Find Academic Supervisors

• Non-destructive Testing
• X-ray physics
• Physical modelling and simulation
• +7 more

Philipp Schütz is a Professor at the Lucerne School of Engineering and Architecture (HSLU), part of the Lucerne University of Applied Sciences and Arts. He holds dual appointments in the Institute of Mechanical Engineering and Energy Technology (IME) where he leads the CC Thermal Energy Storage research group, and the Institute of Natural and Social Sciences (ING). His office is located in Room E300/E311 at Technikumstrasse 21, 6048 Horw, Switzerland. Dr. Schütz earned his Physics degree from ETH Zürich with specialization in theoretical physics and optics. He completed his PhD in 2009 at the University of Zürich's Biochemical Institute, focusing on computer-aided modeling of spectroscopy experiments and pattern recognition in biochemical networks. From 2010-2014, he worked as a researcher at Empa in Dübendorf developing non-destructive testing methods before joining HSLU in September 2014 as a Physics lecturer. He completed a Certificate of Advanced Studies in Higher Education Didactics in 2015 and the 'Exzellenz in der Lehre' program in 2019. Professor Schütz's research spans Non-destructive Testing with emphasis on X-ray computed tomography , Energy System Modeling , and Computational Physics . His work on phase change materials and thermal energy storage has led to significant advancements in understanding calcium chloride hexahydrate solidification and salt hydrate behavior. He combines experimental work with sophisticated computational modeling, including Monte Carlo simulations and high-performance computing approaches. His expertise in algorithm development for large image datasets has applications across energy systems, materials science, and archaeological conservation. His publication record shows a clear evolution from fundamental physics toward applied engineering solutions, with recent work (2023-2025) increasingly focused on practical thermal energy storage applications for residential and district heating systems. The integration of X-ray computed tomography with energy system modeling represents his unique interdisciplinary approach. Professor Schütz actively leads numerous research initiatives including SWEET PATHFNDR, SWEET DeCarbCH TES, WindCoEconomy, and INTERSTORES. He teaches Mathematics & Physics for Engineering students and Time Series Analysis in the Master of Science in Applied Information and Data Science program. His research group operates advanced X-ray computed tomography facilities for studying material properties, energy storage systems, and conservation methods for archaeological materials, bridging theoretical physics with practical engineering applications in the energy sector.

Jeffrey D. BlanchardView profile

Professor

• Applied Harmonic Analysis
• Compressed Sensing
• Matrix Completion
• +7 more

Jeffrey D. Blanchard is a Professor in the Department of Mathematics and Statistics at Grinnell College. He also holds the endowed Donald L. Wilson Professor of Enterprise and Leadership title and directs the Donald and Winifred Wilson Center for Innovation and Leadership. His academic journey includes a Ph.D. from Washington University in St. Louis (2007), followed by a VIGRE Postdoctoral Fellowship at the University of Utah (2007–2009) and an NSF International Research Fellowship at the University of Edinburgh (2010). His research focuses on applied and computational harmonic analysis, particularly compressed sensing, matrix completion, composite dilation wavelets, and GPU-accelerated scientific computing. These areas intersect with digital signal processing, high-dimensional geometry, and random matrix theory. He has developed efficient algorithms for sparse recovery and large-scale data processing, often leveraging graphical processing units for performance gains. The recent publications reflect a consistent trend in algorithmic development for compressed sensing and sparse approximation, with increasing emphasis on GPU implementations and structured sparsity. His work bridges theoretical analysis with practical computational solutions, often resulting in open-access software tools like GAGA, GGKS, and GGMS. NSF International Research Fellowship (2010) NSF Grants: DMS 1112612, DMS 1620390, OISE 0854991 Harris Faculty Fellowship (2013–2014) MAA Project NExT Fellow (2008–2009) Department of Homeland Security Fellowship (2003–2006) Blanchard has advised numerous undergraduate research projects in areas such as GPU-based k-selection, joint sparsity, and composite dilation wavelets. His research has been supported by multiple National Science Foundation grants focused on algorithm analysis and high-performance computing. He has taught a wide range of courses including Calculus, Numerical Analysis, and Wavelet Applications. He leads a research group that develops GPU-accelerated algorithms for compressed sensing and sparse recovery. His software packages—GAGA, GGKS, and GGMS—are publicly available and used for high-performance computing tasks such as greedy pursuit and order statistics selection on GPUs.

• Numerical Relativity
• General Relativistic Hydrodynamics
• Computational Astrophysics
• +7 more

Pablo Cerda Duran is an Associate Professor at the Universitat de València, affiliated with the Faculty of Physics, Department of Astronomy and Astrophysics. He is a member of the Computational Astrophysics and Cosmology (CompAC) research group and previously contributed to the Computer Aided Modeling of Astrophysical Plasma (CAMAP) group. He earned his PhD in 2006 with a thesis on the general relativistic collapse of rotating stellar cores under the supervision of Dr. José Antonio Font Roda. His research lies at the intersection of numerical relativity, computational astrophysics, and high-performance computing. He specializes in developing and applying numerical methods to simulate extreme astrophysical phenomena, particularly involving compact objects such as neutron stars and black holes. His work includes general relativistic hydrodynamics and magnetohydrodynamics, Einstein’s equations, and instabilities in rotating stars. The body of his recent publications reveals a strong focus on algorithmic innovation in numerical relativity—such as implicit time integration schemes, constrained evolution formulations, and iterative solvers—applied to problems in relativistic astrophysics. His simulations span core collapse, neutron star instabilities, and boson star dynamics, often in the context of gravitational wave sources. Email: pablo.cerda@uv.es His scientific contributions appear in leading journals like Classical and Quantum Gravity , Journal of Computational Physics , and Astronomy & Astrophysics . Although no formal awards are listed, his sustained publication record and collaborative network indicate significant recognition in the field. He has co-authored works with prominent figures such as José Antonio Font, Miguel-Ángel Aloy, and Eric Gourgoulhon. Pablo Cerda Duran leads and contributes to advanced computational modeling efforts, advancing the simulation capabilities of relativistic systems. His work continues to support the theoretical interpretation of gravitational wave signals and high-energy astrophysical events.

• Youth Studies
• Social Inequalities
• Information and Communication Technologies (ICT)
• +7 more

Nuno de Almeida Alves is an Associate Professor in the Department of Social Research Methods at the School of Sociology and Public Policies, ISCTE – University Institute of Lisbon. He is also an Integrated Researcher at CIES-Iscte – Center for Research and Studies in Sociology, where he coordinates the Family, Generations and Health Research Group. He serves as Editor of the Portuguese Journal of Social Science and Associate Editor of the Journal of Youth Studies . Education: PhD in Sociology, ISCTE – University Institute of Lisbon, 2006 Bachelor's degree in Sociology, ISCTE – University Institute of Lisbon, 1998 His research focuses on youth, technology, and social inequalities, particularly examining digital inclusion, educational policies, and generational change. He investigates how digital technologies reshape family dynamics, youth transitions, and civic participation. His work often combines qualitative and quantitative methods, emphasizing methodological innovation in the social sciences. The 15 most recent publications highlight a consistent focus on youth, digital media, and inequality, with recurring themes such as precarious employment, ICT in education, intergenerational relations online, and digital citizenship. His work appears in high-impact journals like New Media & Society , Childhood , and Journal of Youth Studies . Scientific Awards: ISCTE-IUL Scientific Awards (2016) ISCTE-IUL Scientific Awards (2015) ISCTE-IUL Scientific Awards (2013) He has supervised numerous doctoral and master’s students, with thesis topics ranging from e-government and digital literacy to media and social movements. He has also held significant academic leadership roles, including Director of the Department of Social Research Methods and Deputy Director of CIES-Iscte. His editorial work and event coordination, such as the CIES Research Forum and LIM Methodological Workshops, reflect his commitment to advancing sociological research and methodology. Labs and Research Groups: Family, Generations and Health Research Group (Coordinator), CIES-Iscte Methodological Innovation Laboratory (LIM), CIES-Iscte (involved in coordination)

• Applied and Computational Mathematics
• Parallel Scientific Computing
• Computational Physics
• +8 more

Juan Antonio Acebrón Torres is an Assistant Professor in the Department of Information Science and Technology at ISCTE-Instituto Universitário de Lisboa (ISCTE-IUL), Portugal, and a researcher at INESC-ID. He is affiliated with the School of Technology and Architecture and actively contributes to research in computational mathematics and high-performance computing. PhD in Mathematical Engineering, Universidad Carlos III de Madrid, 2000 MS in Theoretical Physics, Universidad Autónoma de Madrid, 1994 His primary research interests include applied and computational mathematics, parallel scientific computing, Monte Carlo methods, probabilistic domain decomposition, and nonlinear physics. He develops scalable algorithms for solving partial differential equations and stochastic systems, with applications in computational physics and linear algebra. His work emphasizes efficiency and parallelization for petascale and distributed computing environments. The most recent publications demonstrate a consistent focus on Monte Carlo-based numerical solvers, particularly for matrix functions and PDEs. Key themes include probabilistic domain decomposition, multilevel Monte Carlo methods, random trees, and high-performance implementations. These works appear in top-tier journals like Journal of Computational Physics , SIAM Journal on Scientific Computing , and Future Generation Computer Systems , indicating strong interdisciplinary impact. Associate Editor, Axioms Acebrón has supervised multiple postdoctoral, PhD, and Master’s students, including Francisco Bernal Martinez and Angel Rodriguez-Rozas. His research has been supported through institutional affiliations and collaborative projects. He has not received any explicitly mentioned scientific awards in the provided text. He leads a research group focused on probabilistic numerical methods and high-performance computing, with ongoing projects involving Monte Carlo solvers for matrix functions and PDEs. A postdoctoral position is currently open in his team, indicating active research expansion.

• Applied Physical Chemistry
• Colloids
• Computational Fluid Dynamics (CFD)
• +12 more

Marco Vanni is a Full Professor in the Department of Applied Science and Technology (DISAT) at the Polytechnic University of Turin, where he has been a faculty member since 1991, progressing from University Researcher to Associate Professor (2001) and Full Professor (2012). He is affiliated with the College of Chemical and Materials Engineering and teaches courses in Applied Physical Chemistry, Thermodynamics for Chemical Engineering, and Hydrogen Production Processes. He has served in leadership roles including Coordinator of the PhD Program in Chemical Engineering (2014–2018) and as Contact Person for Chemical and Sustainable Process Engineering degree programs. His research focuses on the behavior of dispersed multiphase systems, integrating computational fluid dynamics (CFD) with models of microscale phenomena such as mass transfer, surface interactions, and population balances. Key areas include nanoparticle precipitation, gas-liquid reactors, and elastomer processing, with applications in energy, pharmaceuticals, and sustainable engineering. The recent publications reveal a strong trend in multiscale modeling of multiphase systems, particularly using advanced moment methods (QMOM, CQMOM, DQMOM) coupled with CFD. His work spans from fundamental colloid dynamics to applied industrial problems, including collaborations with Eni, Pirelli, and Rockwood. The latest articles also show an expanding interest in thermal and mass transfer in wearable systems, indicating interdisciplinary outreach into textile and biomedical engineering. He has supervised multiple PhD students, including Eleonora Bianca, Antonello Raponi, Marco Ferrari, and Lorenzo Vasquez Giuliano, on topics ranging from magnesium hydroxide precipitation to food emulsions and drug delivery systems. Marco Vanni leads and participates in numerous research projects, including EU-funded initiatives like MULTIPHASE Erasmus Mundus and BATCAT, as well as commercial projects with Eni, Pirelli, and others, focusing on reactor simulation, nanoparticle production, and process optimization. He is also involved in applied research on bioreactors, tire production, and filtration technologies. He is a member of research groups such as the Multiscale Modeling for Materials Science and Process Engineering team and works within laboratories including the Center for Multiphysics Modeling of Materials and Processes and the Laboratory for Multiscale and Process Modeling at DISAT.

Diana Lucia Quintero CastroView profile

Associate Professor

• Condensed Matter Physics
• Experimental Physics
• Neutron Scattering
• +8 more

Diana Lucia Quintero Castro is an Associate Professor in Materials Physics at the Department of Mathematics and Physics, Faculty of Science and Technology, University of Stavanger. She has been a faculty member since 2017 and is actively engaged in teaching and research in experimental condensed matter physics. Education: PhD in Physics, Technical University of Berlin (2011), research conducted at Helmholtz Zentrum Berlin Master in Physics, University of Antioquia, Colombia Physics Engineering, National University of Colombia Her research focuses on quantum magnets and functional materials , particularly exploring magnetic and lattice excitations in systems with geometrical frustration and orbital degrees of freedom. She specializes in neutron scattering methods, contributing to instrumentation such as triple-axis spectrometers and polarization analysis. Her current projects include PHUN (2022–2025) on phonon lifetimes and thermal conductivity, and UiScatt (2021–2023), aimed at building a user group for large-scale photon and neutron facilities at UiS. The recent publications show a strong trend in phonon dynamics in materials like SrTiO3, magnetic excitations in quantum magnets such as Sr3Cr2O8 and SrYb2O4, and instrumentation development . Her work bridges experimental neutron scattering with first-principles computations, highlighting interdisciplinary collaboration across institutions like SINTEF, HZB, and DTU. Scientific Awards: No awards explicitly mentioned in the text. She mentors students and early-career researchers, evident from her co-authorship with numerous junior scientists. She has secured competitive funding from the Norwegian Research Council (NRC) and internal UiS grants. Her work involves close collaboration with international labs and facilities, supporting both fundamental science and applications in energy materials. Labs and Teams: She is central to the UiScatt initiative, building a neutron/photon user group at the University of Stavanger. She collaborates with neutron facilities like BER II (HZB) and instruments such as FLEXX and CAMEA, contributing to spectrometer development and data analysis workflows.

Richard KowarView profile

Researcher

• Inverse Problems
• Wave Propagation
• Medical Imaging
• +3 more

Priv.-Doz. Dr. Richard Kowar is a Postdoctoral Researcher at the Department of Mathematics, University of Innsbruck. His research focuses on inverse problems, wave propagation, and mathematical modeling in medical imaging and physics. He specializes in photoacoustic tomography, regularization methods for ill-posed equations, and the analysis of causal wave equations. His work bridges theoretical mathematics with practical applications in biomedical imaging and signal processing. Key research areas include the analysis of dissipative systems, fractional calculus in wave equations, and iterative algorithms for image reconstruction. His studies on causality in wave equations and diffusion models have advanced the understanding of nonlocal operators and their applications. Kowar has contributed to projects like VASCAGE, integrating mathematical techniques with real-world engineering and medical challenges. Publications highlight advancements in photoacoustic imaging with attenuating media, numerical inversion techniques, and theoretical frameworks for causal dynamics. Despite no listed scientific awards, his extensive bibliography demonstrates impactful contributions to applied mathematics and engineering.

Bart De MoorView profile

Professor

• Mathematical Engineering
• System Theory and Control
• Numerical Optimization
• +4 more

Professor Bart De Moor is a full professor at the KU Leuven Faculty of Engineering Sciences, affiliated with the Department of Electrical Engineering (ESAT) and the STADIUS Center for Dynamic Systems, Signal Processing, and Data Analysis. His research spans mathematical engineering, system theory, and biomedical data science, with significant contributions to Machine Learning Medical AI Time Series Analysis High-Dimensional Data Optimization underpinned by an ERC Advanced Grant and the Order of the Crown (2025). His recent work includes AI-driven clinical decision support systems ( BJOG , 2025), spatial omics pipelines for pancreatic cancer ( Cancer Research , 2025), and energy demand forecasting ( Applied Energy , 2025). He has supervised over 93 PhD students and founded 9 spin-offs, including Health House and Athumi .

Kristian DebrabantView profile

Associate Professor

• Numerical Analysis
• Stochastic Models
• Numerical Methods and Simulation
• +7 more

Kristian Debrabant is an Associate Professor at the Department of Mathematics and Computer Science, Faculty of Science and Engineering, University of Southern Denmark. He is also affiliated with the STEM Center for Educational Research – FNUG, highlighting his dual engagement in research and pedagogical development. His academic credentials include a Dr.rer.nat. degree, reflecting a strong foundation in mathematical sciences. Department: Department of Mathematics and Computer Science Position: Associate Professor in Computational Science Additional Affiliation: STEM Center for Educational Research – FNUG Email: debrabant@imada.sdu.dk ORCID: https://orcid.org/0000-0003-2901-162X His research lies at the intersection of numerical analysis and stochastic modeling, with a focus on developing and analyzing numerical methods for stochastic differential equations (SDEs). Key areas include Runge-Kutta methods, exponential integrators, weak and strong convergence, and applications in computational science. He also contributes to interdisciplinary work, such as urban traffic modeling and environmental systems. Recent publications show a consistent trend in advancing numerical schemes for SDEs, particularly those with non-standard noise (e.g., fractional Brownian motion), stiff systems, and non-globally Lipschitz coefficients. His work spans theoretical developments in convergence and order conditions to practical implementations in finance, ecology, and engineering. The integration of B-series and tamed schemes reflects a deep engagement with both classical and modern numerical frameworks. Kristian Debrabant supervises PhD students, as indicated by the 'PhD supervision (2)' note in his profile. While specific grant details are not listed, his publication record and active research suggest ongoing funding support. His involvement with the STEM Center implies leadership or significant contribution to educational innovation in STEM disciplines.

Sergio PirozzoliView profile

Professor

• Computational Fluid Dynamics
• Turbulence
• High Performance Computing
• +4 more

Sergio Pirozzoli is a Full Professor at the Department of Mechanical and Aerospace Engineering , Sapienza University of Rome. His research focuses on Computational Fluid Dynamics (CFD) , Turbulence , High-Performance Computing , and Supersonic/Hypersonic Flow modeling. He specializes in developing advanced numerical solvers for compressible and turbulent flows, with applications to aerospace, thermal engineering, and environmental systems.

Korshunov Maxim MikhailovichView profile

Associate Professor

• Superconductivity
• Quantum Physics
• Condensed Matter Physics
• +7 more

Maxim Mikhailovich Korshunov is an Associate Professor and Head of the Department of Theoretical Physics and Wave Phenomena at the Institute of Engineering Physics and Radioelectronics, Siberian Federal University. He also serves as Chief Researcher at the Laboratory of Physics of Magnetic Phenomena, L.V. Kirensky Institute of Physics, FRC KSC SB RAS. With 23 years of experience in his specialty, he has established himself as a leading researcher in condensed matter physics and superconductivity. His educational background includes a PhD in Condensed Matter Physics (2004) and a D.Sc. in Condensed Matter Physics (2015) from the Kirensky Institute of Physics. In 2022, he was elected as a Corresponding Member of the Russian Academy of Sciences and in 2018 was awarded the academic title of Associate Professor in Theoretical Physics. Korshunov's research focuses on quantum macrophysics, specifically the description of quantum systems and manifestations of quantum properties at macroscopic scales. His work encompasses superconductivity, photophysical processes, nanosystems, quantum transport, and the structure and properties of complex systems and metalloorganic compounds. He has developed theoretical models for layered transition metal compounds, particularly examining the relationship between magnetism and unconventional superconductivity. Analysis of his recent publications reveals a strong focus on iron-based superconductors, cuprates, and quantum materials. His work frequently addresses spin-orbit coupling, electronic structure, superconducting order parameters, and the effects of doping and pressure on quantum materials. A significant portion of his research investigates the interplay between magnetism and superconductivity, particularly in iron pnictides and chalcogenides. Medal of the ORDER "FOR SERVICE TO THE FATHERLAND" 2nd degree (2024) Letter of thanks from the Russian Academy of Sciences (2023) Medal "For Impeccable Work and Distinction" of the Ministry of Education and Science (2022) Top Peer Reviewer 2019 Award, Publons — Clarivate Analytics (2019) Publons Peer Review Awards 2018 (2018) State Prize of Krasnoyarsk Krai in professional education (2012) Prize of the Siberian Branch of the Russian Academy of Sciences named after Academician L.V. Kirensky (2012) Korshunov has led multiple research grants, including projects funded by the Russian Foundation for Basic Research (RFBR) on "Spin fluctuations as a source of superconducting pairing in iron selenides" and "Effects of electronic correlations and multi-orbital physics in iron compounds and cuprates." His research has been supported by the Dynasty Foundation and the Russian Science Foundation. He has also contributed to educational development through numerous methodological materials for courses in statistical physics, quantum theory of solids, and field theory methods. As Head of the Department of Theoretical Physics and Wave Phenomena, Korshunov leads a research team investigating quantum phenomena in condensed matter systems. His laboratory focuses on theoretical modeling of superconducting and magnetic materials, with particular emphasis on iron-based superconductors and cuprates. The team employs advanced computational methods including cluster perturbation theory and ab initio calculations to understand the electronic structure and quantum properties of these materials.

• Petri nets
• Discrete event systems
• Flexible manufacturing systems
• +4 more

Daniel Yuh Chao is a Professor in the Department of Management Information Systems at National Chengchi University's College of Commerce, specializing in Petri nets, discrete event systems, and flexible manufacturing systems control. His academic career at NCCU spans from Associate Professor (1994-1997) to full Professor (1997-present), with recognition as a Distinguished Professor in 2008. National Chengchi University, Department of Management Information Systems (1994-present) Ph.D. in EECS from University of California, Berkeley (1983-1987) M.S. in Electrical Engineering from University of California, Los Angeles (1980-1981) B.S. in Mechanical Engineering from National Taiwan University (1972-1976) Professor Chao's research focuses on Petri net theory and applications, particularly in deadlock prevention and control of flexible manufacturing systems. His work has established significant theoretical foundations for siphon-based control, elementary siphon analysis, and maximally permissive controllers. He has developed innovative approaches for state enumeration, controller synthesis, and simplification of control structures without requiring full reachability analysis. His publication record shows consistent high-impact research from 1986 through 2014, with over 80 journal and conference papers. The most recent works (2013-2014) demonstrate continued advancement in recursive state computation methods, closed-form controller solutions, and structural analysis of weakly dependent siphons, with applications to increasingly complex manufacturing systems. Distinguished Professor at National Chengchi University (2008) Professor Chao has supervised numerous research projects on deadlock analysis and Petri net applications in manufacturing systems. His theoretical contributions have practical implications for industrial automation, particularly in developing efficient control strategies for complex resource-sharing systems. His research group has produced significant advancements in computational methods for Petri net analysis, reducing the complexity of controller synthesis while maintaining system liveness and performance. His laboratory work focuses on applying Petri net theory to real-world manufacturing challenges, with particular emphasis on developing computationally efficient control algorithms that can scale to large industrial systems. Current research directions include extending control methodologies to more general Petri net structures and developing closed-form solutions for controller synthesis in infinitely large systems.

• Nonlinear Dynamics and Control
• Model Order Reduction
• Flexible Multibody Dynamics
• +2 more

Dr. Li Mingwu is an Assistant Professor and doctoral supervisor in the Department of Mechanics and Aerospace Engineering at Southern University of Science and Technology (SUSTech). He joined SUSTech in 2023 after completing a postdoctoral fellowship at ETH Zurich (2020-2022). Education: B.Eng. in Engineering Mechanics (2013), Huazhong University of Science and Technology M.Eng. in Computational Mechanics (2016), Dalian University of Technology Ph.D. in Mechanical Engineering (2020), University of Illinois at Urbana-Champaign His research focuses on nonlinear dynamics and control, with achievements in high-dimensional nonlinear vibration model reduction, spacecraft trajectory optimization algorithms, complex mission design, fluid-structure interaction, and flexible multibody dynamics. He has published over 20 first/corresponding author papers and developed key software tools: COCO (Continuation Core for nonlinear analysis) SSMTool 2.x (Exact Nonlinear Model Reduction Toolbox) Research trends include: Exact reduced-order models for nonlinear systems Applications in soft robotics and rotating machinery Bifurcation analysis and multistability phenomena Computational optimization for control systems Scientific recognition: NODYCON23 Best Paper Award (twice) ICDVC 2022+1 Best Oral Presentation Award Organizer of ICVE 2021 and ECCOMAS 2022/2024 Young Editor of Acta Mechanica Sinica Guest Editor for Journal of Dynamics and Control special issue Teaching contributions include courses on: Vibration Theory (MAE318) Advanced Numerical Analysis (MAE5002) Nonlinear Dynamics and Chaos (MAE5034)

Craig DouglasView profile

Professor

• Dynamic Data-Driven Application Systems
• Big Data
• High Performance Computing
• +6 more

Dr. Craig Douglas is a Professor of Mathematics and Statistics at the University of Wyoming's College of Engineering and Physical Sciences, with an Adjunct Professor appointment in Computer Science. He earned his Ph.D. from Yale University and is globally recognized for pioneering research in Dynamic Data-Driven Application Systems (DDDAS) for Big Data, high-performance computing, and multigrid algorithms for partial differential equations. He co-developed the first commercial DDDAS for oil and gas pipeline monitoring, deployed across 100+ countries. His work bridges theoretical mathematics and practical engineering challenges. Research Interests Dynamic Data-Driven Application Systems (DDDAS) High-Performance Computing (HPC) Multigrid Algorithms Big Data Analytics for Energy and Environment Computational Science Applications Recent Publications Advances in DDDAS frameworks Computational wildfire modeling CO 2 sequestration simulation Finite-difference and finite-element methods Cache optimization for multigrid algorithms Email: cdougla6@uwyo.edu