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Luca GuazzottoView profile

Professor

• Plasma Physics
• Magnetohydrodynamics (MHD)
• Tokamak Equilibria
• +3 more

Dr. Luca Guazzotto is a Professor in the Department of Physics at Auburn University, affiliated with the College of Sciences and Mathematics. He holds a Ph.D. from the University of Rochester (2005) and a Laurea in Nuclear Engineering from Politecnico di Torino (2000). His research focuses on plasma physics and magnetohydrodynamics, particularly equilibrium and stability properties of plasmas in magnetic-confinement devices like tokamaks, with an emphasis on macroscopic flow effects. He has developed influential codes such as FLOW2 and contributed to analytic equilibrium solutions for fusion applications. His work includes studies on ignition conditions, burning plasmas, and multi-fluid modeling. Guazzotto has held roles at Auburn since 2014, including Associate Professor (2014–2023) and visiting positions at Princeton Plasma Physics Laboratory and MIT's Plasma Science and Fusion Center. His research integrates computational tools (e.g., FLOW2, SIM2D) and analytical methods to advance fusion energy science. Key contributions include two-fluid equilibrium modeling, analytic solutions for the Grad-Shafranov equation, and multi-dimensional plasma stability analysis. Guazzotto's publications span journals like Phys. Plasmas and Journal of Plasma Physics , addressing topics ranging from tokamak ignition to pedestal formation mechanisms. Guazzotto's research outputs emphasize plasma dynamics and fusion reactor design, with recent works exploring realistic analytic equilibria and burning plasma profiles. His freely available codes (FLOW2, analytic equilibrium tools) facilitate broader scientific collaboration. While no explicit awards are noted, his extensive publication record and code development highlight significant academic impact.

Rong ChenView profile

Professor

• Nonlinear Time Series Analysis
• Multivariate Time Series
• Monte Carlo Methods
• +5 more

Rong Chen is a Distinguished Professor and Chair of the Department of Statistics at Rutgers University, within the School of Arts and Sciences. With a Ph.D. from Carnegie Mellon University, Professor Chen has established himself as a leading researcher in statistical time series analysis, Monte Carlo methods, and their applications across various fields. Professor Chen's research focuses on: Nonlinear and Multivariate Time Series Analysis Monte Carlo Methods, Statistical Computing and Bayesian Analysis Statistical Applications in Science, Engineering and Business His research trajectory has evolved significantly over the years, beginning with foundational work on nonlinear time series and moving toward more complex high-dimensional tensor time series analysis. Recent publications show a strong emphasis on matrix and tensor factor models for high-dimensional time series, reflecting the growing importance of analyzing complex structured data in modern applications. His work bridges theoretical statistical innovation with practical problem-solving across finance, engineering, and computational biology. Professor Chen has been recognized for his contributions to the field with prestigious fellowships: ASA Fellow (American Statistical Association) IMS Fellow (Institute of Mathematical Statistics) As Chair of the Department of Statistics, Professor Chen oversees academic programs including the Master in Financial Statistics and Risk Management (FSRM) and the Master in Data Science (Statistics Track) programs. His leadership extends to guiding research directions in the department and fostering collaborations across disciplines. Professor Chen has secured numerous research grants supporting work in time series analysis, statistical computing, and applications in finance, engineering, and bioinformatics. Professor Chen's research group maintains active collaborations with researchers in finance, engineering, and computational biology, applying statistical innovations to real-world problems including financial time series analysis, protein folding studies, HIV infection dynamics modeling, wind power forecasting, and nuclear material detection systems.

• Granular Mechanics
• Mechanical Engineering
• Neutron Diffraction
• +5 more

Associate Professor Christopher Wensrich is a faculty member in the School of Engineering at the University of Newcastle, Australia, specializing in Mechanical Engineering. He has a strong background in applied mechanics from both computational and experimental perspectives, with significant expertise in granular mechanics, neutron diffraction strain measurement, and Bragg-edge transmission strain tomography. Education: PhD, University of Newcastle Bachelor of Mathematics, University of Newcastle Bachelor of Engineering, University of Newcastle Professor Wensrich's research focuses on several interconnected areas within mechanical engineering and materials science. His primary expertise lies in granular mechanics, spanning from micromechanics and homogenization of granular systems to analytical modeling of granular dynamics (particularly the silo quaking problem) and computational modeling using the Discrete Element Method (DEM). He is also a pioneer in applying neutron diffraction strain scanning techniques to granular systems. In the broader field of applied mechanics, he has made significant contributions to neutron diffraction-based strain measurement, including breakthroughs in Bragg-edge Transmission Strain Tomography, where he demonstrated the world's first practical application outside of simple axisymmetric systems. His publication record demonstrates a consistent focus on developing and applying advanced techniques for strain measurement and reconstruction in granular and composite materials. His recent work has centered on tomographic reconstruction methods using neutron diffraction, with particular emphasis on Bragg-edge techniques for 2D and 3D strain field reconstruction. His research bridges theoretical mathematics, computational methods, and experimental validation, creating a robust framework for non-destructive stress measurement in complex materials. Professional Recognition: President of the Australian Neutron Beam User Group (ANBUG) since December 2022 Member of the ACNS Program Advisory Team at ANSTO (Australian Nuclear Science and Technology Organisation) since March 2019 Visiting Fellow at Clare Hall College, Cambridge University (January-June 2023) Visiting Researcher at Isaac Newton Institute for Mathematical Sciences (January-June 2023) Professor Wensrich has secured substantial research funding, with a total of $5,478,793 across 42 grants. His funding portfolio includes projects from the Australian Research Council (ARC), ANSTO, and international partners like Oakridge National Laboratory and Japan Proton Accelerator Research Complex. He has successfully supervised 11 PhD and Masters students to completion, with research topics spanning granular mechanics, conveyor systems, and neutron strain tomography. His current research involves collaborations with institutions worldwide, focusing on advanced strain measurement techniques and their application to complex material systems.

Pierre Lafaye de MicheauxView profile

Associate Professor

• Applied Statistics
• Biostatistics
• Statistical Theory
• +11 more

Associate Professor Pierre Lafaye de Micheaux is a statistician based at the School of Mathematics and Statistics, University of New South Wales , where he has worked since 2020. He previously held academic roles at Université Paul Valéry (2020, Associate Professor), ENSAI (2015–2017, Professor), Université de Montréal (2011–2016, Associate Professor), and Grenoble Alps University (2003–present, Assistant Professor). His research spans theoretical and applied statistics , focusing on complex random vectors , neuroimaging genetics , and data science for IoT . Education: PhD in Statistics (2003, Université de Montréal & Montpellier) MSc in Biostatistics (1998, Montpellier) BSc in Mathematics and Physics (1996, Montpellier) MSc in Cognitive Neuroscience (2007, Grenoble Institute of Technology) Research interests include: Dependence Measures : Leveraging complex analysis for big data dependence testing under 3V's (Volume, Variety, Velocity). Neuroimaging Genetics : Developing statistical tools for fMRI/EEG/DTI phenotyping of genetic variation with institutions like CHeBA and INSERM. IoT Data Science : Creating Raspberry Pi-based statistical computing tools for real-time sensor data streams. Complex-Valued Inference : Building a unified framework for complex random vectors in neuroimaging and nuclear engineering. Recent publications demonstrate expertise in circular data analysis , nonparametric testing , and central limit theorem counterexamples , with applications in medical imaging and finance. He has supervised numerous PhD, MSc, and honors students on topics ranging from deep learning to stochastic processes. Scientific achievements include: Université de Montréal Provost Honor List (2003) Editor of the Journal of Statistical Software (2017–present) Co-leader of three research groups: Dependence Measures , Neuroimaging Genetics , and Data Science & IoT He has secured grants from UNSW Research Infrastructure Scheme and NSERC , with industry collaborations including BNP Paribas (credit risk) and Olea Medical (stroke treatment analytics). Current teaching includes Statistical Inference (ZZSC5905) and Data Science (DATA3001) at UNSW.

Martin Skovgaard AndersenView profile

Associate Professor

• Optimization
• Signal Processing
• Machine Learning
• +12 more

Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.

• Computational Engineering
• Biomedical Engineering
• Digital Twin
• +4 more

Professor Perumal Nithiarasu is a Professor and Director of Research in the College of Engineering at Swansea University. He also serves as Deputy Head of the College and Dean of Academic Leadership (Research Impact). His expertise spans computational engineering, biomedical engineering, and artificial intelligence, with a focus on blood flow dynamics and finite element methods. He has held leadership roles, including directing the Zienkiewicz Center for Computational Engineering and co-chairing international conferences like the Computational Biomedical Engineering series. Notable awards include the Zienkiewicz ICE Silver Medal (2002) and an EPSRC Senior Fellowship (2006). Research Interests: Computational Fluid Dynamics (CFD) Biomedical Engineering Applications Finite Element Method (FEM) Digital Twin Technology Artificial Intelligence in Biomedical Systems Scientific Contributions: Professor Nithiarasu has pioneered numerical methods like the Locally Conservative Galerkin (LCG) and developed influential tools such as the Zienkiewicz Lecture series. His work bridges computational models with clinical applications, including cardiovascular simulations and patient-specific coronary analysis. He leads a 30+ member research group and edits the International Journal for Numerical Methods in Biomedical Engineering . Grants & Impact: His research has been funded by EPSRC and other bodies, with applications in medical devices (e.g., glaucoma treatment shunts) and thermal systems. Collaborations include RAEng, IACM, and industry partners.

Martin H. WührView profile

Associate Professor

• Proteomics
• Cellular Organization
• Molecular Biology
• +3 more

Martin H. Wühr is an Associate Professor of Molecular Biology and a member of the Lewis-Sigler Institute for Integrative Genomics at Princeton University. He leads the Wuhr Lab, focusing on quantitative proteomics to understand cellular organization. His work investigates how molecules self-organize into organelles and cells, particularly studying nuclear-cytoplasmic proteome partitioning and its impact on biological function. Research employs mass spectrometry-based proteomics combined with computational, biochemical, and imaging approaches, using models like human tissue cells and Xenopus laevis embryos. Key contributions include analyzing protein localization dynamics during embryogenesis and developing novel proteomic methodologies. Recent publications highlight advancements in multiplexed proteomics, nuclear import mechanisms, and microbial nutrient interactions. Awards and grants are not explicitly listed, but his lab’s technical innovations indicate significant field impact. Advising and lab management details are not provided, though collaborations with institutions like MIT and Harvard suggest active academic engagement. Labs/Teams: The Wuhr Lab at Princeton University focuses on systems-level proteomics and cellular organization studies.

Giovanni RussoView profile

Professor

• Computational Fluid Dynamics
• Numerical Methods for Conservation Laws
• Numerical Methods for Kinetic Equations
• +5 more

Giovanni Russo is a Full Professor of Numerical Analysis at the Department of Mathematics and Computer Science, University of Catania, Italy . He coordinates the PhD program in Pure and Applied Mathematics and has been a visiting scholar at institutions including Courant Institute, University of California, Los Angeles, University of Michigan, and University of Bordeaux. His career spans over four decades across academia and research institutions. Education: PhD in Physics (1986, University of Catania), Laurea in Nuclear Engineering (1982, Politecnico di Milano). Research Interests: Russo specializes in Computational Fluid Dynamics , Numerical Methods for Conservation Laws , and Kinetic Equations . His work includes asymptotic preserving schemes , IMEX methods , semi-Lagrangian schemes , and high-order numerical techniques for PDEs with applications to fluid dynamics, plasma physics, and multiscale modeling. Scientific Trends: Recent publications focus on modeling epidemic dynamics , kinetic equations for inert mixtures , semi-Lagrangian methods , and uncertainty quantification in quantum systems. These works reflect his expertise in high-order numerical schemes , multiscale analysis , and applied mathematical modeling . Scientific Awards: CNR-NATO Fellowship (1987) Advising and Grants: Russo has supervised nine PhD students and served as Principal Investigator (PI) for major projects including MOSCOVID (modeling COVID-19) and ModCompShock (Horizon 2020 Marie Curie project). He has also organized international conferences like the 18th European Conference on Mathematics for Industry with 370 participants. Labs and Teams: Russo collaborates with research groups at the University of Catania and has been a visiting researcher at Courant Institute, University of Michigan, and GSSI L’Aquila. He contributes to journals as an editor and reviewer, including SIAM Journal of Numerical Analysis and Journal of Computational Physics .

• Electromagnetism
• Neutron Scattering
• Fluid Mechanics
• +5 more

Patrick Ciarlet is a Professor at ENSTA Paris, part of the Institut Polytechnique de Paris, where he is a member of the POEMS (Wave Propagation, Mathematical Study and Simulation) research team within the Applied Mathematics Unit (UMA). He also serves as the responsible person for the Master's degree in Mathematics and Applications at the Institut Polytechnique de Paris. His research is structured around two main poles in applied mathematics: Modeling in electromagnetism, neutron scattering and fluid mechanics Numerical analysis of PDEs and scientific computing Professor Ciarlet's work demonstrates a strong focus on mathematical methods for solving problems in wave propagation and electromagnetism, particularly with challenging materials such as metamaterials that have sign-changing coefficients. His research spans theoretical mathematical analysis and practical numerical implementations, with applications in nuclear engineering (neutron diffusion equations) and plasma physics. His publication record shows consistent contributions to finite element methods, with particular expertise in stability analysis, error estimation, and specialized approaches like T-coercivity for handling problems with sign-changing coefficients. He has also developed educational resources through lecture notes on Maxwell's equations and variational methods for non-coercive problems. As an academic leader, he plays a significant role in shaping mathematics education at the graduate level through his responsibility for the Master's degree program in Mathematics and Applications, contributing to ENSTA Paris's research focus on cutting-edge areas with applications in sustainable energy, transportation, and defense.

• Fluid-Rock Interaction
• Electron Microscopy
• Nanogeoscience
• +6 more

Dr. Oliver Plümper is an active researcher at Utrecht University's Faculty of Geosciences, specifically within the Earth Sciences department and the Structural Geology & Electron Microscopy group. His work spans multiple disciplines at the intersection of geology, chemistry, physics, and materials science, with a particular focus on understanding processes occurring at the nanoscale that influence large-scale geological phenomena. Plümper's research interests center around fluid-rock/mineral interaction, nano(geo)sciences, mineral physics, and rock deformation. He employs a multi-faceted approach that combines natural observations, experimental techniques, micro and nano-analytics, and numerical modeling to address fundamental questions in Earth sciences. His work particularly emphasizes how nanoscale processes in the Earth's interior can influence large-scale geological structures and phenomena, including mountain building, earthquake generation, and the carbon cycle. His current research portfolio includes several major projects: the ERC Starting grant 'nanoEARTH' investigating mineral-water interactions deep within the Earth; the Dutch Research Council Vidi project 'RELEASE' studying carbon cycling in subduction zones; the EU INFRAIA project 'EXCITE NETWORK' as co-Principal Investigator supporting access to advanced imaging facilities; the UIO-UU project 'serpAI' using artificial intelligence to study mantle rock alteration; and the UU-NIOZ project 'I-NANO' examining iron nanoparticles' effects on ocean biogeochemistry. These projects collectively demonstrate his focus on understanding how nanoscale processes influence planetary-scale phenomena. Plümper is a strong advocate for open science and transdisciplinary research, leading a diverse team of scientists from earth sciences, chemical engineering, mathematics, physics, and chemistry. He actively contributes to the Utrecht University Electron Microscopy Center and is involved in multiple initiatives promoting open access to analytical facilities. His collaborative approach is evident in his extensive publication record across high-impact journals including Nature Geoscience, PNAS, and Geology, with research spanning from fundamental mineral physics to applications in geothermal energy, raw material extraction, and carbon storage.

Valerie VenturaView profile

Professor

• Computational Neuroscience
• Time Series Forecasting
• Graphical Models
• +6 more

Valerie Ventura is a Professor in the Department of Statistics and Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. She serves as Co-Director of the Ph.D. Program and maintains dual affiliations as affiliated faculty in the Machine Learning Department and the Center for the Neural Basis of Cognition (CNBC). As a graduate advisor for the Program in Neural Computation (PNC), she bridges statistical methodology with neuroscience applications. Her research focuses on computational neuroscience, particularly time series forecasting, networks, algorithms, and nonparametric methods. Key research areas include computational neuroscience, biostatistics & epidemiology, graphical models & networks, and DELPHI lab projects. Her work integrates advanced statistical techniques to solve complex problems in neural data analysis, motor control, and neural decoding. Analysis of her publication record reveals consistent contributions to neural decoding methodologies, spike train analysis, and cortical covariance modeling. Her recent work emphasizes practical applications in brain-machine interfaces and neural prosthetics, with a strong focus on improving decoding accuracy through novel statistical approaches to spike sorting and waveform analysis. The integration of machine learning with traditional statistical methods characterizes her interdisciplinary approach. Scientific Awards: 2006 Canadian Journal of Statistics Best Paper Award As an educator, Ventura serves as Co-Director of the Ph.D. Program in Statistics and Data Science while advising students through the CNBC's Program in Neural Computation. Her collaborations span multiple departments and research centers, particularly through the DELPHI Lab Group which focuses on computational neuroscience applications. Current research directions suggest continued innovation in neural decoding algorithms and applications to motor control systems.

Michael ShelleyView profile

Professor

• Fluid Dynamics
• Active Matter
• Biophysics
• +3 more

Michael Shelley is the Lilian and George Lyttle Professor of Applied Mathematics at New York University's Courant Institute of Mathematical Sciences. He holds additional roles as Professor of Mathematics, Neural Science, and Mechanical Engineering. His research focuses on fluid dynamics, active matter, and biophysical systems, with notable contributions to microswimmer dynamics, cytoplasmic flows, and fluid-structure interactions. Shelley leads the Applied Mathematics Laboratory at Courant and directs the Center for Computational Biology at the Flatiron Institute. His work bridges theoretical, computational, and experimental approaches to understand complex biological and physical phenomena. Research interests include nonlinear dynamics of fluids, collective behavior in active matter systems, and biomechanical processes such as mitosis and cellular transport. Recent studies involve modeling microtubule networks, cytoplasmic stirring, and spindle positioning in cells. His publications span topics like fluid-structure interactions, viscoelastic flows, and the mechanics of swimming organisms. Key projects include the dynamics of erodible bodies in fluid flows, optimization of microswimmer designs, and the rheology of active suspensions. Shelley collaborates across disciplines, integrating applied mathematics with biology, physics, and engineering. His work has advanced understanding of self-organization in living systems and fluid-driven morphological changes.

Svein-Erik HamranView profile

Professor

• Planetary Science
• Geophysics
• Radar Technology
• +4 more

Svein-Erik Hamran is a Professor at the University of Oslo's Centre for Space Sensors and Systems (CENSSS) , specializing in radar technology and planetary exploration. His work focuses on subsurface analysis using ground-penetrating radar (GPR), with significant contributions to missions like NASA's Perseverance rover (RIMFAX instrument) and ESA's ExoMars. He leads the CENSSS, advancing space sensor systems for planetary and terrestrial applications. Research Interests: Planetary radar systems, subsurface geophysics, Mars exploration, instrument development for space missions, and remote sensing techniques. His team develops advanced radar technologies to study Martian geology and subsurface structures, with applications in Earth's cryosphere and environmental monitoring. Recent Article Trends: Focus on Mars subsurface stratigraphy (Jezero Crater), radar attenuation analysis, and compact instrumentation for space missions. Collaborations span global institutions, emphasizing interdisciplinary approaches in planetary science. Scientific Awards: None explicitly listed. Advising/Grants: Active in mission-related projects but specific grants/students not detailed. Labs/Teams: Leads CENSSS, collaborates with NASA/JPL, ESA, and international geophysics groups.

Linda K. NozickView profile

Professor

• Transportation Systems Engineering
• Network Science
• Critical Infrastructure Resilience
• +5 more

Dr. Linda K. Nozick is a Professor and Director of Civil and Environmental Engineering at Cornell University, leading research in infrastructure resilience and disaster risk management. She co-founded the College Program in Systems Engineering and previously served as a Visiting Associate Professor at the Naval Postgraduate School. Her work focuses on mathematical modeling for complex systems, including transportation networks, natural hazard mitigation, and critical infrastructure protection. Education: B.S. in Systems Analysis and Engineering, George Washington University (1989) M.S./Ph.D. in Systems Engineering, University of Pennsylvania (1990-1992) Research Interests: She pioneers models for transportation systems, network science, and disaster risk management. Key areas include: Optimization of infrastructure resilience Hurricane evacuation modeling Insurance-market dynamics for catastrophic risks Seismic retrofitting strategies Data-driven decision support systems Recent Work Highlights: Recent studies address hurricane evacuation behavior prediction using mobility data, equity in hazmat routing, and multi-hazard risk assessment for power grids. Awards: 2011 Presidential Early Career Award for Scientists and Engineers NSF CAREER Award (1997) Sandia National Labs Recognition Award (2009) Member of Nuclear Waste Technical Review Board Academic Service: Leads Cornell's Master of Engineering strategic planning and coordinates the Engineering Systems & Management mission area. She advises federal agencies on infrastructure renewal through National Academy Committees. Lab/Team Affiliations: Directs research initiatives in disaster resilience engineering and collaborates with Sandia National Labs on infrastructure optimization tools.

Zijian DiaoView profile

Associate Professor

• Quantum Computation
• Quantum Information Theory
• Natural Language Processing
• +7 more

Zijian Diao is an Associate Professor in the Department of Mathematics at Ohio University's College of Arts and Sciences, where he contributes to both teaching and research. He is based at the Eastern Campus in Shannon Hall and teaches a wide range of undergraduate mathematics courses. Education: Ph.D. in Mathematics, Texas A&M University, 2001 M.S. in Computer Science, University of Illinois, 2003 B.S. in Automation, University of Science and Technology of China, 1996 His research spans interdisciplinary areas with a strong focus on quantum computation and information theory , where he has made contributions to Grover's algorithm, quantum counting, and quantum circuit design. He has also worked in natural language processing , particularly in multilingual speech-to-speech translation systems like MARS. Additional interests include control theory , partial differential equations , and mathematical modeling . His recent and ongoing publications reflect a blend of theoretical mathematics and applied computational science. The works trend toward quantum algorithms and foundational mathematical proofs, with earlier contributions in NLP and control systems. His research bridges pure mathematics with practical implementations in quantum computing and language technologies. Scientific Awards: Ohio University Regional Higher Education Outstanding Professor Dr. Diao advises undergraduate and graduate students in mathematics and related fields, though specific student names are not listed. He has been involved in research projects supported by academic grants, particularly in quantum computing and interdisciplinary technology development. His curriculum vitae indicates sustained scholarly activity across multiple domains. He is associated with research groups and collaborations in quantum computing, having co-authored works with G. Chen, C. Huang, and others. His work in NLP was part of larger team efforts involving researchers from industry and academia, suggesting active participation in collaborative technical teams.