جست‌وجوی استاد و پژوهشگر

David Warneمشاهده پروفایل

مدرس

• Statistical Data Science
• Bayesian Inference
• Computational Biology
• +۳ مورد دیگر

Dr. David Warne is a Lecturer in Statistical Data Science at the School of Mathematical Sciences , Queensland University of Technology (QUT). He is affiliated with the Centre for Data Science and the ARC Centre of Excellence for Mathematical and Statistical Frontiers . His research focuses on Bayesian inference, computational statistics, and their applications in ecology, environmental science, and computational biology. David holds a PhD in Mathematics (QUT), a BMath/BInfoTech (QUT), and a BInfoTech (Hons) (QUT). Research Interests: Bayesian methods, likelihood-free inference, parameter identifiability, stochastic modeling, and applications to ecological systems and environmental decision-making. His work bridges statistical theory with computational tools to address complex biological and environmental problems. Publications: David’s recent work emphasizes innovative statistical approaches for model calibration, uncertainty quantification, and decision analysis in ecological and biomedical contexts. Key themes include multifidelity Bayesian inference, model misspecification, and agent-based modeling of tumor growth. Awards: He received the Outstanding Doctoral Thesis Award during his PhD at QUT. His research has been published in high-impact journals such as Journal of Computational Physics , Ecological Indicators , and Bulletin of Mathematical Biology . Supervision: Topics include stochastic process modeling, coral reef recovery, and scalable Bayesian inference. Labs/Teams: Active contributor to the Centre for Data Science and the ARC Centre of Excellence for Mathematical and Statistical Frontiers.

Nicholas Hillsمشاهده پروفایل

استاد

• Computational Fluid Dynamics (CFD)
• Gas Turbine Engineering
• Parallel Computing
• +۳ مورد دیگر

Professor Nicholas Hills is a Professor of Computational Engineering and Faculty Associate Dean of the Doctoral College at the University of Surrey. He leads the Rolls-Royce University Technology Centre (UTC) in Thermo-Fluid Systems, focusing on advanced computational methods for gas turbine engineering. His research emphasizes massively parallel CFD simulations, multi-physics modeling, and thermal-fluid systems optimization. Previously, he held the Royal Academy of Engineering/Rolls-Royce Chair and served as Head of the Department of Mechanical Engineering Sciences until 2021. His work has been funded by EPSRC, InnovateUK, the EU, and industry partners. Research interests include turbine aerodynamics, high-pressure systems, and multi-physics frameworks for industrial applications. He leads the UK Applied Aerodynamics Consortium, leveraging national supercomputing resources for aerospace innovation.

Daniele Schiavazziمشاهده پروفایل

دانشیار

• Uncertainty Quantification
• Computational Mathematics
• Medical Image Processing
• +۴ مورد دیگر

Dr. Daniele Schiavazzi is an Associate Professor in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, with a concurrent appointment as Associate Professor in Aerospace and Mechanical Engineering (AME). He holds a Ph.D. and B.Eng. from the University of Padova, Italy (Summa cum Laude), with postdoctoral training at Stanford University and University of California San Diego. His research focuses on uncertainty quantification, computational hemodynamics, and machine learning , with applications in cardiovascular modeling. Key areas include stochastic analysis, numerical modeling, finite element methods, and Bayesian inference for clinical decision-making under uncertainty. He has pioneered frameworks like InVAErt networks and NeurAM for model emulation and uncertainty reduction. Dr. Schiavazzi leads the Multifidelity Uncertainty Quantification (MUQ) Group , developing open-source tools such as SimVascular for image-based cardiovascular modeling. His work integrates physics-informed AI, multi-resolution expansions, and dimensionality reduction to address complex biomedical problems, including coronary artery disease and pulmonary hypertension. Notable contributions include NSF CAREER Award-funded research on Bayesian inference networks and collaborative projects on cardiovascular model ensembles. His lab actively explores privacy-preserving data synthesis and differential privacy for electronic health records.

Benjamin Peherstorferمشاهده پروفایل

دانشیار

• Computational Mathematics
• Machine Learning
• Computational Statistics
• +۵ مورد دیگر

Benjamin Peherstorfer is an Associate Professor at the Courant Institute of Mathematical Sciences, New York University. His research focuses on computational mathematics, machine learning, and scientific computing, with an emphasis on nonlinear model reduction, uncertainty quantification, and multifidelity methods. He holds editorial roles at journals such as Journal of Scientific Computing and SIAM Journal on Scientific Computing . Peherstorfer co-organizes workshops on topics like digital twins and fusion energy, reflecting his commitment to advancing interdisciplinary computational science. He has advised students like Jules Berman, who received the Harold Grad Memorial Prize. His work integrates machine learning with traditional numerical methods to address challenges in high-dimensional systems and real-time simulations. Notable contributions include developing Neural Galerkin schemes for solving evolution equations and advancing multifidelity Monte Carlo methods to accelerate uncertainty propagation. His research bridges theoretical foundations with practical applications, such as wildfire-atmosphere coupling and plasma confinement in stellarators. Key Roles: Secretary of the SIAM Activity Group on Computational Science and Engineering Co-organizer of the Long Program on Multi-Fidelity Methods for Fusion Energy (IPAM, 2026) Main Research Themes: Reduced-order modeling Data-driven operator inference Adaptive sampling algorithms

Murali Krishna Emaniمشاهده پروفایل

پژوهشگر

• High Performance Computing
• Scalable Machine Learning
• Parallel programming models
• +۳ مورد دیگر

Murali Krishna Emani is an Assistant Computer Scientist in the Data Science group at Argonne Leadership Computing Facility (ALCF) within Argonne National Laboratory. Previously, he served as a Postdoctoral Research Staff Member at Lawrence Livermore National Laboratory. His research spans High Performance Computing , Scalable Machine Learning , and Emerging HPC architectures . Key interests include parallel programming models, runtime systems, and online adaptation for scientific applications. At ALCF, he co-leads the AI Testbed initiative exploring AI accelerator performance for scientific machine learning, and chaired the MLPerf HPC group at MLCommons for benchmarking large-scale ML on HPC systems. His recent publications (2023-2025) reveal strong focus on LLM optimization (MoE inference, KV cache management), AI accelerator benchmarking , and scientific applications (climate modeling, protein design). The work demonstrates cross-cutting themes in hardware-software co-design and performance modeling for emerging architectures. ACM Gordon Bell Prize finalist for climate modeling (2025) Winner of ACM Gordon Bell Special Prize for HPC-based Covid-19 research (2022) Emani actively mentors PhD students and postdocs, with advisees now faculty at Binghamton University, California State University, and researchers at NVIDIA, Microsoft, and national labs. His service includes program committees for SC, IPDPS, and AAAI conferences. Current projects focus on performance modeling for ML/DL frameworks on supercomputers, co-design of hardware architectures for ML algorithms, and benchmarking ML/DL frameworks on HPC systems.

Bethany Luschمشاهده پروفایل

استادیار

• Scientific Machine Learning
• Dynamical Systems
• Supercomputing
• +۷ مورد دیگر

Bethany Lusch is an Assistant Computer Scientist in the Data Science Group at the Argonne Leadership Computing Facility, Argonne National Laboratory. She holds advanced degrees in applied mathematics and focuses on integrating artificial intelligence with scientific computing, particularly for dynamical systems and PDE-based simulations. Education: PhD in Applied Mathematics, University of Washington, 2016 MS in Applied Mathematics, University of Washington, 2011 BS in Mathematics (Honors), University of Notre Dame, 2010 Her research centers on scientific machine learning, with emphasis on developing machine-learning emulators for expensive simulations such as climate models and computational fluid dynamics. She pioneers methods that embed domain knowledge into deep learning frameworks, particularly through Koopman operator theory to linearize nonlinear dynamics. Her work spans representation learning, reduced-order modeling, and analysis of supercomputing system logs. The 15 most recent publications reflect a strong trend toward using deep learning—especially recurrent and autoencoder architectures—to model complex physical systems. Key themes include surrogate modeling, latent-space dynamics, closure in reduced-order models, and interpretable AI in scientific contexts. Her work bridges theoretical rigor with practical applications in engineering and geophysics. Scientific Awards: NPSC Fellow, National Physical Science Consortium (now GFSD), Aug 2010 – May 2016 Six-Year Graduate Fellowship, NSF-SUMR Scholarship University of Notre Dame Mathematics Department Scholarship, Aug 2007 – Jun 2010 Bethany Lusch has collaborated extensively with researchers at Argonne and the University of Washington. Her projects often involve interdisciplinary teams working on high-performance computing applications. She has contributed to the development of tools like MELA for visual analytics of HPC logs and has secured competitive fellowships and funding. Her prior role as a Research Associate at the University of Washington (2016–2018) preceded her current position at Argonne (2018–present), indicating a continuous research trajectory. She is actively involved in advancing AI-driven scientific discovery, with ongoing work in universal embeddings for PDEs and multifidelity modeling. Her lab affiliations include the Argonne Leadership Computing Facility, where she leverages world-class supercomputing resources to develop and test her models.

Andrew Glawsمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Surrogate Modeling
• +۷ مورد دیگر

Andrew Glaws is a Researcher in Applied Mathematics at the National Renewable Energy Laboratory (NREL), working within the Computational Science Center. He joined NREL as a postdoctoral researcher in January 2019 and transitioned to a Researcher position in 2021, focusing on physics-informed deep learning for energy systems. His educational background includes: PhD in Computer Science, University of Colorado Boulder Master of Science in Mathematics, Virginia Polytechnic Institute and State University Bachelor of Science in Mathematics, Vanderbilt University Bachelor of Science in Physics, Vanderbilt University Glaws' research centers on applying machine learning and data-driven methods to renewable energy challenges. His core interests include: Machine Learning and Deep Learning Surrogate Modeling Uncertainty Quantification and Sensitivity Analysis Dimension Reduction Multifidelity Methods Exploratory Data/Model Analysis He has extensive cross-domain collaborations in wind/solar energy, climate science, building energy analysis, bioenergy, and battery technology. Analysis of his 45 publications (2020-2025) reveals a pronounced shift toward generative AI applications in energy systems since 2022, with 2025 outputs emphasizing aerodynamic design, photovoltaic reliability, and offshore wind platform modeling using advanced neural architectures. No scientific awards are documented in available sources. His advisory activities and grant funding details are not specified in the provided materials, though his 28 Mendeley readers and active conference participation indicate significant academic engagement. Glaws operates within NREL's Computational Science Center, contributing to interdisciplinary teams tackling energy challenges through computational mathematics and AI-driven approaches.

Christine Schwarzمشاهده پروفایل

مدرس

• Topology optimization of structures
• Multifidelity shape optimization
• Multiphysics robustness analysis
• +۲ مورد دیگر

Christine Schwarz is a Lecturer at the Chair of Land Management and Rural Development at the Technical University of Munich (TUM). Her research focuses include topology optimization for nonlinear collision processes, multifidelity shape optimization, multiphysics robustness analysis, finite element modeling for injury minimization, and multi-level material modeling for fiber composites. Department: Chair of Land Management and Rural Development University: Technical University of Munich Email: christine.schwarz@tum.de

• Machine Learning
• Surrogate Modeling
• Data Augmentation
• +۵ مورد دیگر

Tobias Lehrer is a Doctoral Candidate at the Chair of Computational Solid Mechanics at the Technical University of Munich since 2023. His research focuses on integrating machine learning with computational mechanics for manufacturing optimization. 2023 - Present: Doctoral Candidate, Chair of Computational Solid Mechanics, TU Munich 2020 - 2023: Doctoral Candidate, Laboratory of Finite-Element-Method, OTH Regensburg 2018 - 2020: M.Sc. in Mechanical Engineering, OTH Regensburg His research interests span machine learning, surrogate modeling, data augmentation, synthetic data, generative AI, sheet metal forming, and sensitivity analysis. His publications emphasize deep drawing processes , parametric CAD modeling , and surrogate-based optimization for manufacturability assessment. Key projects include AMEDEO and eEgO, aiming to enhance early-stage prediction of sheet metal part feasibility through simulation-driven machine learning frameworks. In teaching, Tobias has led courses on structural optimization , computational mechanics for car body design , and structural mechanics modeling . His work bridges computational methods with industrial applications, particularly in automotive manufacturing.

• Computational Mechanics
• Structural Optimization
• Crash Simulation
• +۴ مورد دیگر

Tanja Schnappinger is a PhD student at the Department of Computational Solid Mechanics, Technical University of Munich (TUM), affiliated with the Professorship for Computational Mechanics. Her research focuses on multi-fidelity methods, uncertainty quantification, and advanced modeling in computational mechanics. Education: PhD in Computational Solid Mechanics (2024–Now), Technical University of Munich M.Sc. in Computational Mechanics (2021–2024), Technical University of Munich, with participation in the Honors Program of the Bavarian Graduate School of Computational Engineering (BGCE) B.Sc. in Engineering (2017–2021), Technical University of Munich Semester abroad at National University of Singapore (2020) and Aalto University, Finland (2022) Research Interests: Tanja works on topology optimization of structures for nonlinear collision processes, multifidelity shape optimization, robustness analysis for crash and multiphysics applications, finite element modeling to minimize injuries, and multi-level material modeling for fiber composites. Her projects include AI-based model reduction and uncertainty quantification for dynamic rotor blade design. Scientific Awards: Honors Program of the Bavarian Graduate School of Computational Engineering (BGCE) Contact: Tanja is based at the Professorship for Computational Mechanics, Theresienstr. 90 (2nd floor), 80333 Munich. She can be reached at tanja.schnappinger@tum.de or +49 (89) 289 - 28691, Room 0106.02.634.

Hemant Kumar Singhمشاهده پروفایل

دانشیار

• Evolutionary Computation
• Engineering Design Optimization
• Multi-objective Optimization
• +۴ مورد دیگر

Hemant Kumar Singh is an Associate Professor at the School of Engineering and Information Technology at the University of New South Wales (UNSW) in Canberra, Australia. He is based at the Australian Defence Force Academy campus and maintains an active research program in evolutionary computation methods for engineering design optimization. Institution: University of New South Wales (UNSW) School: School of Engineering and Information Technology Location: UNSW Canberra, Australian Defence Force Academy Email: h.singh@unsw.edu.au Education: PhD, University of New South Wales, Australia, 2011 B.Tech (Mechanical Engineering), Indian Institute of Technology, Kanpur, India, 2007 Research Interests: Dr. Singh's research focuses on developing efficient evolutionary computation methods for design optimization problems. His primary areas include multi-/many-objective optimization and decision-making, constraint handling in evolutionary algorithms, bilevel optimization, computationally expensive/surrogate-assisted optimization, and multi-concept optimization. His work bridges theoretical advances in optimization with practical engineering applications, particularly in design problems where computational resources are limited. Publication Trends: Dr. Singh's recent publications (2023-2025) demonstrate a strong focus on advancing multi-objective and bilevel optimization techniques, with particular attention to computationally expensive problems. His work integrates evolutionary algorithms with surrogate modeling, multifidelity approaches, and novel solution representation methods. A significant portion of his recent research addresses the challenge of multi-concept optimization, where design solutions involve fundamentally different conceptual approaches that need to be compared and optimized simultaneously. Awards and Recognition: Outstanding Reviewer Award, ACM Genetic and Evolutionary Computation Conference (GECCO) 2024 Best Paper Award, IEEE Congress on Evolutionary Computation (CEC) 2023 Best Paper Nomination, Parallel Problem Solving from Nature (PPSN), 2022 Winner, Competition on Online Data-driven Multi-objective Optimization, IEEE CEC 2019 Australia Bicentennial Fellowship 2016 Endeavour Australia Fellowship 2018 Research Supervision and Grants: Dr. Singh has successfully supervised numerous PhD and Masters students to completion, with many now holding prominent positions in academia and industry. He has secured significant research funding including two ARC Discovery Project Grants (2019-23, 2022-25), an Endeavour Australia Fellowship (2018), and an Australia Bicentennial Fellowship (2016). His research has been supported by collaborations with industry partners and international institutions across Australia, Germany, China, and the United States. Teaching: Dr. Singh teaches undergraduate courses including ZEIT 3500 Engineering Structures, ZEIT 3700 Mechanical Design 1, and ZEIT 4700 Mechanical Design 2 at UNSW Canberra.

Francesco Montomoliمشاهده پروفایل

استاد

• Machine Learning
• Uncertainty Quantification
• Computational Fluid Dynamics (CFD)
• +۸ مورد دیگر

Prof. Francesco Montomoli is an Honorary Professor at Imperial College London's Department of Aeronautics, within the Faculty of Engineering. Previously, he held roles as a Professor (2014–2025), Reader (2018–2021), Senior Lecturer (2012–2018), and Research Professor (2011–2014). He earned a MSc and PhD from the University of Florence, with a visiting scholar stint at Texas A&M University's Turbine Heat Transfer Laboratory. His research focuses on Machine Learning, Uncertainty Quantification (UQ), Additive Manufacturing, and Computational Fluid Dynamics (CFD) applied to aerospace propulsion and energy systems. He co-founded two Imperial spinouts, including Toffeex ltd, and leads the UQLab, which develops robust design methodologies for AM and advanced UQ techniques. Notable awards include the RAEng Enterprise Fellowship and Amelia Earhart Fellowship. His work spans gas turbines, heat transfer optimization, and rare event modeling (e.g., deflagration effects). Collaborations include Rolls-Royce, Airbus, and General Electric. Recent articles highlight advancements in physics-informed neural networks, multi-fidelity UQ, and fluid-structure interaction under extreme conditions. He advises multiple PhD students and maintains academic ties with institutions like the University of Padova and Politecnico di Torino.

Bruno Sudretمشاهده پروفایل

استاد

• Uncertainty Quantification
• Reliability Analysis
• Sensitivity Analysis
• +۳ مورد دیگر

Prof. Bruno Sudret is a Full Professor of Risk, Safety, and Uncertainty Quantification at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering. His research focuses on computational methods for uncertainty quantification, reliability analysis, and Bayesian approaches for model calibration. He has contributed to the development of the UQLab software framework and the UQWorld community platform. Before joining ETH Zurich in 2012, Sudret held roles at the University of Berkeley (postdoc), EDF R&D (researcher and group head), and Phimeca Engineering (Director of Research and Strategy). Education: Master's in Science from Ecole Polytechnique (1993), Master's and PhD in Civil Engineering from Ecole Nationale des Ponts et Chaussées (1996, 1999). His work emphasizes stochastic simulators, surrogate modeling, and reliability-based design optimization. He serves on editorial boards for journals like Reliability Engineering and Systems Safety and Structural Safety .

Goh Siang Huatمشاهده پروفایل

دانشیار

• Geotechnical Engineering
• Earthquake Engineering
• Soil-Pile Interaction
• +۵ مورد دیگر

Dr. Goh Siang Huat is an Associate Professor in the Department of Civil Engineering at the National University of Singapore. He holds a B.Eng. (First Class Honours) and M.Eng. from NUS (1991, 1995) and a Ph.D. from Cornell University (2001). His research focuses on geotechnical centrifuge modeling, seismic soil-pile interaction, and large deformation numerical analysis. He has contributed to the recovery efforts of the World Trade Centre post-9/11 and led roles in the Centre for Protective Technology and Underground Singapore Conference. Education: B.Eng. (First Class Honours), National University of Singapore (1991) M.Eng. (Civil), National University of Singapore (1995) Ph.D., Cornell University (2001) Research Interests: Geotechnical Centrifuge Modeling of Dynamic Events Numerical Analysis of Seismic Soil-Pile Interaction Large-Scale Parallel Computations Offshore Foundation Design (Spudcans, Lattice Legs) Recent Trends in Publications: His recent work emphasizes computational methods for seismic response analysis, geotechnical reliability under spatial variability, and machine learning applications in subsurface modeling. Key themes include spudcan foundations, slope stability, and gas reservoir dynamics. Awards: Silver Award, Singapore Accreditation Council Assessor (2012) Bronze Award, Singapore Accreditation Council Assessor (2009) Teaching & Service: He teaches Geotechnical Finite Element Methods and has served as Associate Editor for Journal of Earthquake and Tsunami . His roles include Operations Manager at the Centre for Protective Technology and organizer of APSSRA2012 and Underground Singapore Conference. Labs/Teams: Active in the Centre for Protective Technology within the Faculty of Engineering, focusing on geotechnical innovation and disaster resilience.

• Renewable Energy
• Ocean Engineering
• Robotics
• +۳ مورد دیگر

Eirini Katsidoniotaki is a Postdoctoral Associate (Research Fellow) in the Department of Mechanical Engineering at the Massachusetts Institute of Technology, School of Engineering. Her research addresses critical challenges in offshore renewable energy and aquaculture technology through advanced computational and robotic systems. Her primary research areas include wave energy conversion, structural health monitoring of marine structures, and aquaculture robotics. She specializes in developing real-time monitoring solutions using digital twins, multi-fidelity modeling, and machine learning techniques. Her work integrates computational fluid dynamics with experimental validation to analyze extreme loads and improve system resilience in ocean environments. Recent projects focus on adaptive path planning for net pen inspection and surrogate modeling for wave energy farms. Analysis of her publication record reveals a strong emphasis on practical applications of computational methods to enhance safety and efficiency of offshore energy systems. Her interdisciplinary approach bridges ocean engineering, robotics, and data science to tackle complex problems in marine renewable energy, with consistent focus on real-time monitoring, structural dynamics, and extreme condition resilience.