Find Academic Supervisors

• Scientometrics
• Bibliometrics
• Higher Education Evaluation
• +3 more

Tanja Yevremov serves as Associate Professor at the Department of Psychology, Faculty of Philosophy, University of Novi Sad, where she has been employed since 2002. She teaches Advanced Statistics and Multivariate Analysis for Psychology students and Fundamentals of Statistics for Social Work students, while coordinating international cooperation through the Erasmus+ program. Her educational background includes: Bachelor's degree in Psychology (1996), Faculty of Philosophy, University of Novi Sad Master's degree in Psychology (2008), Faculty of Philosophy, University of Novi Sad Ph.D. in Psychology and Scientometrics (2013), Faculty of Philosophy, University of Novi Sad Dr. Yevremov's research centers on scientometrics and higher education evaluation, applying bibliometric methods to analyze knowledge structures in psychology and social sciences. Her work examines publication patterns, citation dynamics, and personality psychology frameworks, with significant contributions to understanding the Dark Triad construct and national research systems. She also investigates student satisfaction metrics and teaching quality assessment in academic settings. Her publication record demonstrates consistent interdisciplinary application of bibliometric techniques across psychology, sociology, and educational research, revealing methodological innovations in mapping scientific knowledge development and evaluation practices. She actively participates in the Laboratory for Experimental Psychology and contributes to institutional initiatives including student evaluation systems and international academic exchange programs.

• Statistics
• Econometrics
• Supply Chain Management
• +7 more

Dr. Anna Tatarczak is a Lecturer at the Department of Management Research Methods, Institute of Management and Quality Sciences, Faculty of Economics, Maria Curie-Skłodowska University. She holds a PhD in mathematical sciences from Jagiellonian University (2015) and an MSc in mathematics from Maria Curie-Skłodowska University. Her 13-year career spans research and education in statistics, econometrics, logistics, and decision analysis. PhD: Jagiellonian University (2015) MSc: Maria Curie-Skłodowska University (original text suggests Master of Science) Specialized in data analysis and project management through additional postgraduate studies Her research focuses on quantitative methods in management, particularly game theory approaches for supply chain coalitions, profit allocation models, and horizontal collaboration in logistics. She has led multiple research projects including "Coordination of supply chains: a game theory approach" (2020-2022) and commercial projects for companies like SOLVERIS and Innovative Engineering. Her academic output includes 27 publications with ministerial score 868, focusing on: Collaborative logistics Supply chain optimization Decision support systems Economic disparity analysis Energy consumption modeling Statistical applications in management She has implemented educational projects for children and youth, and served as expert for international entities including the State Education Development Agency (Latvia) and Horizon 2020. Her work combines rigorous mathematical approaches with practical industry applications.

• Psychology
• Human Factors
• Data Visualization
• +7 more

Pedro Valero Mora is a Professor at the University of Valencia , affiliated with the Faculty of Psychology and Speech Therapy and the Department of Behavioural Science Methodology . He leads research at the Universitat de València Research Institute of Transit and Road Safety (INTRAS) and contributes to the GIDOP Optimal Development Research Group . PhD in Psychology (Universitat de València, 1996) Specializes in Human-Computer Interaction , Driving Simulation , and Data Visualization Develops tools for Statistical Analysis and Educational Technology Focuses on Transportation Safety , Behavioral Science , and GIS Applications His recent work analyzes driving performance through GIS mapping , fatigue studies , and mindfulness impacts . Publications emphasize automated data visualization and road safety in university campuses. Research spans 26 years, including collaborations on EU COST Action TU1101 and ViSta software development.

• Lifespan Psychology
• Cognitive Aging
• Neural Plasticity
• +2 more

Ulman Lindenberger is Managing Director (2006-2009, 2016-2019, 2025-2027) and Director of the Center for Lifespan Psychology at the Max Planck Institute for Human Development in Berlin. He concurrently serves as Co-Director of the Max Planck UCL Centre for Computational Psychiatry and Ageing Research. He holds honorary professorships at Freie Universität Berlin, Universität des Saarlandes, and Humboldt-Universität zu Berlin. Education includes a Dipl.-Psych. from Technische Universität Berlin (1985), Dr. phil. from Freie Universität Berlin (1990 summa cum laude ), and Habilitation in Psychology (1998). His research examines: Behavioral and neural plasticity across lifespan Brain-behavior relationships Lifespan developmental theory Multivariate developmental methodology Formal models of behavioral change His publications focus on cognitive aging patterns, neural plasticity mechanisms, and methodological innovations in lifespan psychology. Research demonstrates consistent themes: neurocognitive dedifferentiation in aging, dopaminergic modulation of cognition, and environmental influences on brain plasticity. Awards include: Gottfried Wilhelm Leibniz Prize (2010) Fellow of Royal Society (2025) Foreign Member of Royal Swedish Academy of Sciences (2023) Mentoring Award of German Psychological Society (2011) He has supervised over 50 doctoral students and secured major grants including DFG collaborative projects, BMBF initiatives (Berlin Aging Study I/II), EU Horizon 2020 (LIFEBRAIN), and Max Planck Society strategic funds. Leads multidisciplinary teams at Center for Lifespan Psychology and Max Planck UCL Centre, coordinating international projects like COBRA (Cognition, Brain, and Aging) and SYNAPSE.

Michel BesserveView profile

Researcher

• Causal Machine Learning
• Complex Systems
• Neuroscience
• +4 more

Michel Besserve is a Senior Research Scientist in the Empirical Inference department at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. His research bridges machine learning theory with applications in neuroscience and complex systems analysis. He leads a research group focused on developing causal machine learning tools to uncover the internal structure and transformations of complex artificial, physical, and socioeconomic systems. Dr. Besserve's primary research interests center on causal machine learning and its applications to understanding complex systems. His work investigates how causality can provide principled ways to study and improve AI algorithms, particularly focusing on the identifiability of causal models and the principle of Independence of Causal Mechanisms (ICM). He develops theoretical frameworks and practical tools for causal inference in complex equilibrium systems, neural circuits, and socioeconomic contexts. His research has significant implications for building trustworthy and interpretable AI systems that can reliably handle real-world complexity. Analysis of Dr. Besserve's recent publications reveals a strong focus on causal representation learning, with significant contributions to independent mechanism analysis and the identifiability of nonlinear generative models. His work spans both theoretical foundations and practical applications, connecting machine learning with neuroscience to understand brain function through causal inference. The interdisciplinary nature of his research is evident in publications spanning top machine learning conferences (NeurIPS, ICML, ICLR) and leading neuroscience journals (Nature, PLOS Biology). Dr. Besserve has established productive collaborations across multiple institutions, particularly with researchers at the Max Planck Institute and ETH Zurich. His work demonstrates how integrating causal principles with machine learning can address fundamental challenges in AI robustness and interpretability, with applications ranging from brain network analysis to economic modeling. His research group focuses on developing the Causal Computational Model (CCM) framework, which aims to create digital representations of real-world systems that integrate data, domain knowledge, and interpretable causal structure. This work has potential applications in climate modeling, industrial digital twins, and economic simulation.

• Visual Analytics
• Temporal Data Analysis
• Data Cleansing
• +3 more

Theresia Gschwandtner is a Researcher at TU Wien's Research Division of Visual Analytics (E193-07). Her work focuses on advancing visual analytics methodologies for temporal data, fraud detection, and uncertainty visualization. She leads the Network Lab and contributes to tools like TimeCleanser for data cleansing and NEVA for fraudulent network identification. Her research emphasizes interactive systems for guidance in data analysis, provenance tracking, and enhancing user-centric visualization frameworks. Key research interests include temporal data preprocessing, multivariate time series analysis, and the integration of automated guidance systems into visual analytics platforms. She has collaborated on projects such as Hermes (economic network exploration) and TBSSvis (temporal blind source separation), which combine algorithmic innovation with intuitive user interfaces. Guidance frameworks and user studies are central to her work, exploring how automated support impacts performance and mental state during complex data analysis tasks. She has advised students on theses addressing data quality, cyclical pattern detection, and lighting design visualization. Notable contributions include the Quantifying Uncertainty in Time Series Processing framework and the LightGuider system for interactive lighting design guidance. Her work bridges theoretical advancements with practical applications in healthcare, finance, and engineering domains.

Silvia MikschView profile

Professor

• Visual Analytics
• Information Visualization
• Interaction Design
• +6 more

Silvia Miksch is a Full University Professor of Visual Analytics at TU Wien's Faculty of Informatics, leading the CVAST Center. She holds a PhD from the University of Vienna and has held roles including Head of the Department of Information and Knowledge Engineering at Danube University Krems. Her research focuses on Visual Analytics, Information Visualization, Temporal Data Analysis, and Medical Informatics. She has supervised numerous PhD and Master’s students, with notable advisees including Ignacio Baltazar Pérez Messina and Davide Ceneda. Her work bridges theory and practice, addressing challenges in Visual Analytics for healthcare, business intelligence, and digital humanities. Awards include the IEEE VGTC Technical Achievement Award (2023) and induction into the IEEE Visualization Academy (2020). She actively contributes to conferences like IEEE VIS and EuroVis as program chair and steering committee member. Her projects, such as 'VisuExplore' and 'DisCo', have received recognition for advancing visualization in medical and cultural domains. Key research areas include guidance-enriched systems, network visualization, and temporal reasoning. She explores applications in fraud detection, cultural heritage analysis, and pandemic data visualization. Her lab's tools, like 'Hermes' and 'COVIs', exemplify task-driven design for real-world data challenges.

Bersimis SotiriosView profile

Associate Professor

• Statistical Process Control
• Public Health Surveillance
• Health Analytics
• +5 more

Sotirios Bersimis is an Associate Professor at the University of Piraeus, Department of Business Administration. He holds additional roles as an elected member of the board of directors of the National Statistical Institute (Greece) and representative for FenSTATs and ECAS. Previously, he served as President of the Hellenic Organization for Health Care Services (EOPYY) and as President of the European Healthcare Fraud & Corruption Network (EHFCN). His education includes a PhD in Statistics from the University of Piraeus, an MSc in Statistics from Athens University of Economics and Business, and a BSc in Statistics and Insurance Science from the University of Piraeus. His research focuses on stochastic models for process monitoring, statistical process control, and health analytics. He has published over 60 peer-reviewed articles in journals like Journal of Quality Technology , Statistics in Medicine , and Annals of the Institute of Statistical Mathematics . His work emphasizes applications in healthcare surveillance, quality management, and fraud detection. Notable contributions include the development of the Process Monitoring Group and the 'Multivariate Statistical Process Control Charts: An Overview' paper, which remains highly cited in the field. Bersimis has received awards such as the 2018 ENBIS Best Manager Award and a public honor from the Greek Prime Minister. He actively collaborates with healthcare institutions, pharmaceutical companies, and international organizations, contributing to projects on health expenditure modeling and anti-fraud initiatives. His teaching spans undergraduate and postgraduate programs in statistics, biostatistics, and data science.

Anton WestveldView profile

Senior Lecturer

• Bayesian Methodology
• Network Analysis
• Game Theoretic Data
• +3 more

Anton Westveld is a Senior Lecturer in the Department of Statistics at the Australian National University (ANU), within the Research School of Finance, Actuarial Studies & Statistics (RSFAS). He also serves as an Affiliate Associate Professor at Virginia Commonwealth University since August 2023. His research focuses on Bayesian methodology, network analysis, game theoretic data, and statistical causality, with notable contributions to ecological modeling and agent-based stochastic simulations. Westveld holds a Bachelor’s in Economics and Political Science from the University of Michigan (Ann Arbor), a Master’s in Applied Economics and Statistics from the same institution, and a PhD in Statistics from the University of Washington. His work has been published in prestigious journals like the Annals of Applied Statistics and Proceedings of the National Academy of Sciences . His research interests span Bayesian inference, relational data analysis, and causal modeling, with applications in ecological and health sciences. Recent work includes developing Bayesian methods for ecological drivers in marine viral communities and latent socioeconomic health indices for policy evaluation. Notable articles include analyses of menstrual disorder surveys using Gaussian copulas, ecological metagenomics studies, and Bayesian-optimized bootstrap techniques for uncertainty quantification. His interdisciplinary collaborations bridge statistics with environmental science, public health, and economics.

Jack GallantView profile

Professor

• Computational Neuroscience
• Cognitive Neuroscience
• Systems Neuroscience
• +5 more

Jack Gallant is a Professor of Neuroscience at the University of California, Berkeley, where he leads a research laboratory focused on cognitive, systems, and computational neuroscience. His work centers on developing and applying advanced methods for analyzing functional magnetic resonance imaging (fMRI) data, particularly through the Voxelwise Encoding Model framework. Dr. Gallant's research interests span computational neuroscience, cognitive neuroscience, and systems neuroscience, with a particular focus on understanding how the brain represents visual and semantic information. His lab develops cutting-edge tools for brain mapping and neural decoding, creating detailed maps of cortical organization related to visual and language processing. Recent work has emphasized group-level analysis techniques that integrate data across multiple participants while accounting for individual differences. His publication record demonstrates consistent innovation in fMRI methodology, with recent papers focusing on comprehensive frameworks for encoding models, individual differences in brain organization, and high-resolution mapping of semantic representations. Gallant's work bridges theoretical neuroscience with practical applications, resulting in widely used software tools that advance the field of neuroimaging. Sloan Research Fellow (1998) Dr. Gallant has mentored numerous graduate students and postdoctoral researchers, including Emily Meschke who recently completed her PhD. His lab maintains active collaborations and is currently recruiting postdocs to continue developing the Voxelwise Encoding Model framework. The lab also produces educational resources including interactive brain viewers and comprehensive tutorials that have become standard tools in the neuroimaging community.

Giancarlo ManziView profile

Associate Professor

• Machine Learning
• Multivariate Statistics
• Bayesian Statistics
• +5 more

Giancarlo Manzi is an Associate Professor of Statistics at the Department of Methods and Models for Economics, Territory, and Finance, University of Rome La Sapienza. He holds a PhD from the University of Milan-Bicocca and conducted thesis research at the University of Toronto. His career includes roles at the Medical Research Council Biostatistics Unit in Cambridge and the University of Milan. University of Rome La Sapienza (Current) Medical Research Council Biostatistics Unit (Former Researcher) University of Milan (Former Researcher and Associate Professor) University of Milan-Bicocca (PhD) University of Toronto (Thesis collaboration) His research spans Machine Learning , Bayesian Statistics , and Smart Mobility , with a focus on Covid-19 analytics , data visualization , and epidemiological modeling . He integrates Multivariate Statistics with Public Health to address complex challenges in health systems and urban environments. The 15 most recent publications highlight expertise in quantile regression , Bayesian networks , time-series analysis , and smart mobility optimization . His methodological contributions include wavelet analysis, cross-correlation models, and SIRD modeling frameworks applied to pandemic dynamics and bike-sharing systems. Scientific awards and honors are not explicitly mentioned in the provided texts. Giancarlo Manzi has taught at the University of Verona, Catholic University of the Sacred Heart, and Universidad Carlos III in Madrid, maintaining strong ties with Italy's academic institutions.

• Applied Probability
• Insurance Mathematics
• Finance
• +3 more

Mogens Bladt is a Professor of Applied Probability and Insurance Mathematics at the Department of Mathematical Sciences, University of Copenhagen. He has held positions since 2018 after 24 years as Principal Researcher at the National University of Mexico (1994-2018), with visiting professorships at Technical University of Denmark and University of Copenhagen since 2001. Research: Focuses on time-inhomogeneous phase-type distributions, matrix-oriented life insurance models, heavy-tailed distributions, and diffusion bridge simulation Teaching: Offers graduate/undergraduate courses in Applied Probability, Stochastic Processes, Risk Theory, and Numerical Analysis Scientific Contributions: Developed R packages for Markov jump processes, phase-type distributions, and diffusion bridges. Holds grants from Mexico and Denmark, including Danish Research Council funding (2007–2008). Supervised 5 PhD, 8 Master’s, and 13 Bachelor’s theses Organized academic workshops and served as Associate Editor for Stochastic Models since 1997

• Applied Statistics
• Quantitative Research Methods
• Medical Statistics
• +7 more

Professor Paul White is a distinguished academic at the University of the West of England (UWE Bristol), serving as Professor of Applied Statistics within the Faculty of Engineering and Technology's Department of Engineering, Design and Mathematics. His work focuses on applying statistical methods to benefit society (ASBOS), with particular emphasis on collaborative research across UWE's applied and life sciences departments and with the National Health Service (NHS). Dr. White holds a BSc, MSc, and PhD, though specific institutions are not mentioned in the provided text. His academic journey has established him as a leading figure in applied statistics education and research methodology. Professor White's research spans multiple domains of applied statistics, with particular expertise in quantitative research methods, research methodology, sample size determination, and the design and analysis of experiments. He maintains a strong ethical focus in quantitative research while applying his skills to medical statistics and multivariate statistics. His work often intersects with healthcare applications, as evidenced by his involvement in clinical trials and medical research collaborations. A notable initiative he co-founded is the DARK ARTS (Design And Research Knowledge for Analysis of Randomised Trials) group, which focuses on advancing methodologies for randomized trials. His extensive publication record shows a clear trend toward interdisciplinary collaboration, particularly in healthcare applications of statistics. The most recent articles demonstrate expertise in clinical trials methodology, medical statistics, and psychological interventions. Many publications involve randomized controlled trials across diverse areas including eating disorders, body image interventions, respiratory medicine, oncology, and obstetrics. This reflects his commitment to 'Applying Statistics for the Benefit of Society' (ASBOS) through rigorous quantitative methods. Professor White is actively involved in mentoring students and colleagues, describing himself as 'a willing coach or mentor.' His teaching portfolio is diverse, including courses on 'GANSTA's' (Good At Numbers and STAts) for mathematics students, quantitative methods for healthcare programs, and professional development courses through the UWE Graduate School. He expresses particular interest in developing new statistical techniques using stochastic simulation and welcomes potential PhD students to collaborate on these research programs. Among his collaborative efforts, the DARK ARTS initiative stands out as a significant research group focused on randomized trials methodology. This team, which includes Dr. Caterina Gentili, Dr. Jason Anquandah, and Dr. Deirdre Toher, represents a concentrated effort to advance statistical approaches to clinical research design and analysis.

• Environmental Epidemiology
• Biostatistics
• Chemical Mixtures and Health Effects
• +3 more

Nina Lazarevic is a Research Fellow at the Australian National University's Centre of Epidemiology for Policy and Practice within the National Centre for Epidemiology and Population Health. She holds a PhD in environmental epidemiology and biostatistics, along with Masters degrees in biostatistics and environmental science. Prior to her academic career, she worked as an econometrician in banking and market research. Research Fellow, Centre of Epidemiology for Policy and Practice PhD in Environmental Epidemiology and Biostatistics Masters in Biostatistics and Environmental Science Her research focuses on statistical methods in environmental epidemiology, particularly Bayesian nonparametric approaches for analyzing health effects of environmental chemical mixtures and exposome-health associations. Key projects include studies on PFAS exposure impacts on maternal and child health, telehealth policy evaluation, and asbestos-related cancer incidence in the ACT. Nina has published extensively on environmental chemical exposure impacts, including PFAS contamination studies (2023-2024) and prenatal exposure effects on fetal growth (2022). Her methodological work includes simulation studies for chemical mixture analysis (2020). Current projects involve: ACT Asbestos Health Study II: Linked Data Project ANU Telehealth in Primary Care Study PFAS Health Study Her work leverages large-scale linked data to inform government policy on cardiovascular disease prevention and chronic disease management.

Kyle ParadisView profile

Lecturer

• Sport Psychology
• Mental Health
• Group Dynamics
• +4 more

Kyle Paradis is a Lecturer in Sport Sociology/Sport Psychology (Mental Health) at the School of Sport, Faculty of Life & Health Sciences, Ulster University, Jordanstown Campus. His work bridges sport psychology, mental health, and physical activity research, with a focus on longitudinal behavioral studies and depressive disorder correlations. Education: PhD in Psychology (Western University, 2014) Master of Education: (2023) Master of Human Kinetics: (University of Windsor, 2010) Bachelor of Arts (Honours): (Laurentian University, 2008) Research interests include: Sport Psychology Mental Health in Sports Contexts Group Dynamics Longitudinal Behavioral Analysis Physical Literacy Adolescent Movement Behavior Depressive Disorders His recent publications emphasize longitudinal studies of organizational stressors, physical activity's role in mitigating depression, and group dynamics in sports officiating. He actively participates in international conferences and serves on program committees for sport psychology events.