Find Academic Supervisors

• Pharmacoepidemiology
• Epidemiology
• Observational Research
• +9 more

D.L. (Daniala) Weir is an Assistant Professor at Utrecht University's Science college, specifically within the Pharmacoepidemiology and Clinical Pharmacology department. Her research bridges clinical pharmacology , epidemiology , and applied data science to enhance medication safety for patients with multimorbidity. Education: B.Sc. in Biological Sciences (University of Alberta, 2012) M.Sc. in Epidemiology (University of Alberta, 2014) Ph.D. in Epidemiology (McGill University, 2019) Postdoctoral Fellowship (University of Toronto, 2021) Her work combines causal inference frameworks with machine learning to address clinical challenges using real-world data from Canada, the UK, and the Netherlands. She collaborates across disciplines with biostatisticians , data scientists , and healthcare professionals , emphasizing patient-centered approaches through engagement with patients and caregivers. Key research areas include: Dynamic treatment regimens Medication-related harm prevention Adverse drug event detection mHealth applications for adherence Her publications demonstrate expertise in applied data science methodologies for time-to-event analysis , predictive modeling , and multimorbidity treatment optimization .

• Event Cognition
• Human-Robot Interaction
• Visual Working Memory
• +4 more

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 ArroyoView profile

Assistant Professor

• statistical network analysis
• machine learning
• high-dimensional data analysis
• +1 more

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.

Kangjie ZhouView profile

Research Fellow

• High-dimensional statistics
• Non-convex optimization
• Probability theory
• +3 more

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.

Vinitra SwamyView profile

Researcher

• Explainable AI
• Machine Learning for Education
• Multimodal Learning
• +4 more

Vinitra Swamy is a Postdoctoral Researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the ML4ED Lab (Machine Learning for Education) and the MLO Lab (Machine Learning and Optimization Group). She holds a PhD in Computer Science from EPFL and a Master's and Bachelor's from UC Berkeley, graduating at 20 as the youngest recipient in UC Berkeley's history. Her research focuses on explainable AI, human-centric machine learning, and education technology. She has held roles as a lead engineer at Microsoft AI (ONNX framework) and served as a lecturer at UC Berkeley and UW Seattle. Vinitra's work bridges technical innovation with societal impact, exemplified by projects like MEDITRON-70B (medical LLMs) and iLLuMinaTE (actionable explanations for students). She has received multiple awards, including the 2024 G-Research PhD Prize and Rising Stars in Data Science recognition. Key contributions include interpretable neural architectures (InterpretCC), bias analysis in LLMs, and multimodal learning systems (MultiModN). Her research emphasizes practical applications in education and healthcare, with a focus on user-centered design and ethical AI practices.

Guang ChengView profile

Professor

• Generative Data Science
• Trustworthy AI
• Machine Learning Theory
• +5 more

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 ShaferView profile

Professor

• Probability Theory
• Game-Theoretic Probability
• Statistics
• +4 more

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.

Andy CharlwoodView profile

Professor

• HR and people analytics
• AI and algorithmic management
• Healthcare workforce management
• +2 more

Professor Andy Charlwood is a faculty member at the University of Leeds, affiliated with Leeds University Business School's Department of Management and Organisations. His primary research focuses on HR analytics, algorithmic management in HR, and healthcare workforce dynamics, with particular emphasis on how digital technologies transform HRM practices and worker experiences. He leads interdisciplinary research combining sociology, psychology, and economics approaches. His research interests include people analytics adoption, AI in human resources, healthcare workforce optimization, and digital transformation impacts on employment relations. Current projects examine relationships between workforce factors and care quality using machine learning methods. Professor Charlwood teaches HRM, strategic HRM, people analytics, and organizational studies at undergraduate and postgraduate levels. He has supervised numerous PhD students in areas including people analytics adoption, AI in HR, and healthcare workforce studies. Notable grants include funding from the UK National Institute for Health Research and collaborations with the HR Analytics Think Tank. He serves as Associate Editor for the International Journal of Human Resource Management and holds leadership positions in the British Academy of Management. His work has contributed significantly to understanding digital transformation in HR and labor relations in healthcare contexts.

Stav ZalelView profile

Assistant Professor

• Quantum Gravity
• Causal Set Theory
• Combinatorics
• +3 more

Dr. Stav Zalel is a College Assistant Professor and Fellow at Homerton College, University of Cambridge, affiliated with the Department of Applied Mathematics and Theoretical Physics (DAMTP). Their research focuses on quantum gravity, causal set theory, combinatorics, and quantum field theory on discrete backgrounds. They previously held postdoctoral positions at Imperial College London (2021–2024). Research Interests: Zalel explores foundational questions in quantum gravity using causal set theory, a discrete approach to spacetime. Their work includes developing covariant dynamics for causal sets, analyzing growth models, and applying combinatorial methods to discrete spacetime structures. They also investigate observables in cyclic cosmological models and non-standard spacetime geometries. Publications Highlight: Recent works address covariant frameworks for causal set dynamics, growth models with applications to quantum gravity, and algebraic structures (e.g., Hopf algebras) in combinatorial spacetime models. These contributions advance understanding of discrete spacetime theories and their mathematical underpinnings. Zalel is part of the Relativity and Gravitation research group at DAMTP, contributing to interdisciplinary efforts at the intersection of mathematics and theoretical physics.

Stephen QuackenbushView profile

Professor

• International Conflict
• Deterrence Theory
• Strategic Studies
• +4 more

Stephen L. Quackenbush is a Professor and Director of Defense and Strategic Studies at the Truman School of Government and Public Affairs, University of Missouri. His expertise spans international conflict, deterrence theory, strategic studies, and bridging academic-policy divides. He created the MA in Defense and Strategic Studies program and previously served as President of the International Studies Association-Midwest. Education: Bachelor of Science (BS) in Aerospace Engineering, Illinois Institute of Technology Master of Science (MS) in Aerospace Engineering, Georgia Institute of Technology Master of Arts (MA) and Doctor of Philosophy (PhD) in Political Science, University at Buffalo, SUNY Research Interests: General and extended deterrence mechanisms Gray zone conflicts and geopolitical strategy Counterinsurgency dynamics and military effectiveness Policy applications of strategic studies His recent work emphasizes updating conflict datasets (e.g., Politically Active Dyads) and addressing accidental warfare risks. He has authored influential books like Understanding General Deterrence and International Conflict: Logic and Evidence . Awards: Recipient of the Bronze Star Medal for service during Operation Iraqi Freedom while in the U.S. Army. Led initiatives in curriculum development (MA program creation) and contributed to defense policy debates through game-theoretic models of extended deterrence in Northeast Asia and Middle Eastern conflicts.

• Psychosis
• Childhood Trauma
• Mental Health Interventions
• +4 more

Dr. Amanda Woodrow is a Research Fellow at Edinburgh Napier University's School of Health and Social Care, specializing in mental health research with a focus on psychosis, trauma, and decision-making impairments. Her work investigates causal pathways between childhood trauma and psychosis, alongside developing interventions to address symptoms in clinical settings. She holds a PhD from Edinburgh Napier University (2021) and has contributed to multi-site trials such as DEC:IDES and PICASSO, funded by the Chief Scientists Office. These projects involved collaborations with NHS regions and universities including Glasgow, Lancaster, and Manchester. Her research interests include complex PTSD in psychotic populations, emotion regulation, and thought disorder. She has published on decision-making deficits in psychosis and mental health interventions for justice-involved women. Dr. Woodrow is also a core researcher in the Scottish COVID-19 Public Inquiry, analyzing pandemic impacts on adult and child social care systems. Current and past projects include supervising doctoral candidates exploring implicit self-esteem's role in paranoia and cultural influences on first responders' psychological well-being. She actively participates in the Centre for Mental Health Practice, Policy and Law Research group.

• Recommender Systems
• Context-aware Recommendation
• Explainable AI
• +5 more

Markus Zanker is a Professor in the Department of Knowledge Engineering within the Faculty of Computer Science at Free University of Bozen-Bolzano, Italy. With an extensive publication record spanning over two decades, he has established himself as a leading expert in recommender systems research, with particular expertise in context-aware, group, and knowledge-based recommendation approaches. His work bridges theoretical foundations with practical applications across diverse domains including tourism, healthcare, and e-commerce. Zanker's research interests center on advancing the theoretical underpinnings of recommender systems while addressing practical challenges in real-world deployments. His recent work has focused on causal decision-making frameworks for recommendation, intent-aware systems, and the integration of generative AI in group recommendation scenarios. He has made significant contributions to explainable recommendation systems, medical recommendation applications, and tourism recommendation systems, demonstrating both theoretical rigor and practical impact. His recent publication portfolio reveals a strong trend toward addressing fundamental challenges in recommendation science, including the growing emphasis on causal reasoning to move beyond correlation-based approaches, the integration of generative AI capabilities for more sophisticated group decision support, and the development of frameworks for ethical and socially beneficial recommendation systems. His work increasingly bridges the gap between traditional recommendation algorithms and emerging AI paradigms while maintaining focus on real-world applicability. Zanker is actively involved in the academic community as a workshop organizer, having co-chaired the Knowledge-aware and Conversational Recommender Systems (KaRS) workshop and the Recommenders in Tourism (RecTour) workshop for multiple consecutive years at major conferences including RecSys. His research has been supported through collaborations with numerous international researchers and institutions, focusing on both theoretical advances and practical implementations of recommendation technology across multiple application domains.

Yang PeiView profile

Research Fellow

• International Economics
• Environmental Economics
• Chinese Economy
• +3 more

Yang Pei is a Postdoctoral Research Fellow at the Institute of Nuclear and New Energy Technology (INET), Tsinghua University. He received his Ph.D. in Economics from the University of Houston in 2025, focusing on interdisciplinary research at the intersection of international trade, environmental policy, and China's economic development. His academic background: Ph.D. in Economics, University of Houston, 2025 Pei's research analyzes spatial economic-environmental interactions through structural trade models, macroeconomic frameworks, and advanced econometric techniques including time-series and reduced-form analyses. His work quantifies causal relationships in areas like provincial border effects on welfare, trade-GDP ratio dynamics, and demographic influences on economic growth, with direct policy implications for China's development strategy. He actively disseminates findings through major conferences including the 2024 Chinese Economic Association Conference (Hangzhou), 2024 Midwest International Trade Conference (Indianapolis), and 2023 Midwest Macroeconomics Conference (Lubbock), demonstrating engagement with leading economic research communities.

Claudia KlüppelbergView profile

Professor

• Applied Probability
• Mathematical Statistics
• Financial Risk Management
• +3 more

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.

• Economics
• Demography
• Public Health
• +6 more

Simon Chan is an Adjunct Lecturer at the School of Engineering, Department of Electrical, Electronic and Computer Engineering, affiliated with The University of Western Australia. His research spans economics, demography, public health, and environmental studies with a focus on historical demography, gender disparities, and policy impacts. Research interests include the long-term effects of environmental exposures (e.g., DDT), education policy on fertility, and sociological dynamics like sex ratios and conflict impacts. His work combines quantitative methods with interdisciplinary approaches, often leveraging historical data and policy analysis. Recent articles explore topics such as entrepreneurship origins in Australia’s convict history, mortality linked to sex ratios, and behavioral responses to environmental pollutants. His findings contribute to debates on public health, education reform, and social policy design. No scientific awards or grants are explicitly listed in the provided materials. No student advising or lab affiliations are noted.