Find Academic Supervisors

Eoin O'GormanView profile

Senior Lecturer

• Global Change Ecology
• Food Web Structure and Stability
• Climate Change Effects on Ecosystems
• +3 more

Dr. Eoin O'Gorman is a Senior Lecturer in the School of Life Sciences at the University of Essex. His research investigates the impacts of global change across multiple levels of ecological organisation, from individual metabolism to ecosystem processes, with a particular focus on food web dynamics and body size as a functional trait. BSc, University College Cork (2004) PhD, University College Cork (2009) Current research projects include: Warming effects on trophic interactions Anthropogenic stressors in marine and freshwater systems Body size scaling in ecological networks Food web stability under climate change Recent work demonstrates how warming alters plankton body-size distributions (2025), simplifies freshwater food webs through multiple stressors (2025), and reduces trophic diversity in high-latitude ecosystems (2024). His studies span marine, freshwater, and terrestrial habitats, seeking universal responses to environmental change. Current grants include: 2024 WebDNA: Food web reconstruction through environmental DNA analysis (Leverhulme Trust) 2023 Predicting Impacts of Global Environmental Change on Ecological Networks (NERC) 2022 ORBIT: Offshore Renewables and Benthic Communities (NERC) Supervises PhD candidates studying: Zelin Chen: Offshore structures' impacts on North Sea biodiversity Patrick Eskuche Keith: Southern Ocean food web dynamics Anamika Poyil: Environmental biology of marine communities

• Electromagnetics
• Nonlinear Optics
• High-Performance Computing
• +6 more

Mohammad Zunoubi is an Associate Professor in the Department of Engineering Programs at SUNY New Paltz, part of the School of Science & Engineering. He holds an undergraduate degree from the University of Mississippi and a postgraduate degree from the University of Illinois at Urbana-Champaign. His research focuses on High-Performance Computing solutions for electromagnetic problems, Nonlinear Optics, and Microwave/Antenna Design. He has conducted extensive work in Computational Electromagnetics, utilizing methods like FDTD and FEM. His teaching interests span Electromagnetics, Numerical Methods, and EMC/EMI. He has been recognized with multiple awards, including the SUNY Provost’s Research Award (2005) and several United States Air Force Summer Faculty Fellowships (2009–2017). His research often involves collaboration with the Air Force, focusing on advanced electromagnetic modeling and high-power microwave effects. Dr. Zunoubi’s publications highlight contributions to antenna design, electromagnetic compatibility, and high-performance computing techniques. His work bridges theoretical electromagnetics with practical applications in aerospace, medical physics, and material science.

Nicholas KevlahanView profile

Professor

• Applied Mathematics
• Computational Fluid Dynamics
• Geophysical Fluid Dynamics
• +5 more

Dr. Nicholas Kevlahan is a Professor in the Department of Mathematics and Statistics at McMaster University. He holds a BSc in Physics from the University of British Columbia (1985–1989), a PhD in Applied Mathematics from the University of Cambridge (1990–1994), and completed a Marie Curie postdoctoral fellowship at École Normale Supérieure in Paris (1998). He has held visiting positions at institutions including Université Grenoble-Alpes, INRIA, and the University of Cambridge. His research focuses on advanced computational and mathematical methods for fluid dynamics, including the development of the WAVETRISK code for adaptive climate modelling, data assimilation techniques, fluid-structure interaction, and compressive sampling. His work integrates interdisciplinary approaches across applied mathematics, geophysics, and engineering. Key research areas include geophysical fluid dynamics, numerical analysis, partial differential equations, and wavelet-based methods. He has published extensively on topics such as ocean circulation models, turbulence simulation, and adaptive numerical methods. Dr. Kevlahan teaches courses in numerical methods, differential equations, asymptotic analysis, and mathematical physics. He mentors a diverse group of PhD, MSc, and BSc students in his active research laboratory.

Ching-Yao LaiView profile

Assistant Professor

• Geophysics
• Climate Science
• Fluid Dynamics
• +3 more

Ching-Yao Lai is an Assistant Professor of Geophysics at Stanford University, leading the Lai Research Group. His work integrates mathematical and machine-learned models with observational data to study ice dynamics, geophysics, and fluid mechanics across vast spatial scales. Key focuses include understanding ice-sheet behavior under climate change, fluid-elastic interactions, and interdisciplinary collaborations. He holds a Ph.D. (2018, Princeton University) in Mechanical and Aerospace Engineering and a B.S. (2013, National Taiwan University) in Physics. Research interests span ice dynamics, climate science, and fluid mechanics, with emphasis on machine learning applications to uncover missing physics in ice-sheet models. Notable contributions include discovering self-similar blow-up solutions for Euler equations and developing physics-informed neural networks. His work bridges theory and observation, addressing global challenges like ice-sheet vulnerability. Scientific awards include the 2024 Sloan Research Fellowship and 2023 Google Research Scholar Award. He leads NSF-funded projects on singularities in incompressible flows and Greenland meltwater pathways. Advising highlights include mentoring students like Yongji Wang (Science publication, 2025) and Yuno Iwasaki (2024 Soros Fellow). Labs/Teams: Lai Research Group at Stanford, collaborating across geophysics, engineering, and computer science. Active in open science, with a YouTube channel and Google Scholar profile.

• Mathematics Education
• Quantitative Research Methods
• Educational Attitudes
• +4 more

Dr. Maria Pampaka is a Professor at the University of Manchester, holding joint appointments in the Manchester Institute of Education and the Department of Social Statistics. Her research focuses on mathematics education, particularly learners’ attitudes and dispositions, advanced quantitative methods, and the application of complex systems theory. She has led projects on mathematics anxiety, gender equality measurement, and longitudinal survey analysis. Pampaka employs methodologies like Rasch modeling, missing data imputation, and agent-based simulations. She has collaborated internationally on studies ranging from regional gender gaps to tourist behavior analysis. Her work contributes to UN Sustainable Development Goals related to quality education and gender equality. Her research interests span mathematics education, quantitative research methods, and social statistics. Notable projects include a systematic review of mathematics anxiety and the development of the Extended Regional Gender Gaps Index (eRGGI). She has also explored applications of machine learning in tourism and healthcare, such as predicting revisit intentions and optimizing hospital resource allocation during the pandemic. Projects: Systematic review of mathematics anxiety, eRGGI development, analysis of sexual assault survivor support needs, and requirements discovery for smart driver assistive technologies. Key methodologies: Rasch modeling, multilevel modeling, thematic analysis, agent-based simulations, and machine learning. Collaborations: Cross-disciplinary work with social scientists, engineers, and healthcare professionals internationally. Pampaka’s work bridges education, statistics, and policy, aiming to inform equitable practices in learning and societal structures. She is engaged in impact initiatives like the Good Behavior Game evaluation and policy-informed gender equality research.

Jennie E. BrandView profile

Professor

• Social Stratification
• Inequality and Mobility
• Social Demography
• +3 more

Jennie E. Brand is a Professor of Sociology and Professor (by courtesy) of Statistics and Data Science at the University of California, Los Angeles (UCLA). She co-directs the Center for Social Statistics (CSS) and serves as President of the Association of Population Centers (APC). Her affiliations include the Population Association of America (PAA), International Sociological Association (ISA), and editorial boards of Social Forces , Sociological Methodology , and Science Advances . Research Interests include social stratification, inequality, causal inference, and the socioeconomic impact of higher education. She explores Access to and returns from higher education Consequences of job displacement and disruptive events Machine learning applications in social science Intergenerational mobility and family effects Scientific Awards include the ASA Mathematical Sociology Outstanding Publication Award (2024), Sociological Research Association membership (2019), and the ASA Methodology Leo Goodman Mid-Career Award (2016). Grants awarded by NICHD, NIH, NSF, and Robert Wood Johnson Foundation support her work on population research, causal inference, and health disparities. She has mentored numerous graduate students, including Ph.D. recipients at NYU, Stanford, and Oxford.

Ann WallinView profile

Senior Lecturer

• Consumer decision making
• Brand signaling and portfolio strategy
• Marketing education
• +3 more

Dr Ann Wallin is a Senior Lecturer in the Marketing Discipline at the UQ Business School within the Faculty of Business, Economics and Law at The University of Queensland. Previously a Senior Project Manager at a global market research firm, she has expertise in industries including Financial Services, Automotive, and Aged Care. She holds a Bachelor, Bachelor (Honours), and PhD from The University of Queensland. Research Interests: Ann focuses on consumer decision making, brand signaling, marketing education, discrete choice experiments, structural choice modeling, and survey research. Her work bridges theoretical consumer behavior with practical educational methodologies. Research Trends: Her publications span tourism behavior during crises (e.g., pandemic impacts on cruising), methodological innovations in marketing education (e.g., improving quantitative research practices), and structural choice modeling in consumer decisions. Recent work explores food authenticity perceptions in post-travel behavior and the role of social norms in charitable giving. Awards & Grants: No specific awards mentioned, though she secured funding (2014–2015) for research on retirement choice metrics. Supervision includes associate advising on two PhDs: 'The Effect of Social Media Complaint Management' and 'Food for Thought: Taste Marketing in Service Settings'. Labs/Teams: Engaged with curriculum development initiatives and education-focused research collaborations, emphasizing student-partnered learning approaches.

Jie LiView profile

Professor

• Mineral Physics
• Geochemistry
• Petrology
• +3 more

Jie Li is the Rodney C. Ewing Collegiate Professor of Earth and Planetary Sciences and Professor of Earth and Environmental Sciences at the University of Michigan. She holds a Ph.D. in Earth and Planetary Sciences (Harvard University, 1998) and an M.A. in Geophysics (Harvard University, 1997). Her research focuses on Earth and planetary materials under extreme conditions, leveraging high-pressure techniques like diamond-anvil cells and synchrotron facilities. Key interests include terrestrial planet evolution, core composition, and dynamics of planetary interiors. Recent studies explore light elements in Earth’s core, Mercury’s magnetic field origin, and early Earth crust formation. Her work bridges experimental geochemistry, mineral physics, and computational modeling to understand planetary interiors. Notable contributions include pioneering studies on iron spin states in the lower mantle and the role of metallic melts in mantle dynamics. She collaborates globally, advancing methodologies for high-pressure experiments. Her research has been featured in outlets like Mashable India , highlighting her insights on Earth’s core dynamics and planetary science. Li’s lab emphasizes interdisciplinary approaches to unraveling the origins of planetary materials and their evolution over geological time.

Peter ConstantinView profile

Professor

• Mathematical Physics
• Applied Mathematics
• Fluid Dynamics
• +4 more

Peter Constantin is a Professor in the Department of Mathematics at Princeton University, specializing in mathematical physics and applied mathematics. His research focuses on fluid dynamics, partial differential equations, and nonlinear systems, with particular emphasis on Navier-Stokes equations, surface quasi-geostrophic (SQG) equations, and magnetohydrodynamics (MHD). He explores critical aspects of fluid behavior, including singularity formation, turbulence, and inviscid limits. Key areas of study include global regularity analysis for hydrodynamic models, stability of plasma equilibria, and coupled systems such as Nernst-Planck-Navier-Stokes. His work bridges theoretical analysis and applications in geophysical fluid dynamics and plasma physics. Recent research highlights include studies on the inviscid limit of vorticity distributions, magnetic relaxation in MHD systems, and the mathematical foundations of complex fluid models. He has contributed to understanding electrokinetic phenomena, electrodiffusion, and the interplay between fluid dynamics and particle interactions. His publications reflect a deep engagement with both foundational PDE theory and applied problems, addressing topics like singularity conditions in Euler equations, blow-up criteria, and the role of symmetry in fluid and plasma systems.

Martin OttoView profile

Professor

• Mathematical Logic
• Model Theory
• Complexity Theory
• +4 more

Prof. Martin Otto is a Professor of Mathematics at the Technische Universität Darmstadt, specializing in Logic and Mathematical Foundations of Computer Science. He holds a position in the Department of Mathematics (Fachbereich 4) and serves as Dean of Studies. His academic journey includes a PhD from the University of Freiburg (1990), habilitation from RWTH Aachen (1996), and prior roles as a Lecturer/Reader at Swansea University (1999–2003). Research Interests: Mathematical Logic, Model Theory, Complexity Theory, Algorithmic Model Theory, Finite Model Theory, and Logic in Computer Science. Notable contributions include work on bisimulation, guarded logics, and inquisitive semantics. His research bridges structural properties in mathematics and computational expressiveness. Teaching: Courses span Mathematical Logic, Model Theory, Linear Algebra, and Modal Logics. Recent offerings include Introduction to Mathematical Logic (2024/25), Logic & Knowledge Representation, and advanced seminars on model-theoretic topics. Publications: Over 50 peer-reviewed papers in journals like the Journal of Symbolic Logic, and conference proceedings such as LICS and CSL. Key works address guarded fragments, bisimulation invariance, and finite model theory applications. Affiliations: Member of the Logic Group at TU Darmstadt. Editorships include the Bulletin of Symbolic Logic and Lecture Notes in Logic. Organized workshops like AlMoTh 2020 (cancelled due to pandemic) and participated in Simons Institute programs (2016).

Timothy A. BrownView profile

Professor

• Classification of Anxiety and Mood Disorders
• Psychometric Methods
• Structural Equation Modeling
• +3 more

Dr. Timothy A. Brown is Professor Emeritus in Psychological & Brain Sciences at Boston University, where he directs research at the Center for Anxiety & Related Disorders. His expertise spans anxiety/mood disorders classification, temperament-psychopathology relationships, and advanced statistical methodologies. His research focuses on dimensional models of emotional disorders, psychometric evaluation of diagnostic instruments, and structural equation modeling applications. He developed influential frameworks for understanding comorbidity patterns through latent variable modeling. Publications demonstrate consistent themes in psychometric validation, transdiagnostic approaches, and longitudinal modeling of psychopathology. Recent work emphasizes COVID-19 mental health impacts and clinical assessment optimization. Dr. Brown teaches graduate courses in multivariate statistics, including confirmatory factor analysis and structural equation modeling. He authored the authoritative textbook Confirmatory Factor Analysis for Applied Research . He serves as Associate Editor of the Journal of Abnormal Psychology and collaborates internationally on classification systems for emotional disorders.

Leslie GreengardView profile

Professor

• Integral Equation Methods
• Electromagnetics
• Acoustics
• +5 more

Leslie Greengard is a Silver Professor of Mathematics and Computer Science at New York University's Courant Institute, part of the Faculty of Arts and Science. His research focuses on integral equation methods for electromagnetics, acoustics, plasma physics, and fluid dynamics, with recent work extending to kernel-based methods in statistical inference. He leads the Courant Mathematics and Computing Laboratory and contributes to novel electromagnetic simulation techniques. Education: Ph.D. and M.D. in Computer Science and Medicine from Yale University (1987) B.A. in Mathematics from Wesleyan University (1979) Research Interests: Greengard's group develops fast solvers for complex geometries in electromagnetics and fluid dynamics. Notable contributions include the Fast Multipole Method (FMM) for particle simulations and Quadrature by Expansion (QBX) for layer potential evaluations. His work bridges computational mathematics and applications in physics, engineering, and biomedicine. Recent Article Trends: Recent publications emphasize fast algorithms (e.g., FMM, adaptive Gauss transforms), biomedical applications (e.g., deep brain stimulation modeling), and cosmological simulations. His methods address challenges in high-dimensional data, multiscale problems, and real-time computation. Awards & Recognition: While no specific prizes are listed, his foundational work on FMM has had a transformative impact on computational science. Advising & Collaborations: Collaborations span academia and industry, with co-authors including V. Rokhlin, M. O’Neil, and others. No formal advisees are listed here, though his research involves postdoctoral fellows and graduate students. Labs & Teams: Active in the Courant Mathematics and Computing Laboratory, focusing on electromagnetic simulation and design methodologies.

Andreas Milias ArgeitisView profile

Associate Professor

• Systems Biology
• Biochemistry & Molecular Biology
• Mathematics, Interdisciplinary Applications
• +6 more

Andreas Milias Argeitis is an Associate Professor in the Faculty of Science and Engineering at the University of Groningen. He leads the Milias-Argeitis Lab within the Molecular Systems Biology research unit at the Groningen Biomolecular Sciences & Biotechnology Institute (GBB). His research focuses on integrating experimental and computational approaches to understand cellular processes, particularly the coordination between cell growth, division, and metabolic dynamics in budding yeast. Key research areas include systems biology, TOR signaling pathways, cell cycle regulation, and the application of machine learning and mathematical modeling. His lab develops advanced tools like optogenetic control systems and deep learning algorithms for cell segmentation and tracking. Recent work has revealed metabolic oscillations linked to the cell cycle and explored the role of proteins like Sch9 in TORC1-dependent signaling. Notable achievements include an NWO Vidi Grant (2018) and an ENW Science-M Grant (2023) for studying how growth drives the cell division cycle. He has over 40 peer-reviewed publications, including work in Nature Communications , Nature Metabolism , and Journal of Cell Science . His research bridges fundamental biology with technological innovations in single-cell analysis and synthetic biology. Lab activities include CRISPR/Cas9 genome editing protocols, fluorescent protein maturation studies, and the development of photo-switchable enzymes. Collaborations span biochemistry, mathematics, and engineering, reflecting his interdisciplinary approach to unraveling cellular mechanisms.

Andrea WaltherView profile

Professor

• Nonlinear Optimization
• Algorithmic Differentiation
• Mathematical Optimization
• +3 more

Andrea Walther is a Professor of Mathematical Optimization at the Humboldt University of Berlin, holding a position within the Faculty of Mathematics and Natural Sciences. She leads the Mathematical Optimization research group at the Institute of Mathematics, focusing on algorithmic differentiation, nonlinear optimization, and applied mathematics. Her academic journey includes a Diploma in Business Mathematics (1996, University of Bayreuth), a PhD (1999, TU Dresden), and habilitation (2008, TU Dresden). She has held roles such as Junior Professor at TU Dresden (2007–2008) and Professor at the University of Paderborn (2009–2019) before joining Humboldt in 2019 as a MATH+ Professor. Education : 1991–1996: Studies in Business Mathematics, University of Bayreuth 1996: Diploma in Business Mathematics, University of Bayreuth 1999: PhD in Mathematics, TU Dresden 2008: Habilitation, TU Dresden Her research interests center on optimization methods, particularly algorithmic differentiation (e.g., ADOL-C software), nonsmooth optimization, and applications in engineering and machine learning. She leads initiatives like the Cluster of Excellence MATH+ and contributes to projects such as the Transregio 154. Key Projects: Co-PI of DFG Project 'Mixed-integer non-smooth optimization for gas market problems' (2020–2022) Principal Investigator in MATH+ Projects (EF3-7, AA2-7) Co-developer of ADOL-C, a widely used tool for algorithmic differentiation Notable awards include being a SIAM Fellow. Her work bridges theoretical advancements and practical applications, with contributions to energy sector optimization, inverse problems, and computational frameworks for solving complex systems.

Anders SkrondalView profile

Professor

• Psychometrics
• Statistics
• Biostatistics
• +3 more

Anders Skrondal is a Professor II at the University of Oslo's Faculty of Educational Sciences, affiliated with the Centre for Educational Measurement (CEMO). He also serves as a Senior Scientist at CEFH (Research Council of Norway Centre of Excellence) at the Norwegian Institute of Public Health and Co-Principal Investigator at CREATE, another Norwegian Centre of Excellence. His academic journey includes roles as Head of the Biostatistics Group at the Norwegian Institute of Public Health and Professor of Statistics at the London School of Economics (LSE), where he directed the Methodology Institute. Skrondal's research focuses on psychometrics, statistics, biostatistics, and econometrics, with a major contribution being the development of the GLLAMM framework. He has authored 14 books and over 200 peer-reviewed papers, achieving an h-index of 63 and 30,000+ citations. His awards include the 1997 Psychometric Society Dissertation Prize and leadership roles in prestigious organizations like the Psychometric Society and Royal Statistical Society. Research Interests: Skrondal specializes in statistical methodologies including latent variable modeling, multilevel modeling, and missing data analysis. His work bridges theoretical advancements and practical applications in medicine, psychology, and social sciences. He is renowned for integrating latent variable and mixed model frameworks to address complex data structures. Recent trends in his publications emphasize methodological solutions for missing data, non-ignorable mechanisms, and psychometric model validation. His articles span statistical theory, medical applications, and educational measurement. Awards: President, Psychometric Society (2016–2017) Elected Member, International Statistical Institute Outstanding Academic Title for 'The Cambridge Dictionary of Statistics' (2011) Fulbright Professor at UC Berkeley (2013–2014) Advising & Grants: Skrondal has led major research initiatives such as CEFH and CREATE, funded by the Research Council of Norway. While no specific advisee list is provided, his collaborations span international institutions. His work on GLLAMM software is used in over 750 journals, reflecting widespread academic impact. Labs/Teams: Active in CEMO and CEFH, he contributes to interdisciplinary teams advancing educational measurement and public health research. His involvement in CREATE focuses on equality in education through statistical innovations.