جستجوی استاد راهنمای دانشگاهی

Fangbao Tianمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics
• Fluid-Structure Interaction
• Biomechanics
• +۵ مورد دیگر

Associate Professor Fangbao Tian is a distinguished researcher and academic at UNSW Canberra's School of Engineering and Technology, where he also serves as Deputy Head of School for Research since July 2023. Previously, he held positions as Senior Lecturer (2017-2021) and Lecturer (2014-2017) at the same institution after completing postdoctoral research at Vanderbilt University. His academic journey began with a BSc (2006) and PhD (2011) in Theoretical and Applied Mechanics and Engineering Mechanics from the University of Science and Technology of China. Dr. Tian's research focuses on Computational Fluid Dynamics (CFD) tools for complex flows and fluid-structure interaction, with particular emphasis on bio-inspired applications. His work spans modeling laryngeal aerodynamics and vocal-fold vibration, fluid-structure interaction of plates in viscous fluid, fish swimming and insect flight, blood flow dynamics, and non-Newtonian flow phenomena. Recent work has expanded into Martian atmosphere aerodynamics, showing his research's growing interdisciplinary nature. His extensive publication record demonstrates consistent contributions across fluid dynamics, with recent trends showing increasing focus on compressible flows, bio-inspired flight systems, heat transfer applications, and computational methods like Lattice Boltzmann approaches. The research shows strong connections between fundamental fluid mechanics and practical applications in aerospace, biomedical engineering, and environmental systems. UNSW Canberra Goldstar Award 2022 IEEE Outstanding SMCS Chapter Award 2021 Outstanding Volunteer Award 2021 UNSW Canberra Silverstar Award 2018 UNSW Canberra Silverstar Award 2017 Journal of Fluids and Structures Highly Cited Research 2017 ARC DECRA 2016 Dr. Tian actively supervises PhD students across diverse topics including bushfire-enhanced wind loads, bio-inspired flight on Mars, flow control optimization, and fluid-structure interactions in compressible flows. He has secured over $5 million in external funding as Chief Investigator, including significant Australian Research Council projects examining Martian atmosphere aerodynamics, bio-inspired flapping wings, and cardiovascular flow modeling. His editorial roles include Associate Editor for Journal of Fluids and Structures and Scientific Reports, reflecting his standing in the fluid dynamics research community.

Mauro Werderمشاهده پروفایل

مدرس

• Glaciology
• Hydrology
• Numerical Modeling
• +۷ مورد دیگر

Dr. Mauro Werder is a Lecturer at the Department of Civil, Environmental and Geomatic Engineering at ETH Zurich. His work focuses on glaciology, subglacial hydrology, and numerical modeling, combining computational methods with field measurements. He has developed widely used models such as GlaDS (Glacier Drainage System) and BITE (Bayesian Ice Thickness Estimation), and contributed to projects like SHMIP and 4D-Antarctica. Current Projects: Gladder (2025-2028), DIWING (2023-2026), LEAD (2020-2026), 4D-Antarctica (2019-2022), CORDS (2023-2024) Education: PhD in Glaciology (2009, Swiss National Science Foundation funded) His research spans subglacial drainage systems, sediment transport (SUGSET model), Bayesian inversion techniques, and field experiments involving artificial lakes and R-channels. He actively teaches courses on GPU-based PDE solving, applied glaciology, and reproducible scientific computing. Scientific Awards: Swiss National Science Foundation (SNF) Fellowship for Prospective Researchers (2010-2011) European Union (FP7) Marie Curie International Outgoing Fellowship (2011-2014) He collaborates with institutions like the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), and contributes to software development through packages like BITEmodel.jl and Parameters.jl. His fieldwork includes experiments on Greenland's Jakobshavn Isbræ and Switzerland's Plaine Morte glacier.

William Harbertمشاهده پروفایل

استاد

• Geophysics
• Seismic Processing
• Microseismic Analysis
• +۵ مورد دیگر

William Harbert is a Professor in the Department of Geology and Environmental Science at the University of Pittsburgh, where he leads research in geophysics and subsurface characterization. His work bridges fundamental geophysical principles with practical applications in energy and environmental systems. Education: MS in Exploration Geophysics from Stanford University PhD in Geophysics from Stanford University Research focuses on seismic analysis across multiple scales, from micro-CT to surface seismic. His group specializes in advanced processing of microseismic, reflection seismic, and VSP data to image subsurface structures and understand pore-scale dynamics. Current work integrates deep learning for geophysical object detection and classification, with emphasis on organic shale systems and CO 2 storage monitoring. Key areas include rock physics, microseismicity analysis, and environmental geophysics for water quality assessment. Publication trends show strong emphasis on energy-related geophysics, particularly hydraulic fracturing monitoring, CO 2 sequestration verification, and unconventional reservoir characterization. Recent work increasingly incorporates machine learning techniques and addresses environmental monitoring challenges in subsurface operations. Scientific recognition: DOE ORISE Research Associate Resident Institute Fellow of the NETL-Institute for Advanced Energy Solution Professional engagements include membership on the Altarock Review Board for DOE-funded geothermal projects and prior service on the Scientific Advisory Board for the In Salah CO 2 Injection Project. His research involves extensive collaboration with national laboratories and industry partners on subsurface monitoring technologies. His laboratory group develops advanced geophysical processing techniques for subsurface imaging across multiple scales, with current projects focusing on microseismic monitoring of shale reservoirs and CO 2 storage sites.

Ethan N. Epperlyمشاهده پروفایل

پژوهشگر ارشد

• Randomized algorithms
• Quantum algorithms
• Scientific computing
• +۳ مورد دیگر

Ethan N. Epperly is a Miller Research Fellow in the Department of Mathematics at the University of California, Berkeley, where he conducts cutting-edge research in applied mathematics with a focus on computational techniques for large-scale problems. Dr. Epperly received his PhD in Applied and Computational Mathematics from Caltech, where his research was supported by a Department of Energy Computational Science Graduate Fellowship. His educational background established a strong foundation in both theoretical and applied mathematics. His primary research interests include randomized and quantum algorithms, scientific computing, and large-scale machine learning. Dr. Epperly specializes in designing computational techniques for solving large-scale problems in machine learning, quantum information, and scientific computing, with particular expertise in kernel matrix approximation, low-rank approximation, and numerical linear algebra problems. His work bridges theoretical analysis with practical computational efficiency. Epperly's recent publications demonstrate a strong focus on developing efficient randomized algorithms for matrix computations. His research shows how randomized approaches can achieve accuracy and stability comparable to classical methods while offering significant computational advantages, particularly in settings where computational resources are limited. His work on Krylov subspace methods, Cholesky decomposition variants, and trace estimation has advanced the field of numerical linear algebra. Hertz foundation fellowship finalist Thomas A. Tisch Prize for Graduate Teaching in CMS W. P. Carey & Co. Prize in Applied Mathematics SIAM Student Paper Prize Department of Energy Computational Science Graduate Fellowship As a Miller Research Fellow, Dr. Epperly collaborates with leading researchers including Joel A. Tropp, Robert J. Webber, and Yifan Chen. His work has significant implications for machine learning applications requiring efficient handling of large-scale matrix computations, with potential applications across scientific computing and quantum information processing.

Guillaume Chiavassaمشاهده پروفایل

استاد

• Wave Propagation
• Numerical Analysis
• Computational Fluid Dynamics
• +۶ مورد دیگر

Guillaume Chiavassa is a Professor in Applied Mathematics at Ecole Centrale de Marseille, affiliated with the Laboratoire M2P2 (Mechanics, Modeling and Physical Processes Laboratory). He leads research in the Thermodynamics, Waves, Digital, Interfaces and Combustion team, focusing on advanced computational methods for complex physical phenomena. His research spans wave propagation in porous media, numerical modeling of plasma flows in Tokamak configurations, multilevel schemes for conservation laws, penalization methods for compressible flows, and wavelets in numerical analysis. Chiavassa's work demonstrates exceptional mathematical rigor applied to challenging physical systems, particularly in nonlinear wave dynamics and computational fluid mechanics. His methodologies bridge theoretical mathematics with practical engineering applications. Analysis of his recent publications reveals a strong focus on wave propagation phenomena across diverse media, with significant contributions to numerical methods for nonlinear systems. His work consistently addresses the mathematical challenges of modeling complex physical behaviors including material softening, fractional attenuation in porous media, and plasma dynamics in fusion devices. The interdisciplinary nature of his research connects applied mathematics with mechanical engineering, geophysics, and nuclear fusion technology. Chiavassa leads the PROSPERO Software project and participates in the ANR Espoir research initiative and the Consortium SEISCOPE. His teaching activities include courses on hyperbolic equations, finite elements, and heat transfer, with practical computational components developed for student instruction. He maintains an active research program through Laboratory M2P2, where his team develops advanced numerical methods for simulating complex physical phenomena with applications ranging from environmental engineering to nuclear fusion research.

Stuart Longمشاهده پروفایل

دانشیار

• Antenna Design
• Applied Electromagnetics
• MRI Safety Testing
• +۳ مورد دیگر

Stuart Long is an associate dean of undergraduate research and faculty member at the Honors College of the University of Houston, where he serves as the academic adviser for all honors students majoring in Electrical and Computer Engineering. He teaches courses on electromagnetic waves and conducts research in antenna design and applied electromagnetics. Education: Received his doctorate from Harvard University. Stuart Long's research focuses on biomedical applications of electromagnetics, particularly MRI safety testing for implantable medical devices. His work addresses RF-induced heating, electromagnetic compatibility, and safety protocols for devices such as orthopaedic implants and active implantable systems. Recent publications emphasize computational modeling, machine learning, and historical advancements in antenna design. His scholarly contributions include the 2024 Distinguished Achievement Award, the 2018 Chen-To Tai Distinguished Educator Award, and the 2014 John Kraus Antenna Award. These honors reflect his leadership in electromagnetic safety research and engineering education. Stuart Long has actively contributed to improving pedagogy in engineering education, particularly through collaborative learning and retention workshops for diverse student populations. His academic advising role supports the integration of rigorous technical training and interdisciplinary research opportunities for honors students.

Chuanfei Dongمشاهده پروفایل

استادیار

• Star-Terrestrial Planet Interactions
• Magnetic Reconnection
• Turbulence
• +۵ مورد دیگر

Chuanfei Dong is an Assistant Professor of Astronomy at Boston University's College of Arts & Sciences and of Electrical and Computer Engineering at the College of Engineering. His research focuses on understanding plasma physics and its applications to space science, planetary atmospheres, and fusion energy. Dong joined BU in January 2023 after working as a staff scientist at the Princeton Plasma Physics Laboratory. Education: B.S. in Space Science from University of Science and Technology of China M.S. in Earth and Atmospheric Sciences from Georgia Institute of Technology M.S.E. in Nuclear Engineering and Radiological Sciences from University of Michigan M.S. in Planetary and Space Sciences from University of Michigan Ph.D. in Scientific Computing from University of Michigan Research Interests: Dr. Dong's research spans multiple disciplines within space physics and plasma science. His primary interests include Star-Terrestrial Planet Interactions in our Solar System and beyond, magnetic reconnection and turbulence phenomena, wave-particle interactions in space plasmas, and applications of physics-informed machine learning to plasma problems. He also investigates high-intensity laser-plasma interactions with applications to fusion energy research. His work bridges the gap between theoretical plasma physics and observational space science, with particular focus on planetary atmospheres, solar wind interactions, and exoplanet habitability. Dong's interdisciplinary approach combines computational modeling, observational data analysis, and theoretical frameworks to address fundamental questions in space physics. Research Trends: Dong's recent publications demonstrate a strong focus on applying advanced computational techniques to space plasma physics problems. His work spans solar system bodies including Earth, Mars, Mercury, and the Moon, with increasing attention to exoplanet systems. A notable trend is the integration of machine learning approaches with traditional plasma physics modeling, particularly for complex phenomena like Landau damping and magnetic reconnection. His research has significant implications for understanding atmospheric evolution, space weather, and potential habitability of planetary bodies. Scientific Awards: DOE Early Career Research Award (2023) - $875,000 grant for plasma turbulence research Alfred P. Sloan Research Fellow (2024) Metcalf Travel Award Advising and Grants: Dr. Dong mentors undergraduate research assistants and plans to expand his research group with the support of his DOE Early Career Award, which will fund a graduate student and postdoctoral researcher. His research is supported by the Department of Energy and has connections to NASA missions including MAVEN (Mars) and BepiColombo (Mercury). Dong is also involved with the Mauve telescope project as BU institutional PI. His work has been featured in numerous media outlets including Phys.org, Science Daily, and German TV program zdf/3sat. Labs and Teams: Dr. Dong leads a research group focused on computational plasma physics at Boston University. He collaborates with researchers at Princeton Plasma Physics Laboratory and is involved with multiple NASA missions. His team develops advanced computational models to simulate space plasma phenomena, with particular expertise in magnetohydrodynamics (MHD), particle-in-cell methods, and physics-informed machine learning approaches. Dong is also affiliated with BU's Hariri Institute for Computing.

• Biomaterials
• Biomedical Engineering
• Non-clinical testing methodology for Medical Devices
• +۳ مورد دیگر

Professor Kiyotaka Iwasaki at Waseda University's Faculty of Science and Engineering is a leading figure in biomedical engineering with a focus on cardiovascular device development , tissue engineering , and regulatory science . His career spans over two decades at Waseda University, including roles as Associate Professor (2006-2014) and positions at Harvard Medical School's Laboratory for Tissue Engineering. Holding a Doctor of Engineering from Waseda, he serves on numerous international regulatory committees and has contributed to ISO/TC194 standards for medical devices. 1993-2002: Waseda University Education in Mechanical Engineering 2001-2004: Research Associate at Waseda University 2004: Research Scientist at Harvard Medical School 2018-Present: Professor at Waseda University His research interests include Non-clinical testing methodologies for medical devices Regulatory science frameworks Tissue engineering for ligament and cardiac applications Cardiovascular biomedical engineering His scientific contributions reveal through Development of decellularized tissue grafts for orthopaedic surgery Innovations in 3D cardiac tissue engineering using fibrin-based cell sheet stacking Pioneering bioresorbable stent technology with magnesium alloys Creation of biomechanical simulators for valvular disease modeling His awards span from the 2021 Japanese Ministerial Science Commendation 2020 JSME Standards Award 2018 ARIA Innovation Award 2001 ASAIO Fellowship While his publications demonstrate expertise in Vascular and cardiac device testing Bioresorbable stent evaluation Machine learning in medical device regulation Decellularized tissue applications

• Superconducting Logic
• Bio-based Architectures
• Novel Computing Mediums
• +۶ مورد دیگر

Dr. Jennifer Volk is an Assistant Professor at the College of Engineering, University of Wisconsin-Madison, specializing in Electrical & Computer Engineering. Her research focuses on leveraging novel technologies like superconductor electronics and photonics to create efficient systems for datacenters, neuromorphic computing, quantum computing, and space/sensing applications. She employs a holistic approach spanning circuit design, materials science, and computer microarchitecture. PhD (2024), University of California, Santa Barbara BS (2016), University of California, Santa Cruz Her research interests include superconducting logic , bio-based architectures , and novel computing mediums , emphasizing co-optimization of logic and circuit blocks. Her work develops design abstractions to simplify adoption of unconventional technologies. Dr. Volk's publications demonstrate expertise in superconducting circuit design, radiation-hardened CMOS for particle physics, and photonic materials. She has received numerous awards including the 2025 John D. Wiley Assistant Professorship and IEEE fellowships in applied superconductivity. 2025 John D. Wiley Assistant Professorship 2024 UC Santa Barbara President's Dissertation Year Fellowship 2023 IEEE CSC Graduate Study Fellowship in Applied Superconductivity 2022 IEEE Micro Top Picks Honorable Mention 2021 IEEE Micro Top Picks She teaches E C E 340 - Electronic Circuits I (Spring 2025). Her work bridges materials science, circuit design, and system architecture to enable next-generation computing platforms.

Roman Kremsمشاهده پروفایل

استاد

• Quantum Physics
• Machine Learning
• Quantum Chemistry
• +۳ مورد دیگر

Roman Krems is a Professor and Distinguished University Scholar at the University of British Columbia (UBC) in the Department of Chemistry, with affiliations to the Stewart Blusson Quantum Matter Institute. His research focuses on the intersection of quantum physics, machine learning, and chemistry, particularly in quantum materials and quantum technologies such as quantum computing and sensing. Key Roles: Professor at UBC (2013–present), Distinguished University Scholar (2017–present) Education: Ph.D. from Göteborg University (2002), Postdoctoral Fellow at Harvard-MIT Center for Ultracold Atoms (2003–05) Research Interests include: Quantum machine learning (QML) for solving complex physics problems Quantum scattering theory in electromagnetic fields Applications of quantum computing to chemistry Developing machine learning algorithms for quantum dynamics Recent publications highlight advancements in extrapolating quantum observables, Gaussian process models for collision dynamics, and quantum walks in disordered systems. His work bridges theoretical physics, computational methods, and experimental applications in cold molecule research. Scientific Awards include the UBC Killam Teaching Prize (2017), election as Fellow of the American Physical Society (2015), and the Keith Laidler Award (2013). He has held editorial board positions for journals such as Machine Learning: Science & Technology and New Journal of Physics . Research Group members include graduate students and postdocs working on quantum technologies, machine learning, and molecular scattering. He also contributes to outreach through invited talks and seminars at institutions like MIT and Lawrence Berkeley National Laboratory.

Patrick S. Marketمشاهده پروفایل

استاد

• Synoptic and Mesoscale Dynamics
• Winter Weather
• Heavy Rainfall
• +۶ مورد دیگر

Dr. Patrick S. Market is a Professor of Atmospheric Science and currently serves as the Director of the School of Natural Resources at the University of Missouri. He also acts as Interim Co-Director of the Missouri Water Center. His research focuses on synoptic and mesoscale dynamics, particularly winter weather, heavy rainfall, flash flooding, and severe local storms. He has contributed to advancements in precipitation efficiency studies and operational forecasting techniques. His work explores the role of artificial intelligence in weather prediction and communication, emphasizing the continued importance of human expertise in an automated forecast process. Dr. Market has secured grants for data stream maintenance and digital equity planning, and he has led educational initiatives integrating research into synoptic meteorology classrooms. Notable collaborations include projects with the National Weather Service and studies on the Ozark Plateau's topographical influence on weather systems.

Ming-Jun Laiمشاهده پروفایل

استاد

• Multivariate Splines
• Sparse Solutions of Underdetermined Linear Systems
• Wavelet Theory
• +۸ مورد دیگر

Ming-Jun Lai is a Professor in the Department of Mathematics at the University of Georgia. His career spans decades, focusing on multivariate splines, sparse solutions of linear systems, wavelet theory, and their applications in numerical analysis and machine learning. Education: Lai received his Ph.D. from Texas A&M University and completed postdoctoral training at the University of Utah. He has supervised 22 Ph.D. students and two current Ph.D. candidates. Multivariate Splines: Applied to scattered data fitting, numerical PDE solutions, image enhancement, and surface design. Sparse Solutions: Used in compressed sensing, low-rank matrix recovery, and graph clustering. Wavelet Theory: Construction of biorthogonal and tight wavelet frames for image edge detection. Optimal Transport: Numerical solutions for Monge-Ampère equations. Research Trends (2025–2023): Recent work includes interpolating space curves with geometric continuity, spherical spline smoothing, and applications in machine learning, particularly graph clustering and optimal control in biological systems. Scientific Awards: UGA Research Medal (2002) McCay Award (2013) Advisees: Lai has mentored 24 Ph.D. students, including Zhaiming Shen (2024), Jinsil Lee (2023), and current students Valerio Palamra and Ye Tian. Laboratory & Collaborations: He collaborates with institutions like Georgia Tech, UCLA, and Zhejiang University, applying splines in aerospace engineering and biomedical imaging.

Johannes Brandstetterمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Geometric Deep Learning
• +۵ مورد دیگر

Johannes Brandstetter is an Associate Professor at the Institute for Machine Learning at Johannes Kepler University Linz (JKU) where he leads the "AI for data-driven simulations" research group. He is also Co-founder and Chief Scientist at Emmi AI, bridging academic research with industrial applications in AI-driven physics simulation. Brandstetter earned his PhD after working at CERN's CMS experiment on Higgs boson physics. In 2018, he transitioned to machine learning, joining Sepp Hochreiter's research group in Linz. From 2021-2023, he worked at the Amsterdam Machine Learning Lab under Max Welling and Microsoft Research, developing expertise in Geometric Deep Learning and neural surrogates for partial differential equations. He returned to JKU in October 2023 to establish his own research group. His research spans Machine Learning, Deep Learning, and Physics-Informed Machine Learning with focus areas including Neural PDE solvers, Computational Fluid Dynamics, and Climate Modeling. Brandstetter believes AI is poised to revolutionize industrial-scale simulations, potentially saving thousands of compute hours across engineering domains. His work integrates computer vision, numerical simulation, and engineering components to advance data-driven approaches. Recent publications reveal a strong trend toward foundation models for scientific applications, particularly in atmospheric modeling (Aurora), geometric deep learning, and neural surrogates for complex physical systems. His interdisciplinary work spans computer vision, climate science, computational physics, and engineering, demonstrating the versatility of his research approach. Principal Investigator for "AlKa-DL: Alpine karst spring discharge prediction" (FWF-funded, 2024-2027) Principal Investigator for Cluster of Excellence "Bilateral Artificial Intelligence" (FWF-funded, 2024-2029) Co-PI for "Fast, efficient and flexible CFD simulation through generative AI" (FFG-funded, 2025-2026) As an educator and researcher, Brandstetter actively engages with the scientific community through invited talks at major conferences including presentations on "Closing the Gap Between Scientific Foundation Models and Real-World Applications" (March 2025) and "Scientific Machine Learning for Science and Engineering" (February 2025).

• water resources planning and management
• systems thinking and modelling
• sustainable development
• +۷ مورد دیگر

Evan Davies is a Professor in the Civil and Environmental Engineering Department at the University of Alberta's Faculty of Engineering. He has been a Full Professor since July 2021, following his promotion from Associate Professor (2015-2021) and Assistant Professor (2009-2015) positions at the same institution. Education: Ph.D. (Civil and Environmental Engineering), The University of Western Ontario, London, Ontario (2003-2007) M.E.S. (Environment and Resource Studies), The University of Waterloo, Waterloo, Ontario with field research in China and India (2001-2003) B.A.Sc. (Systems Design Engineering), The University of Waterloo, Waterloo, Ontario, including a year-long exchange at Technical University of Hamburg-Harburg, Germany (1995-2001) Evan Davies' primary research focuses on water resources planning and management, systems thinking and modeling, and sustainable development. His work develops and applies hydrological, water use, and water quality models to understand complex feedbacks among water availability, use, and quality within their social, economic, and environmental contexts. His research spans municipal to global spatial scales and daily to decadal time scales, aiming to provide decision-makers with tools to compare structural, management, and policy alternatives for sustainable water planning. His recent projects include global and regional-scale modeling of water security and the water-energy-food nexus, irrigation reservoir management, municipal water demand projections, flood risk management, and chloramine dissipation in stormwater pipes. Recent research trends show a strong focus on: Integrated assessment modeling of water-energy-food systems Climate change impacts on water resources Machine learning applications in hydrology Water security under decarbonization scenarios Flood risk assessment and management Sustainable urban water systems Scientific Awards: Faculty of Engineering Graduate Teaching Award, University of Alberta (2020-2021) Faculty of Engineering Undergraduate Teaching Award, University of Alberta (2018-2019) Doctoral Fellowship (CGS), Natural Sciences and Engineering Research Council (2005-2007) University of Western Ontario Graduate Tuition Scholarship (2005-2007) Ontario Graduate Scholarship in Science and Technology (2004-2005) Masters/Doctoral Fellowship (PGS A/B), Natural Sciences and Engineering Research Council (2002-2004) Davies has supervised numerous graduate students working on projects related to water resources planning and management. His research has been supported by various grants, including funding from the Natural Sciences and Engineering Research Council. He collaborates extensively with researchers at the Joint Global Change Research Institute (JGCRI) in College Park, MD, and with government agencies and industry partners on water management projects across Canada, particularly in Alberta's Bow River basin. Davies leads a research group focused on water resources systems modeling, which employs system dynamics, optimization techniques, and machine learning approaches to address complex water management challenges. His team collaborates with decision-makers and stakeholders to ensure research outcomes are directly applicable to real-world water management problems.

• Numerical Analysis
• Scientific Computing
• Data Science
• +۴ مورد دیگر

Per-Gunnar Martinsson serves as Professor of Mathematics and Deputy Director of the Oden Institute at The University of Texas at Austin, holding the W. A. "Tex" Moncrief, Jr. Endowment in Simulation-Based Engineering and Sciences. He concurrently acts as Affiliated Professor of Mathematics at the Royal Institute of Technology (KTH) in Stockholm, where he chairs the MathDataLab scientific advisory board. Educational background: Ph.D. in Computational and Applied Mathematics, UT-Austin (2002) His research spans numerical analysis, scientific computing, and data science with emphasis on randomized linear algebra methods, accelerated direct solvers for elliptic PDEs, structured matrix computations, and applications in computational fluid dynamics and acoustics. Recent work extends to boundary integral equations, heterogeneous materials modeling, and lattice equations. Scientific awards: Germund Dahlquist Prize by SIAM (2017) Dr. Martinsson leads research initiatives through the Oden Institute's Center for Numerical Analysis and Center for Scientific Machine Learning, while providing strategic oversight to KTH's MathDataLab as chair of its scientific advisory board.