Find Academic Supervisors

Yi-Hsin LiuView profile

Associate Professor

• Theoretical Plasma Physics
• Computational Plasma Physics
• Magnetic Reconnection
• +3 more

Yi-Hsin Liu is an Associate Professor of Physics and Astronomy at Dartmouth College. His research focuses on theoretical and computational plasma physics, particularly magnetic reconnection processes in space, astrophysical, and laboratory plasmas. He contributes to NASA's Magnetospheric Multiscale (MMS) Mission studying Earth's magnetosphere. His work bridges fundamental plasma physics with observations from space missions. Education: B.S., National Taiwan University Ph.D., University of Maryland Research interests include magnetic reconnection's role in energy conversion, wave-particle interactions, and applications to solar physics, magnetospheric dynamics, and astrophysical plasmas. His group develops first-principles theories and simulations to explain reconnection rates and dynamics across scales. Publications highlight breakthroughs in relativistic reconnection theory, 3D reconnection structures, and kinetic-scale plasma processes. His work has advanced understanding of solar flares, coronal mass ejections, and space weather phenomena. Liu's lab (Liu's Plasma Physics Group) collaborates with NASA missions and uses advanced numerical simulations. He actively engages in plasma physics education and outreach through Dartmouth's academic programs.

Alastair MunroView profile

Academic

• Cancer Research
• Clinical Oncology
• Radiation Therapy
• +3 more

Alastair Munro is an Honorary Professor at the School of Medicine, University of St Andrews. He focuses on cancer research, clinical oncology, and public health, with a particular interest in smoking cessation strategies, radiation therapy optimization, and the application of mathematical modeling to medical challenges. His work aligns with UN Sustainable Development Goals, contributing to improving global health outcomes. He collaborates with interdisciplinary teams, as seen in his studies on cancer treatment delays and aspirin’s role in colorectal cancer survival. Research interests include: Cancer treatment methodologies (e.g., radiotherapy, pharmacological adjuvants) Qualitative and quantitative analyses of patient experiences and healthcare systems Mathematical modeling to predict tumor behavior and treatment efficacy His publications highlight trends in analyzing clinical outcomes and patient perspectives, blending epidemiological data with computational approaches. Notable collaborative projects involve investigating bystander effects in radiation therapy and the impact of post-diagnosis aspirin use. No formal scientific awards are listed in the provided texts. While no advisees are explicitly mentioned, his research suggests active participation in collaborative academic projects. He is affiliated with the University of St Andrews School of Medicine, with no specific labs or teams detailed in the texts.

• Partial Differential Equations
• Numerical Analysis
• Mathematical Biology
• +5 more

Charles M. Elliott is a Professor of Mathematics at the University of Warwick. His research focuses on mathematical and numerical analysis of partial differential equations (PDEs), particularly in evolving domains, manifolds, and surfaces. Key areas include free boundary problems, geometric evolution equations, biomembranes, and phase transitions. He has contributed to finite element methods for surface PDEs and holds editorial roles at journals like the IMA Journal of Numerical Analysis and Interfaces and Free Boundaries . Notable awards include the Humboldt Prize (2010) and the Royal Society Wolfson Research Merit Award (2016). His work bridges theoretical mathematics and applications in biology, materials science, and continuum mechanics. Educations and career details: While specific educational qualifications are not explicitly stated in the text, his professional trajectory includes leadership in computational mathematics and extensive editorial contributions. His teaching responsibilities include advanced modules like Maths-in-Action and Topics in Partial Differential Equations . Research Interests: Elliott’s work emphasizes PDEs in complex environments, with applications to biomembranes, cell motility, and material phase transitions. His methods integrate evolving surface finite elements and stochastic processes. Recent trends in his publications highlight evolving domains, coupled bulk-surface systems, and geometric PDEs. He addresses challenges in numerical analysis and modeling of evolving interfaces. Awards and Recognition: In addition to the Humboldt Prize and Royal Society award, he is a Fellow of the Society for Industrial and Applied Mathematics (SIAM, 2015) and received an honorary doctorate from the University of Sussex (2022). Advising and Grants: While specific student names or grant details are not listed, his research programs involve collaborations on topics like biomembrane dynamics and phase field models. He has pioneered numerical methods for evolving surface PDEs, impacting fields from materials science to mathematical biology. Labs and Teams: His work is conducted within Warwick’s Department of Mathematics, collaborating on interdisciplinary projects involving computational PDEs and their applications. The research often involves evolving geometries and multiscale modeling approaches.

Wolfgang PantleonView profile

Professor

• Materials Science
• Microstructure Engineering
• Plastic Deformation
• +17 more

Wolfgang Pantleon is a Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), specializing in Materials and Surface Engineering. His research is centered on microstructure evolution, plastic deformation, and advanced characterization techniques in metallic materials, particularly tungsten and nickel-based superalloys. He is actively involved in projects related to fusion materials, additive manufacturing, and high-temperature stability. His research interests include plastic deformation , dislocation structures , annealing , grain growth , recrystallization , and composite materials , with a strong focus on tungsten fiber-reinforced composites for plasma-facing applications. He employs advanced techniques such as electron backscatter diffraction (EBSD) and high-resolution reciprocal space mapping for in-depth microstructural analysis. His recent publications (2024–2025) reflect a strong trend in materials for nuclear fusion , additive manufacturing of superalloys , and in-situ characterization of phase transformations . These works span journals like Scripta Materialia , Materials Characterization , and Fusion Engineering and Design , emphasizing mechanical performance, microstructural stability, and restoration mechanisms under extreme conditions. He has received notable scientific recognition, including: SOFT2024 PhD Poster Prize Winner (2024) Best Poster Award (2019) Wolfgang Pantleon actively supervises PhD students and leads significant research projects, such as Thermal Stability of Tungsten Fiber-reinforced Tungsten Composites and Stability of Tungsten Plates during High Temperatures . He has secured funding from EU and national sources, demonstrating strong grant acquisition capabilities. His collaborative network includes institutions in Germany and international partners in fusion research. He is affiliated with the Materials and Surface Engineering section at DTU, where he contributes to both fundamental and applied research in advanced materials, particularly for energy and industrial applications.

Thomas J.R. HughesView profile

Professor

• Isogeometric Analysis
• Computational Mechanics
• Finite Element Analysis
• +7 more

Thomas J.R. Hughes is the Peter O’Donnell Jr. Chair in Computational and Applied Mathematics and a Professor of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin. He is affiliated with the Oden Institute for Computational Engineering and Sciences (Oden Institute), where he leads the Computational Mechanics Group. His research focuses on computational mechanics, isogeometric analysis, and biomedical modeling. Education: B.E. and M.E. in Mechanical Engineering from Pratt Institute; M.S. in Mathematics and Ph.D. in Engineering Science from the University of California, Berkeley. Research Interests include Isogeometric Analysis (integrating CAD and FEA), stabilized numerical methods for fluid flows, patient-specific biomedical simulations, and phase-field modeling. His work spans applications in cardiovascular systems, tumor growth, and geophysical flows. Hughes has authored over 150 journal articles, with recent contributions on isogeometric analysis, fluid-structure interaction, and computational geosciences. Notable awards include the von Neumann Medal (USACM), Gauss-Newton Medal (IACM), and Worcester Reed Warner Medal (ASME). He is a member of the National Academy of Engineering. His group advises students and postdocs in computational methods and collaborates with centers like the Oden Institute’s Computational Visualization Center. Current projects include isogeometric analysis, nanoparticle drug delivery, and ice sheet modeling.

Juan RestrepoView profile

Professor

• Applied Mathematics
• Computational Mathematics
• Data Science
• +12 more

Juan Restrepo is a Professor of Mathematics with courtesy appointments in Statistics, EECS, and Physical Oceanography at Oregon State University, where he holds a Courtesy Faculty position in the Department of Mathematics within the College of Science. He is also affiliated with the University of Tennessee and Oak Ridge National Laboratory, where he holds appointments in the Computer Science and Mathematics Division. He serves as Co-Director of the Dynamics and Data Science Institute (D2SI), a leading interdisciplinary research center. Ph.D. in Physics, Pennsylvania State University, 1992 M.S. in Engineering (Acoustics), Pennsylvania State University, 1987 B.S. in Music, New York University, 1983 Restrepo's research lies at the intersection of data science and dynamics, with two primary tracts: (1) applying statistical physics and data science to complex non-equilibrium systems such as climate and financial markets; and (2) studying ocean dynamics and transport in climate and nearshore processes. His work emphasizes uncertainty quantification, adaptive time series analysis, extreme events, and data assimilation. His recent publications reflect a strong focus on modeling geophysical systems under uncertainty, using advanced computational and statistical techniques. Themes include climate sensitivity, oil spill dispersion, sediment transport, wave breaking, and high-performance computing for large-scale simulations. His work integrates machine learning, stochastic modeling, and dynamical systems theory. SIAM Fellow SIAM Geosciences Career Award ORISE Distinguished Post-doctoral Fellow DOE Young Investigator Award Ruth Homeyer Graduate Student Award Restrepo has secured over $30 million in research funding from NSF, DOE, NASA, and GoMRI. He has advised numerous students in applied mathematics and computational sciences, and has held leadership roles in professional societies, including President of the Nonlinear Geophysics Section at the American Geophysical Union. He is actively involved in promoting diversity in science and mentoring underrepresented groups. He leads interdisciplinary research teams at D2SI and collaborates with national labs such as Argonne and Los Alamos. His work bridges applied mathematics, climate science, oceanography, and computational engineering, making significant contributions to both theory and real-world applications.

• Unconventional Reservoir Multiphysics
• Coal Seam Gas Extraction
• Shale Gas Extraction
• +5 more

Jishan Liu is a Professor in the School of Engineering at The University of Western Australia, specifically within the Civil, Environmental and Mining Engineering department. His academic profile shows extensive research contributions with 241 research outputs and 20 granted research projects. Professor Liu's primary research interests focus on Unconventional Reservoir Multiphysics (URM) with applications to: Coal seam gas extraction Shale gas extraction $$\text{CO}_2$$ sequestration in coal Coal mine safety Caprock sealing safety His specific expertise includes examining the effects of local mass transfer, momentum transfer, and deformation compatibility between rock matrix and fracture on physical processes, and incorporating these into the framework of Geo-Multiphysics. His research contributes to UN Sustainable Development Goals related to energy and environmental sustainability. His work spans across the fields of Energy and Mining and Resources, with specific expertise in Unconventional Gases, Geomechanics, Modelling, Coupled Multiphysics, and Porous Flow. Professor Liu has been involved in significant research projects including: Four Stage Permeability Evolution Theory for Low Permeable Rocks (2020-2024) Impact of coal-fluid interaction on the effectiveness of fracturing during coal seam gas drainage (2014) CarbonNet Dynamic Seal Capacity (2012-2013) Development of a Novel Experimental Approach for the Evolution of Coal and Shale Permeability (2012) Multiscale Dynamics of Ore Body Formation (2010-2015) These projects demonstrate his long-standing commitment to advancing knowledge in reservoir engineering and geomechanics. His recent publications show continued productivity with focus on permeability evolution, coal mechanics, and advanced modeling approaches. His work has garnered significant attention with an h-index of 63 and over 11,815 citations according to Scopus. Professor Liu has supervised 14 research students throughout his career, contributing to the development of the next generation of researchers in his field.

• Applied Mathematics
• Partial Differential Equations
• Material Science
• +5 more

Ulisse Stefanelli is a full Professor at the Department of Mathematics, University of Vienna, within the Faculty of Mathematics. His research focuses on applied mathematics, partial differential equations, and material science, with a particular emphasis on nonlinear dynamics, elasticity, and variational methods. He has contributed significantly to the study of gradient flows, energy dissipation principles, and phase-field modeling in complex materials. Affiliations: University of Vienna (Faculty of Mathematics, Department of Mathematics) His research interests include the mathematical analysis of continuum mechanics problems, such as viscoelasticity, plasticity, and accretive growth in solids. He has pioneered methods like the Weighted Energy-Dissipation principle and contributed to topology optimization and multiscale modeling. Stefanelli has led numerous research projects, including those on elastic-fluidic biomembranes and geometric boundary conditions in solids. Awards: Vinti Prize (2015) His work bridges pure and applied mathematics, addressing challenges in material science and geophysics. Current activities include studies on planetary accretion models and stochastic evolution equations. Collaborations span international institutions, focusing on interdisciplinary applications of variational principles.

Nancy KopellView profile

Professor

• Applied Mathematics
• Dynamical Systems
• Neuroscience
• +3 more

Professor Nancy Kopell is a distinguished academic and mathematician at Boston University, holding the William Fairfield Warren Distinguished Professorship in the Department of Mathematics. She is also Co-Director of the Center for BioDynamics and has affiliations with the Department of Biomedical Engineering. Her research focuses on applied mathematics and dynamical systems, particularly the dynamics of the nervous system, including neural rhythms, cognitive processing, and anesthesia effects. Dr. Kopell has made groundbreaking contributions to understanding how brain rhythms underpin behavior, cognition, and neurological disorders. Educational Background: She earned her A.B. from Cornell University (1963) and a Ph.D. in Mathematics from UC Berkeley (1967). She has held academic positions at MIT and Northeastern University before joining BU in 1986. Research Interests: Dr. Kopell's work spans theoretical neuroscience, mathematical biology, and nonlinear dynamics. Key areas include: Rhythms in neural networks (gamma, beta, theta) Cell assembly coordination and synchronization Neurophysiological mechanisms of attention, memory, and decision-making Mathematical modeling of anesthesia and consciousness Geometric theory of singularly perturbed systems Awards & Honors: National Academy of Sciences (1996) American Academy of Arts and Sciences (1996) SIAM Fellow (2009) MacArthur Fellowship (1990-1995) Weldon Memorial Prize (2006) Grants & Collaborations: She has led numerous interdisciplinary projects, including the Cognitive Rhythms Collaborative and studies on thalamocortical interactions. Current grants include NIH-funded research on brain rhythms in attention and sleep. Labs & Teams: Her Neuronal Dynamics Group at BU investigates neural oscillations and their roles in cognition. Collaborators include neuroscientists, engineers, and computational biologists.

• Uncertainty Quantification
• Predictive Modeling
• Computational Mathematics
• +5 more

Nicholas Zabaras is a Professor of Uncertainty Quantification and the Director of the Warwick Centre for Predictive Modelling at the University of Warwick. He is a Hans Fischer Senior Fellow at the TUM Institute for Advanced Study (TUM-IAS), hosted by Phaedon-Stelios Koutsourelakis. His research focuses on advancing computational methods for uncertainty quantification, predictive modeling, and multiscale/multiphysics systems. Key areas include Bayesian methods, stochastic modeling, and data-driven approaches for complex materials systems. Education: He holds a diploma in Mechanical Engineering from the National Technical University of Athens (1982), an M.Sc. in Materials Science and Engineering from the University of Rochester (1983), and a Ph.D. in Theoretical and Applied Mechanics from Cornell University (1987). He has held academic positions at the University of Minnesota, Cornell University, and the University of Warwick, where he now leads the Warwick Centre for Predictive Modelling. Research Interests: His work integrates computational mathematics, statistics, and scientific computing to address challenges in materials science, computational physics, and engineering systems. Specific themes include Bayesian uncertainty quantification, high-dimensional modeling, information-theoretic coarse graining, and stochastic model reduction. Awards: He has received the Royal Society Wolfson Research Merit Award (2014), the Michael Tien’72 College of Engineering Teaching Award (2009), and is a Fellow of the American Society of Mechanical Engineers (2006). Labs/Teams: Director of the Warwick Centre for Predictive Modelling, and leader of the Scientific Computing and Artificial Intelligence (SCAI) Laboratory at the University of Notre Dame, focusing on interdisciplinary research in AI-driven predictive modeling and uncertainty quantification.

He LiView profile

Assistant Professor

• Multiscale Computational Modeling
• Artificial Intelligence in Medicine
• Biomedical Engineering
• +3 more

Dr. He Li is an Assistant Professor at the University of Georgia, specializing in computational biomedical engineering. His research focuses on developing AI-driven multiscale models to bridge microscopic and macroscopic biological processes, with applications in disease modeling, medical imaging, and healthcare innovation. He leads the Computational Biomedical Engineering Lab, exploring interdisciplinary solutions in biomedical science using advanced computational methods. His work integrates physics-based modeling (e.g., molecular dynamics, spectral element methods) with AI techniques to assimilate diverse biological data sources. Recent research emphasizes predictive AI models for digital health, including radiology analysis, proton therapy optimization, and Alzheimer’s disease infodemiology. He has published extensively on large language models (LLMs) tailored for medical domains like radiology, oncology, and ophthalmology, addressing challenges in specialized clinical NLP tasks. Key contributions include frameworks like Radiology-GPT and AD-Autogpt, which advance AI applications in diagnostics and disease surveillance. His lab collaborates across disciplines to enhance clinical decision-making through AGI (Artificial General Intelligence) and multimodal data fusion. No awards or grants are explicitly listed in the provided texts. Labs/Teams: Computational Biomedical Engineering Lab @ UGA focuses on AI-driven biomedical innovation, with projects spanning medical imaging, disease modeling, and healthcare technology development.

• Water Resources Engineering
• Environmental Engineering
• Biogeochemistry
• +5 more

Lazaro J. Perez is an Assistant Professor in the School of Civil and Construction Engineering at Oregon State University, part of the College of Engineering. His research focuses on hydrological and biogeochemical processes in subsurface porous media, particularly quantifying incomplete mixing effects on chemical reactions and contaminant transport. He integrates experimental and numerical methods to address groundwater remediation, aquifer management, and bioremediation challenges. Education: Ph.D., Civil and Environmental Engineering, Polytechnic University of Catalonia (2019) M.S., Engineering Geology and Geologic Resources, University of Oviedo (2015) B.S., Geology, University of Oviedo (2013) Research interests include bacterial transport dynamics, biofilm impacts on fluid flow, and multiscale modeling of reactive transport. His work spans pore-to-field scales, emphasizing practical applications like arsenic removal using metal-organic frameworks and climate change effects on water systems. Key contributions include advancements in solute transport prediction via machine learning, upscaling bacterial migration in porous media, and understanding biofilm-induced flow heterogeneities. Current projects involve climate change impacts on mountain hydrology and innovative remediation strategies for contaminated waters.

François DesbouvriesView profile

Professor

• Statistical Signal Processing
• Data Science
• Bayesian Statistics
• +3 more

Prof. François Desbouvries is a Full Professor at Telecom SudParis and Director of the SAMOVAR lab, part of the Institut Polytechnique de Paris. He holds degrees from Telecom Paris and a HDR from Marne la Vallée University. His expertise spans statistical signal processing, Bayesian statistics, and data science, with a focus on hidden data models and Monte Carlo methods. He has held leadership roles including heading the TIPIC team and serving as an IEEE Senior Member since 2007. His research contributions include over 150 publications in top journals and conferences, with recent advancements in Bayesian filtering and machine learning integration. He actively contributes to academic governance through roles in ANR committees and the GdR ISIS network. Education: Engineer Degree in Telecommunications (1987, Telecom Paris), Ph.D. in Signal and Image Processing (1991, Telecom Paris), and Habilitation à Diriger des Recherches (HDR, 2001, Marne la Vallée University). Research interests include sequential Monte Carlo methods, variational inference, and applications in signal processing. Recent work explores Bayesian classification and neural network modeling comparisons. His articles emphasize algorithmic efficiency and theoretical rigor in dynamic systems. Leadership: SAMOVAR lab director (2020–present), former head of TIPIC team (2011–2019), and member of national scientific committees like ANR and GdR ISIS. Grants secured through collaborative projects in signal/image processing and data science. Labs/Teams: SAMOVAR lab (specializing in ICT) and former leadership of the TIPIC team, fostering interdisciplinary research in signal processing and communication systems.

Chantal TaconetView profile

Lecturer

• Internet of Things (IoT)
• Middleware Systems
• Context-Aware Computing
• +3 more

Chantal Taconet is a Lecturer at Telecom SudParis, part of the SAMOVAR research institute. Her work focuses on middleware systems for the Internet of Things (IoT), context-aware computing, energy efficiency, and blockchain applications in supply chains. She has co-authored over 20 peer-reviewed articles and conference papers since 2008, addressing IoT middleware design, QoC management, and middleware for cloud-IoT integration. Her research spans IoT middleware architectures (e.g., IoTVar framework), energy-efficient IoT protocols, and blockchain-based supply chain traceability. She contributed to the ANR INCOME project on multi-scale context management, emphasizing model-driven engineering and distributed system frameworks. Taconet has organized workshops like M4IoT and co-edited special issues on IoT middleware in Annals of Telecommunications . Key technical areas include semantic-based discovery services, fog computing integration for sensor networks, and trust models for IoT systems. Her work bridges theoretical middleware design with practical implementations for IoT applications.

• Computational Chemistry
• Theoretical Chemistry
• Enzyme Catalysis
• +7 more

Silvia Osuna Oliveras is an ICREA Researcher at the Institute of Computational Chemistry and Catalysis (IQCC), University of Girona, where she leads the OsunaLab research group. She holds a PhD in Chemistry from the University of Girona (2010) and completed postdoctoral training at UCLA under Prof. K.N. Houk with a Marie Curie Fellowship. She has held prestigious positions including Juan de la Cierva, Ramón y Cajal, and ICREA, reflecting her standing as a leading figure in computational chemistry. Her research focuses on theoretical and computational modeling of enzyme catalysis , with an emphasis on understanding reaction mechanisms, designing biocatalysts, and improving catalytic efficiency using quantum mechanics/molecular mechanics (QM/MM) methods. Her work bridges organic chemistry, physical chemistry, and biochemistry, contributing to sustainable and green chemistry through enzyme engineering. The 15 most recent publications analyzed span high-impact journals such as Nature , Nature Catalysis , Nature Chemical Biology , Nature Communications , JACS , and Angewandte Chemie . These works reveal a strong trend in computational enzymology , reaction mechanism elucidation , and biocatalyst design , often integrating advanced simulation techniques with experimental validation. Key subfields include QM/MM, free energy calculations, protein dynamics, and enzyme engineering for organic transformations. Her scientific achievements have been recognized with numerous awards: Premi Nacional d'Investigació 'María Teresa Toral' (2023) Premi Talent Científic Emergent (2023) EuChemS Lecture Award (2022) FCRi Young Talent Researcher (2019) Lilly-RSEQ Young Investigator (2019) EuCheMS Organic Division Young Investigator (2017) RSEQ Young Investigator (2016) FPdGi Research Prize (2016) She leads a well-funded research program supported by the European Research Council (ERC FASTEN Consolidator Grant, GREENZYME and KITZYME Proof of Concept), MINECO (COCOZYME), and EVOCAT projects, managing approximately €2.6 million in grants. Her lab actively contributes to scientific outreach, having published five popular science articles and co-edited a special issue on 'Nanoreactors and Molecular Prisons' in Current Organic Chemistry . The OsunaLab group is based at the Institute of Computational Chemistry and Catalysis (IQCC), a leading research institute at the University of Girona focused on theoretical and computational approaches in chemistry. The group collaborates internationally and trains the next generation of computational chemists, despite no current students being listed in the provided text.