas Research Fellowship (2021). His research also explores global health equity, such as assessing mpox risks in Southeast Asia and vaccine development for emerging pathogens like Nipah virus. His career includes roles as an Assistant Professor at Nagasaki University and a JSPS Research Fellow in London. He collaborates internationally, contributing to interdisciplinary teams like the CMMID COVID-19 Working Group. His work frequently appears in high-impact journals such as Science , Lancet , and Proceedings of the National Academy of Sciences .