Find Academic Supervisors

Samuel WongView profile

Associate Professor

• Protein Structure Prediction
• Bayesian Inference
• Stochastic Systems
• +2 more

Dr. Samuel Wong is Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. His research develops statistical methods for complex data science problems in protein structure analysis, dynamic systems inference, and materials reliability. Education includes PhD from Harvard Statistics Department (2013). Research addresses challenges in conformational sampling for protein folding, inference for differential equation models, and uncertainty quantification in materials science. Leads development of MAGI software for manifold-constrained Gaussian processes. Publications showcase innovations in Sequential Monte Carlo methods, spatial data fusion, and Bayesian approaches to industrial problems. Recent work focuses on protein structure variability and COVID-19 transmission modeling. Supervises graduate students in Bayesian analysis and computational statistics. Teaches courses including Analysis of Spatial Data and Applied Linear Models.

• Environmental and Resource Economics
• Sustainable Fisheries Management
• Water Resource Management
• +5 more

Joshua Abbott is a Professor and Director of Environmental and Resource Economics at Arizona State University's School of Sustainability. His research focuses on equitable and sustainable governance of natural resources amid environmental change, employing economic modeling and interdisciplinary collaboration. Key areas include sustainable fisheries policy, blue economies, water resource management in arid regions, and valuation of natural capital. Abbott has contributed to NOAA Fisheries and US Fishery Management Council initiatives, emphasizing ecosystem and community resilience. Education : Ph.D. in Agricultural and Resource Economics (Environmental & Resource Economics & Econometrics), University of California, Davis M.A. in Economics, University of Washington, Seattle B.B.A. in Economics, Baylor University Research Interests : Sustainable fisheries governance, water resource valuation, ecological-economic couplings, policy innovation for natural resource management, and green accounting frameworks. Professional Roles : Editor in Chief, Marine Resource Economics Former Co-Editor, Journal of the Association of Environmental and Resource Economists Founding member of the Economics for Sustainability lab group Teaching : Courses include Natural Resource Economics, Statistical Methods, Sustainable Seafood, and the Graduate Certificate in Environmental and Sustainability Economics. His work bridges economic theory and practical policy, addressing challenges such as climate adaptation, biodiversity outcomes, and urban water scarcity. Abbott collaborates with institutions like the Center for Biodiversity Outcomes and the Global Futures Scientists and Scholars network.

• STEM Education
• Mathematics Education
• Interdisciplinary Learning
• +4 more

Professor Lyn English is a leading academic in STEM and mathematics education at Queensland University of Technology's College of Education, Department of Curriculum. Her research focuses on interdisciplinary approaches, particularly integrating engineering and data science into early years education. She has authored or co-edited numerous books and journal articles, emphasizing the role of design-based problem solving and mathematical modeling in fostering critical thinking. English has contributed extensively to curriculum frameworks, including the 'Pattern and Structure Mathematics Awareness Program' (PASMAP), and has explored gender dynamics in mathematics achievement. She collaborates with institutions globally, such as Routledge, Springer, and the Australian Council for Educational Research, to advance educational methodologies. Her work highlights the importance of bridging theory and practice in STEM education. English’s research spans cognitive development in statistical and probabilistic reasoning, foundational mathematical concepts in primary education, and teacher professional development. She advocates for accelerating students' learning through innovative curricula like the RAMR framework and has conducted studies on pedagogical practices in diverse educational settings. Her edited volumes, including Handbook of International Research in Mathematics Education and Ways of Thinking in STEM-based Problem Solving , consolidate cutting-edge perspectives in the field. English’s contributions to conferences and symposia underscore her commitment to advancing STEM integration and addressing challenges in mathematics education research.

Maria Cristina MoralesView profile

Professor

• Sociology of Latinxs
• U.S.-Mexico Border Studies
• Structural Inequality
• +4 more

Maria Cristina Morales is a Professor in the Department of Sociology & Anthropology at the University of Texas at El Paso, College of Liberal Arts . Her research critically examines how Latinx ethnicity intersects with social statuses and institutions, particularly along the U.S.-Mexico border , using quantitative and qualitative methodologies while prioritizing reflexive and theory-driven approaches . Education: PhD in Sociology, Texas A&M University (2004) Research Focus includes structural violence at the U.S.-Mexico border, environmental hazard perceptions near border ports, and gender/sexuality dynamics . Her publications and presentations bridge border studies, immigration, labor rights, and environmental justice . Recent projects include decolonizing survey research and Latinxs on the Margins (forthcoming). Scientific Awards include the Norma Williams Distinguished Service Award (2024), UT Regents Outstanding Teaching Award (2016), and multiple recognitions from the Southwestern Sociological Association and Building Scholars programs. Grants highlight her leadership in National Science Foundation and North American Development Bank funded projects addressing sociological issues, air quality assessment , and data accessibility for vulnerable communities.

Margaret BeckView profile

Professor

• Partial Differential Equations
• Dynamical Systems
• Nonlinear Waves
• +4 more

Margaret Beck is a Professor and former Director of Undergraduate Studies in the Department of Mathematics and Statistics at Boston University. She holds a PhD from Boston University and has held postdoctoral positions at the University of Surrey, MSRI, and Brown University. Her research focuses on partial differential equations and dynamical systems, particularly analyzing the long-time behavior of solutions and stability of nonlinear waves. She has received prestigious awards including the 2019 SIAM J.D. Crawford Prize and the 2018 AMS Birman Fellowship. Education: Bachelor's degree from Colorado College PhD in Mathematics from Boston University Research Interests: Nonlinear waves and coherent structures Spectral and nonlinear stability analysis Pattern formation in dissipative systems Applications to fluid dynamics and mathematical physics Awards: 2019 SIAM J.D. Crawford Prize 2018 AMS Birman Fellowship 2012 Sloan Research Fellowship Advising: Supervised 11 graduate students and postdocs, including Montie Avery and Jonathan Jaquette. Actively mentors through the AWM Mentor Network. Current advising handled by Prof. Matt Szczesny during her 2025 sabbatical. Labs/Community: Co-organizes the GeMs group supporting gender minorities in mathematics at BU and co-founded the GeMsGetMath@BU summer math camp for high school students. Engages in initiatives promoting diversity in STEM, including presentations at EDGE and WISE@Warren programs.

Andrea Meilán-VilaView profile

Assistant Professor

• Nonparametric Estimation
• Directional Statistics
• Kernel Smoothing
• +3 more

Andrea Meilán-Vila is an Assistant Professor in the Department of Statistics at Universidad Carlos III de Madrid since 2021, holding a Juan de la Cierva Fellowship since 2023. She earned her PhD in Statistics from Universidade da Coruña (2021) and previously served as a Postdoctoral Fellow at Universidade de Santiago de Compostela's Department of Statistics, Mathematical Analysis and Optimisation. Her research focuses on nonparametric methods for analyzing complex data types, including directional, spatial, and functional data. Key areas include kernel smoothing techniques, goodness-of-fit testing for regression models, and spatial trend estimation. She serves as an Associate Editor for the Journal of Nonparametric Statistics . Recent work emphasizes applications in climate science (temperature curve modeling), fluid dynamics (wake flow control), and biomedical imaging (hippocampus shape analysis). Her methodologies address challenges like sparse data estimation and spatial correlation in regression frameworks. Key Projects: STENED (Stein-based goodness-of-fit tests for non-Euclidean data) Awards: Juan de la Cierva Fellowship (2023) Publications span journals like Journal of Fluid Mechanics , Statistical Papers , and TEST , with a focus on methodological advancements in statistical modeling and computational validation.

• Algorithms for Big Data Analytics
• Computational Geometry
• Machine Learning
• +4 more

Jeff M Phillips is a Professor in the Kahlert School of Computing at the University of Utah, specializing in algorithms for big data analytics, computational geometry, and machine learning. He holds a BS in Computer Science and Mathematics from Rice University (2003) and a PhD in Computer Science from Duke University (2009). He serves as Director of the Utah Center for Data Science, Director of the Data Science Program in the Kahlert School of Computing, and Faculty Co-Director of the One U Data Science Hub. His research focuses on geometric data analysis, coresets, sketches, and handling uncertainty in data. Education: BS/BA (Rice University, 2003), PhD (Duke University, 2009) CI Postdoctoral Fellow at University of Utah (2009–2011) His research interests include algorithms for big data analytics, computational geometry, machine learning, spatial statistics, and AI. He has led NSF-funded projects on spatial data analysis, cosmic origins via AI, and reactive flow data modeling. Phillips has advised numerous PhD and master’s students, contributing to topics like trajectory classification and bias mitigation in word embeddings. His publications span computational geometry, data science, and machine learning. Notable work includes coresets for kernel density estimates, bias mitigation in language models, and scalable spatial scan statistics. Phillips is also active in academic service, serving as co-PC chair for SoCG 2024 and on program committees for major conferences like NeurIPS and ICML.

Tove HelsView profile

Associate Professor

• Traffic Safety
• Transportation Engineering
• Road User Behavior
• +7 more

Tove Hels is an Associate Professor in the Department of the Built Environment at the Faculty of Engineering and Science, Aalborg University, Denmark. She is a key member of the Traffic Research Group, focusing on transportation safety, road user behavior, and traffic policy. Her work bridges engineering, public health, and social science to improve road safety outcomes. Research Interests: Her research spans traffic safety, cyclist-motorist interactions, speed enforcement, accident risk modeling, and the impact of vehicle technologies. She investigates both infrastructure design and behavioral factors influencing road safety, with a strong emphasis on data-driven analysis and policy evaluation. The recent publications reveal a consistent focus on improving cyclist safety through infrastructure (e.g., advanced stop boxes, roundabout design), addressing under-reporting of traffic injuries, and evaluating behavioral interventions for speeding. Her work integrates epidemiological methods, statistical modeling, and real-world policy applications. Scientific Contributions: Principal Investigator in the Danmarks Trafikulykker cohort study (2025–2029) Project participant in EASE: Intervention against speed offenders (2019–2025) Author of over 29 research outputs including journal articles, reports, and policy briefs Frequent contributor to national media and advisory bodies like Dansk Vejforening Advising and Grants: While no formal students are listed, her leadership in major funded research projects indicates a supervisory role in training junior researchers. She has secured and contributed to significant research grants related to traffic safety monitoring and intervention evaluation. Labs and Teams: She is part of the Traffic Research Group at Aalborg University, collaborating closely with researchers such as H. Lahrmann, T.K.O. Madsen, and A.V. Olesen. The group conducts field studies, data linkage projects, and policy evaluations with national impact.

• Large-scale nonparametric and kernel methods
• Robust and trustworthy machine learning
• Multiresolution data and data fusion
• +3 more

Professor Dino Sejdinovic is a faculty member in the School of Computer and Mathematical Sciences at the University of Adelaide, part of the Faculty of Sciences, Engineering and Technology. Previously, he held positions as Lecturer and Associate Professor at the University of Oxford's Department of Statistics (2014–2022). His academic qualifications include a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006). His research focuses on the intersection of statistical methodology and machine learning, encompassing large-scale nonparametric methods, robust machine learning, multiresolution data fusion, and measures of dependence. He has contributed to kernel methods, Bayesian inference, causal discovery, and applications in climate science, quantum computing, and social science data analysis. Education: PhD in Electrical and Electronic Engineering, University of Bristol (2009) Diplom in Mathematics and Theoretical Computer Science, University of Sarajevo (2006) Sejdinovic's work emphasizes bridging theoretical foundations with practical applications, such as cloud type classification using vision transformers and machine learning-driven quantum device optimization. His recent publications explore topics like kernel-based causal inference, Bayesian neural networks, and uncertainty quantification in statistical models. Advising and grants: Eligible to supervise Masters and PhD students in machine learning and statistics, though specific grants or student advisees are not explicitly listed in the provided texts.

Noel CressieView profile

Professor

• Spatial Statistics
• Environmental Informatics
• Bayesian Statistics
• +3 more

Noel Cressie is a Distinguished Professor of Statistics at the University of Wollongong (UOW), Australia, affiliated with the School of Mathematics and Applied Statistics and the National Institute for Applied Statistics Research Australia (NIASRA). He is also the Director of the Centre for Environmental Informatics (CEI). His academic journey includes a PhD from Princeton University (1975) and a B.Sc. with First Class Honours from the University of Western Australia (1972). His research focuses on spatial and spatio-temporal statistics, Bayesian methods, environmental informatics, and applications in climate science. Notable projects include work on atmospheric CO2 flux inversion (WOMBAT framework), Antarctic environmental research (SAEF initiative), and statistical remote sensing for NASA. He has secured over $20 million in research funding and authored four influential books, including Statistics for Spatial Data . Cressie has received prestigious awards such as the COPSS R.A. Fisher Award (2009), Pitman Medal (2014), and Fellowship of the Australian Academy of Science (2018). He leads interdisciplinary teams addressing global challenges like carbon cycle dynamics and biodiversity modeling. His contributions to statistical methodology and environmental science have been recognized through international collaborations and advisory roles.

Alexandros TaflanidisView profile

Professor

• Uncertainty Quantification
• Disaster Risk Reduction
• Bayesian Model Updating
• +3 more

Professor Alexandros Taflanidis holds a concurrent faculty position as Professor in the Department of Civil and Environmental Engineering and Earth Sciences and the Department of Aerospace and Mechanical Engineering at the University of Notre Dame's College of Engineering. He serves as the Director of Graduate Studies for CEEES. His research focuses on uncertainty quantification, disaster risk reduction, Bayesian model updating, and enhancing the sustainability and resilience of civil infrastructure systems, particularly in natural hazard contexts like hurricanes and earthquakes. His work integrates computational statistics and surrogate modeling to improve real-time emergency response and long-term risk mitigation strategies. Prof. Taflanidis earned a Ph.D. from the California Institute of Technology (2007), and M.S. and B.S. degrees in Civil and Environmental Engineering from Aristotle University of Thessaloniki (2003 and 2002). He leads projects such as the Coastal Hazards System (CHS) for Louisiana and Puerto Rico, advancing probabilistic coastal hazard analysis frameworks. His research also explores storm surge emulation, seismic response estimation, and innovative protective device designs for structures. He won the ASCE Huber Prize for his contributions to community resilience through scientific computing. His collaborative efforts include advancing machine learning for data imputation in coastal hazards and developing lifecycle assessment workflows for resilient buildings. Current research trends in his publications emphasize computational efficiency, multi-fidelity modeling, and adaptive strategies for real-time predictions. Prof. Taflanidis's work bridges academic and practical domains, addressing challenges such as climate change impacts on coastal regions and earthquake early warning systems. His lab focuses on integrating interdisciplinary approaches to create actionable solutions for infrastructure resilience.

Fariba KarimiView profile

Professor

• Algorithmic Fairness
• Computational Social Science
• Network Science
• +7 more

Fariba Karimi is a Professor of Social Data Science at Graz University of Technology and leads the Algorithmic Fairness research group at the Complexity Science Hub (CSH) in Vienna. Her work sits at the intersection of computational social science, network science, and AI ethics, with a strong focus on fairness, inequality, and bias in algorithmic systems. She holds a PhD in Physics and Computational Science from Umeå University, Sweden (2015), and was a Postdoctoral Researcher at GESIS – Leibniz Institute for Social Sciences, Germany. Her research combines large-scale data analysis, agent-based modeling, and network science to study how social structures and algorithms interact to shape disparities, particularly for underrepresented groups. Computational Social Science Algorithmic Fairness and Bias Network Science and Homophily Digital Humanism Urban Inequality and Implicit Bias Gender and Age Equity in Academia Her recent publications reveal a consistent focus on understanding and mitigating algorithmic and structural inequities. Themes include the visibility of minorities in networks, the impact of segregation on health and cognition, gender citation gaps in physics, and the development of fair ranking and recommendation systems. She employs interdisciplinary methods to analyze both digital and real-world social systems. Fariba Karimi has received several prestigious recognitions, including: Young Scientist Award from the German Physical Society (2023) ERC Starting Grant (2024) Nomination for the Hedy Lamarr Award (2021) She leads the 'Humanized Algorithms' project at CSH, funded by the ERC, and co-leads an EU Horizon project (MAMMOth) on multi-criteria fairness in AI. She advises PhD researchers such as Lisette Espín-Noboa and collaborates with institutions like the University of Mannheim and the Centre for Social Sciences. Her work contributes to both academic knowledge and practical solutions for fairer AI systems and more equitable societies.

Renaud LambiotteView profile

Professor

• Network Science
• Complex Systems
• Temporal Networks
• +7 more

Renaud Lambiotte is Professor of Networks and Nonlinear Systems at the Mathematical Institute, University of Oxford. He holds a PhD in Physics from Université libre de Bruxelles and has held research and faculty positions at ENS Lyon, Université de Liège, UCLouvain, Imperial College London, and the University of Namur. He is currently an active academic in applied mathematics and network science. His research focuses on complex systems, particularly dynamics on networks, temporal networks, and stochastic processes. He applies these to social and brain networks, data mining, and urban systems. His work bridges theoretical modeling and real-world data, emphasizing the structure and evolution of complex systems. His recent publications demonstrate strong trends in network theory, including hypergraphs, community detection, multidimensional dynamics, and data quality in network interventions. He also explores applications in urban air quality and gentrification, showing a commitment to socially relevant complex systems research. Scientific Awards: Prix Wernaers 2013 Prix Wernaers 2016 Prix Wernaers 2020 Verdickt-Rijdams 2016 de l'Académie royale de langue et de littérature françaises He is the co-founder of L’Arbre de Diane, a publishing initiative at the science-literature interface, which received multiple awards. He teaches advanced courses such as Differential Equations II and Networks. He is affiliated with the Machine Learning and Data Science and the Oxford Centre for Industrial and Applied Mathematics research groups. He has authored or co-edited key texts in the field, including A Guide to Temporal Networks and Modularity and Dynamics on Complex Networks , and has published around 130 peer-reviewed articles. His research is supported by ongoing collaborations and active publication output, indicating sustained academic leadership.

• Public Policy
• Public Management
• Public Sector Innovation
• +12 more

Ivy Liu is an Assistant Professor in the Department of Public Policy Leadership at the University of Mississippi, where she contributes to research and teaching in public policy and management. Her work focuses on public sector innovation, policy learning, and crisis response across state and local governments. Her research interests include: Public sector innovation and economic development Policy diffusion and intergovernmental learning Disaster and public health emergency response Executive leadership in policy-making Advanced quantitative methods in public administration Dr. Liu's recent research employs synthetic control methods, network analysis, and spatial econometrics to evaluate policy impacts. Her publications span topics such as mask policy termination during the pandemic, disaster relief allocation, and corporate tax policy effects on employment, reflecting a strong trend toward rigorous empirical analysis of timely and complex public policy challenges. She has received research support from the CDC’s Public Health Extreme Events Research (PHEER) initiative. Dr. Liu is actively engaged in the academic community, serving on the American Political Science Association’s Section on Public Policy committee and reviewing for top journals in the field. She teaches undergraduate courses in policy research methods and policy design. Her professional affiliations include: American Political Science Association American Society for Public Administration Association for Public Policy Analysis and Management Public Health Extreme Events Research Center Public Management Research Association Dr. Liu holds a Ph.D. in Public Policy and Management from The Ohio State University, with graduate minors in economics and statistics.

Anders Rønn-NielsenView profile

Associate Professor

• Applied Probability Theory
• Lévy-Based Modelling
• Statistical Methods in Efficiency Analysis
• +3 more

Anders Rønn-Nielsen is an Associate Professor at the Department of Finance and Center Coordinator at the Center for Statistics at Copenhagen Business School (CBS). He holds a M.Sc. in Statistics from University of Copenhagen and a PhD in Statistics and Probability Theory from Aarhus University. His research focuses on applied probability theory, particularly Lévy-based spatial models, and statistical efficiency analysis. His academic credentials include: M.Sc. in Statistics, University of Copenhagen PhD in Statistics and Probability Theory, Aarhus University Research interests span: Lévy processes and spatial stochastic modeling Extreme value theory applications in finance and natural sciences Nonparametric production frontier analysis Efficiency measurement methodologies His publications (18+ articles) emphasize theoretical probability and statistical applications in efficiency analysis. He has served as external examiner at Aarhus University and Copenhagen University for master’s and PhD examinations (2017–2019). His teaching responsibilities include advanced probability theory courses and statistical methods training for economics students.