جست‌وجوی استاد و پژوهشگر

• Computational Fluid Dynamics
• Gas Turbines
• Turbomachinery
• +۵ مورد دیگر

Dr. Luca Di Mare is an Associate Professor of Engineering Science at the University of Oxford and a Fellow of St John's College. He leads research at the Oxford Thermofluids Institute, focusing on computational modeling of gas turbines. Previously, he headed the Whole Engine Modelling Group at Imperial College London's Vibration University Technology Centre, sponsored by Rolls-Royce. Research Focus: His group develops multi-fidelity models of gas turbine systems combining expertise in computational geometry , CFD , grid generation , and structural analysis . The Virtual Engine software developed under his leadership is being integrated into Rolls-Royce's engineering systems. He maintains additional research activities in turbulence modeling for compressors as a Visiting Academic at the Whittle Laboratory. Publication Trends: Recent works demonstrate strong focus on: Advanced turbomachinery modeling including aeroelastic phenomena and compressor design Hypersonic flow physics with emphasis on non-equilibrium thermochemistry Innovative shock tube methodologies for high-enthalpy flows Computational algorithm development for unsteady conjugate heat transfer Laboratories & Collaborations: Leads the Virtual Engine development team and collaborates extensively with the Whittle Laboratory on compressor research. Maintains significant industry partnership with Rolls-Royce plc through technology transfer initiatives.

Changqing Chengمشاهده پروفایل

دانشیار

• Complex Systems
• Nonlinear Dynamics
• Data Analytics
• +۶ مورد دیگر

Changqing Cheng is an Associate Professor in the Department of Systems Science and Industrial Engineering at the State University of New York at Binghamton, where he also serves as a Core Faculty Member at the Binghamton Center of Complex Systems (CoCo). His primary research focuses on sensing and data-driven modeling, simulation and analytics for process monitoring, quality control and performance optimization of complex systems, with special interests in nonlinear dynamics, chaotic patterns, and recurrence behaviors. He applies these methodologies across manufacturing (optimal design, uncertainty quantification) and healthcare (data fusion, time series analysis, epidemic modeling). PhD, Industrial Engineering and Management, Oklahoma State University BS & MS, Engineering Mechanics, Dalian University of Technology, China Professor Cheng's research integrates advanced computational techniques with practical applications across multiple domains. His work on nonlinear dynamics and complex systems has led to innovative approaches for process monitoring and anomaly detection in manufacturing, as well as novel methods for healthcare analytics and epidemic modeling. His publications demonstrate a consistent pattern of high-impact research in top journals such as IEEE Transactions, Chaos, and Journal of Computational Physics, with a strong emphasis on methodological innovation and practical application. His research group has produced significant work in uncertainty quantification, deep learning applications in biomedical engineering, and network science applications to epidemic dynamics. The publications reveal a strong interdisciplinary approach that bridges engineering, computer science, and healthcare applications. Professor Cheng's work on basin stability estimation for complex systems and decomposition-enhanced deep learning for cardiac fibrillation detection represents cutting-edge contributions to their respective fields. INFORMS Data Mining and Decision Analytics Best Paper Award (2017) OSU Outstanding Doctoral Student Award OSU Distinguished Graduate Fellowship Material Handling Education Foundation Scholarship CIEADH Best Poster Award, IIE Research Conference Doctoral Colloquium Professor Cheng actively mentors PhD and Master's students, with several receiving prestigious awards including BU Distinguished Dissertation Award (2024) and Excellence in Systems Science Research Award. He serves as Associate Editor for IISE Transactions on Healthcare Systems Engineering and has received significant research funding supporting his students' work. His leadership extends to the Binghamton University Data Science Transdisciplinary Area of Excellence and the Northeast Regional Conference on Complex Systems where he has served as Program Chair for 2025. His lab maintains an active GitHub presence with code repositories for many of his publications.

Rafael Ferreira da Silvaمشاهده پروفایل

استادیار

• Scientific Workflows
• Parallel and Distributed Computing
• High Performance Computing
• +۷ مورد دیگر

Rafael Ferreira da Silva is a Research Assistant Professor in the Department of Computer Science at University of Southern California and a Senior Research Scientist at Oak Ridge National Laboratory. He serves as Group Leader for the Workflow and Ecosystem Services group at ORNL's National Center for Computational Sciences and is the Founder and Executive Director of the Workflows Community Initiative. Additionally, he is the Special Content Editor for the Future Generation Computer Systems journal and holds Senior Member status with both IEEE and ACM. Dr. Ferreira da Silva specializes in modeling and simulation of parallel and distributed computing systems, with expertise spanning scientific workflows, hybrid quantum classical systems, and autonomous science. His technical proficiency includes sophisticated scheduling algorithms, high fidelity modeling and simulation, multi-objective optimization, fault tolerant system design, and energy efficient computing across cloud, edge, and HPC environments. His research focuses on creating resilient digital infrastructures that dynamically adapt to changing research demands, accelerating scientific discovery through robust computational foundations. His recent publications demonstrate significant contributions to exascale workflow applications, HPC-quantum convergence, agentic workflow control mechanisms, and terminology standardization for scientific workflow systems. His work bridges multiple disciplines including high-performance computing, artificial intelligence, quantum computing, and autonomous laboratory systems, reflecting a broad research impact across computational science. Professional Recognition: Senior Member of IEEE Senior Member of ACM Special Content Editor for Future Generation Computer Systems journal With 149 scientific publications, 22 chair roles in conferences, 69 PC member roles in conferences, 18 research grants, and involvement in 270+ research projects, Dr. Ferreira da Silva maintains an extensive research portfolio and leadership position in the computational science community. His Workflows Community Initiative has fostered a thriving network of 51 international workflow users, developers, and researchers. As Group Leader at ORNL, he directs research efforts focused on advancing workflow technologies and their applications across scientific domains, with particular emphasis on creating interoperable systems that can operate across multiple computing facilities and environments.

Kadir Sarıözمشاهده پروفایل

استاد

• Naval Architecture
• Ocean Engineering
• Seakeeping Performance
• +۳ مورد دیگر

Professor Kadir Sarıöz is a full-time faculty member at Istanbul Technical University in the Department of Shipbuilding and Ocean Engineering . His career spans over three decades of research and teaching in naval architecture and ocean engineering, focusing on seakeeping, ship maneuvering, hull-form optimization, and hydrodynamic simulation. Research Interests Professor Sarıöz’s work revolves around the hydrodynamic performance of marine vehicles. His investigations include: Seakeeping behavior of high-speed and conventional displacement hulls Experimental and computational ship maneuvering in calm water and waves Optimization algorithms for hull-form and propeller design under mission-specific constraints Dynamic positioning capability analysis under realistic environmental loads Simulation-based routing of large tankers through restricted waterways such as the Istanbul Strait Recent Projects 2021 – 2023: “Refakat Römorkörü Performansının Çok Serbestlik Dereceli Deneysel ve Hesaplamalı Analizi” (Revolving Fund, Principal Investigator) 2020: “Shell Antalya Terminali B şamandıra sistemindeki boru hattının tadilatı incelemesi” (Revolving Fund, Principal Investigator) Publication Trends Between 1990 and 2025, Professor Sarıöz has authored or co-authored more than 30 peer-reviewed journal and conference papers. Early works concentrated on practical seakeeping criteria and automated hull fairing, while recent contributions emphasize system-based maneuvering prediction, multi-objective optimization of naval combatant hulls, and environmentally driven dynamic positioning analyses. A clear progression from empirical and semi-empirical methods to high-fidelity CFD and experimental validation is evident. Laboratory & Facilities His research is supported by the university’s towing tank and planar motion mechanism facilities, equipped with modern motion controllers and servo-motor systems enabling precise captive maneuvering tests.

Triantafyllou Savvasمشاهده پروفایل

استادیار

• Computational Mechanics
• Structural Engineering
• Fracture Mechanics
• +۵ مورد دیگر

Triantafyllou Savvas serves as an Assistant Professor in the Department of Structural Engineering at the School of Civil Engineering, National Technical University of Athens (NTUA), with his faculty appointment documented since October 2020. His academic work is centered within one of Greece's premier engineering institutions, contributing to both advanced structural analysis education and cutting-edge computational research. Professor Savvas's research program focuses on computational structural mechanics with emphasis on developing advanced numerical methods for complex engineering challenges. His expertise spans material point methods for contact and fracture problems, wave-structure interaction phenomena (particularly for dams and flexible structures), vibroacoustics in porous media, and the mechanical behavior of textile reinforced mortar (TRM) systems for masonry strengthening. His work bridges theoretical computational advances with practical applications in infrastructure resilience against extreme events like tsunamis and earthquakes. Analysis of his recent publications (2021-2025) reveals a cohesive research trajectory centered on computational innovation: he has pioneered high-fidelity material point formulations for contact mechanics, developed multiscale virtual element methods for poroelastic media, established empirical models for TRM-masonry interfaces, and explored machine learning integration in topology optimization. His work consistently addresses critical gaps in simulating dynamic structural failures and wave impacts. Scientific Awards: No scientific awards were documented in the provided source materials. Advising and Grants: While the source materials do not specify current students or grant awards, Professor Savvas's extensive publication record across high-impact journals indicates active graduate research supervision and likely involvement in competitive research funding. His collaborative work with institutions like NTUA's Department of Structural Engineering suggests participation in national and international research consortia focused on computational mechanics and structural safety.

Lauren Clackمشاهده پروفایل

استادیار

• Implementation Science
• Health Care
• Infection Prevention
• +۳ مورد دیگر

Lauren Clack is an Assistant Professor at the Institute for Implementation Science in Health Care , Faculty of Medicine, University of Zurich. She holds a PhD in Psychology (University of Zurich) and an MSc in Applied Ergonomics (University of Nottingham). Education : PhD in Psychology, MSc in Applied Ergonomics. Research Focus : Implementation Science methodology, human-centered design, infection prevention, patient safety, and behavioral science in healthcare. Her work spans neonatal care, antimicrobial stewardship, and surgical safety. Scientific Contributions include systematic reviews on organizational readiness, fidelity frameworks, and tailored implementation strategies. Key awards: Best Paper Award (Swiss Society for Hospital Hygiene) and Pfizer Research Prize 2024 for her Lancet Infectious Diseases study. Leadership in multinational clinical trials (e.g., PROHIBIT study) and collaborations with institutions like Geneva University Hospital and Swissnoso. Her work integrates virtual reality training, pathogen transmission analysis, and patient-clinician co-design projects.

• Computational Mechanics
• Fluid-Structure Interaction (FSI)
• Isogeometric Analysis
• +۳ مورد دیگر

Takuya Terahara is an Assistant Professor at the Green Computing Systems Research Organization, affiliated with the School of Creative Science and Engineering. His research focuses on computational mechanics, fluid-structure interaction (FSI), and isogeometric analysis with applications in biomedical engineering (heart valves, aorta flow), aerospace systems (parachutes), and automotive aerodynamics (tire flows). His work combines T-splines and space-time methods to address challenges in contact mechanics, boundary layer representation, and multi-scale flow dynamics. Notable projects include modeling red blood cell deformation, heart valve FSI with leaflet contact, and spacecraft parachute structural analysis. Research trends highlight his expertise in integrating isogeometric discretization with lattice Boltzmann methods, boundary element analysis, and slip interface techniques. His computational frameworks enable high-fidelity simulations of complex systems requiring continuity, smoothness, and contact representation. Terahara has received research grants from the Japan Society for the Promotion of Science (JSPS), including projects on local mesh refinement for flow analysis (2020-2022) and fluid-structure interaction for heart valves (2017-2020).

Robert McDermottمشاهده پروفایل

استاد

• Superconducting Qubits
• Quantum Coherence
• Quantum Measurement
• +۷ مورد دیگر

Robert McDermott is the Roeske Professor of Physics at the University of Wisconsin–Madison, where he leads a leading research group in experimental quantum computing. He is affiliated with the Department of Physics, the Condensed Matter group, and the Wisconsin Quantum Institute, focusing on superconducting qubits and hybrid quantum systems. His research interests include quantum coherence, scalable coherent control, quantum measurement, and hybrid quantum systems. He investigates the origins of noise in superconducting devices and develops novel control and readout techniques using Single Flux Quantum (SFQ) digital logic and microwave photon counters. His lab operates multiple low-temperature platforms for qubit characterization and device testing. His recent publications reflect a strong focus on improving qubit coherence, reducing quasiparticle poisoning, implementing scalable control architectures, and developing hybrid interfaces between superconducting circuits and atomic systems. His work spans quantum error correction, noise characterization, and quantum measurement technologies. Origin and Reduction of 1/f Magnetic Flux Noise in Superconducting Devices (2016) Quantum-classical Interface Based on Single Flux Quantum Digital Logic (2018) Microwave-to-optical frequency conversion using a cesium atom coupled to a superconducting resonator (2017) High-Fidelity Measurement of a Superconducting Qubit Using an On-Chip Microwave Photon Counter (2021) McDermott advises numerous PhD students and has mentored many postdoctoral researchers who have gone on to prominent roles in academia and industry, including at IBM, Google, Rigetti, and Northrop Grumman. His lab collaborates extensively with researchers at UC Berkeley, Caltech, and the University of California, Santa Barbara. He has secured significant research funding to support his experimental facilities, including dilution refrigerators and advanced fabrication capabilities at the Wisconsin Center for Applied Microelectronics (WCAM). The McDermott Lab maintains state-of-the-art facilities for superconducting device fabrication and low-temperature measurement, with five cryogenic platforms and access to advanced lithography and deposition tools. The group is actively working on integrating classical control electronics at millikelvin temperatures and building hybrid quantum systems for future quantum networks.

Matt Sinclairمشاهده پروفایل

استادیار

• Computer Architecture
• Heterogeneous Computing
• GPU Architecture
• +۷ مورد دیگر

Matt Sinclair is an Assistant Professor in the Computer Sciences Department at the University of Wisconsin–Madison, with affiliate appointments in the Electrical and Computer Engineering Department and the Teaching Academy. He leads the Heterogeneous Architectures Lab (HAL) and is actively involved in the gem5 Project Management Committee, focusing on modern heterogeneous computing systems. His research interests include computer architecture, GPU design, parallel programming, operating systems, and high-performance computing. He develops tools and methodologies for programming and optimizing future heterogeneous systems, particularly focusing on GPUs. His work spans architectural design, efficient software implementation, and simulation frameworks. Recent publications highlight advancements in gem5 simulation accuracy, GPU energy modeling, synchronization mechanisms, and scheduling policies for ML workloads. His research has led to improvements in GPU memory bandwidth modeling, extensible power modeling, and full-system heterogeneous simulation capabilities. Scientific awards include the NSF CAREER Award, the David J. Kuck Outstanding PhD Thesis Award, and a Qualcomm Innovation Fellowship. He has also received honors such as the ACM SIGARCH–IEEE TCCA Outstanding Dissertation Award Honorable Mention and multiple fellowships. He mentors a diverse group of graduate and undergraduate students, many of whom have published at top venues and moved on to roles at NVIDIA, AMD, Microsoft, and leading graduate programs. He is involved in teaching and curriculum development, particularly through the Excel Initiative and as a former Madison Teaching & Learning Fellow. He also stewards the ISCA Hall of Fame, contributing to the academic community beyond research.

• Acute Care Nursing
• Critical Care Nursing
• Nursing Education
• +۵ مورد دیگر

Maria Bliss is a Lecturer in Adult Nursing at City St George's, University of London, specializing in acute and critical care. She is affiliated with the School of Health Sciences and the Department of Adult Nursing, where she contributes significantly to both pre-registration and post-registration nursing education. Her work integrates clinical expertise with innovative teaching methods, particularly simulation-based learning. Research Interests: Maria's research focuses on enhancing nurses' ability to recognize and respond to patient deterioration. She has a strong interest in simulation as an educational strategy, exploring its impact on clinical decision-making, knowledge transfer, and patient outcomes. Her work bridges clinical practice and academic teaching, emphasizing the importance of experiential learning in high-fidelity environments. Publication Trends: Her most recent publication (2018) highlights the perceived benefits of simulation among registered nurses, identifying themes such as improved assessment skills, knowledge application, and confidence in decision-making. The research underscores the value of simulation in post-registration professional development and its positive implications for patient care. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: Maria serves as Senior Personal Tutor and Associate Programme Director for the BSc (top-up degree) in nursing. While specific grant funding is not detailed, her leadership in curriculum development and simulation-based education suggests active involvement in educational innovation and program leadership. She mentors nursing students and supports their academic and professional growth. Labs, Teams, and Clinical Engagement: Maria maintains active clinical engagement through practice in intensive care units and surgical wards at London hospitals, including Homerton University Hospital and The London Independent Hospital. This ongoing clinical activity ensures her teaching remains grounded in current practice. She is involved in developing educational technologies and simulation modules that enhance student learning across acute care settings.

• Deep Learning
• Medical Imaging
• Simulation-based Training
• +۳ مورد دیگر

Bas Oei serves as a University Researcher in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e), actively contributing to the Signal Processing Systems research group and the Eindhoven MedTech Innovation Center. His interdisciplinary work bridges engineering and clinical applications through advanced computational methodologies. His primary research domains include Deep Learning for medical image analysis , simulation-based healthcare training systems , and evidence-based instructional design . Oei develops AI-driven diagnostic tools for point-of-care ultrasound while creating validated assessment frameworks for medical team training, with particular emphasis on translating technical innovations into clinical education practice. Analysis of his publication trajectory reveals a strategic evolution from surgical skill acquisition studies (2008) toward high-impact deep learning applications in pandemic response (2020). His work consistently demonstrates cross-disciplinary integration of computer science with medical education, evidenced by the 498 citations for his COVID-19 ultrasound research and significant industry partnerships reflected in patent citations. Within TU/e's research ecosystem, Oei operates at the intersection of the Signal Processing Systems group's technical expertise and the Eindhoven MedTech Innovation Center's translational mission, collaborating with clinicians and engineers to develop deployable healthcare solutions that address real-world diagnostic and training challenges.

Simon Demangeمشاهده پروفایل

پژوهشگر

• Fluid Dynamics
• Aeroacoustics
• Turbulent Boundary Layers
• +۵ مورد دیگر

Simon Demange is a post-doctoral researcher at the Technical University of Berlin, working within the Institute for Fluid Mechanics and Technical Acoustics (ISTA) in the Faculty of Traffic and Machine Systems. His research focuses on linear stability and resolvent analyses of turbulent boundary layers developing over airfoils, with particular emphasis on developing low-order physics-based models describing noise generation mechanisms from coherent structures in turbulent flows. Dr. Demange completed his research master and PhD thesis within the aerospace and aeronautics department of the von Karman Institute for Fluid Dynamics in 2021. His doctoral research focused on the onset of absolute and global instabilities in high-enthalpy round jets, establishing his expertise in flow instability analysis. Dr. Demange's research interests span several critical areas of fluid dynamics with practical engineering applications: Turbulent boundary layer dynamics over airfoils and wings Resolvent analysis for modeling coherent structures in turbulence Trailing edge noise mechanisms in wind turbines and aircraft Physics-based low-order modeling of flow phenomena Plasma jet instabilities and high-enthalpy flow dynamics Multi-physics flow modeling and simulation Analysis of Dr. Demange's publication record reveals a strong trajectory of increasingly sophisticated work bridging fundamental fluid dynamics with practical engineering applications. His research demonstrates particular expertise in applying stability analysis techniques to understand and model noise generation in aerodynamic systems, with significant implications for wind turbine design and aircraft noise reduction. His collaborative work spans multiple institutions and shows strong interdisciplinary connections between theoretical fluid dynamics, computational methods, and experimental validation. Dr. Demange has developed specialized computational tools including FELiCS, a versatile linearized solver for multi-physics flows, demonstrating his capability in both theoretical development and practical implementation. His work on wind turbine noise addresses a critical challenge in renewable energy technology, while his research on high-speed train aerodynamics contributes to transportation engineering advancements.

• Aeroelasticity
• Finite element analysis
• Higher-order theories
• +۵ مورد دیگر

Matteo Filippi is a Fixed-term tenure-track Assistant Professor in the Department of Mechanical and Aerospace Engineering (DIMEAS) at the Polytechnic University of Turin. He is a member of the College of Mechanical, Aerospace and Automotive Engineering and serves on doctoral colleges for both Aerospace Engineering and Mechanical Engineering programs. His academic work focuses on advanced structural analysis techniques with applications in aerospace engineering. Dr. Filippi's research interests include Aeroelasticity, Finite element analysis, Higher-order theories, Nonlinear dynamics, and Rotor dynamics. His work spans computational mechanics, structural dynamics, and advanced materials, with particular emphasis on high-fidelity modeling approaches for complex engineering problems. He applies these methods to analyze structures ranging from traditional aerospace components to innovative metamaterials and composite structures. His recent publications demonstrate a strong focus on high-fidelity beam modeling, structural dynamics, and wave propagation analysis. The research shows consistent application of the Carrera Unified Formulation (CUF) for developing advanced finite element models that can accurately capture complex structural behaviors while maintaining computational efficiency. His work bridges theoretical developments with practical aerospace applications. Dr. Filippi actively supervises multiple PhD students working on diverse topics including metamaterials, composite structures, and advanced structural analysis techniques. He teaches core aerospace engineering courses including Aerospace Vehicle Design and Aeroelasticity, as well as Fundamentals of Structural Mechanics. He is involved in significant research projects including ASSESS (Automated online monitoring of smart composite structures, 2024-2028), an EU-funded MSCA project, and DRONE-DARE (Drone imaging for Digital twinning and AI Reliability-based damage Evaluation, 2024-2025), part of the PNRR Mission 4 initiative. These projects reflect his expertise in structural health monitoring, composite materials, and advanced computational methods.

Steffen A. Bassمشاهده پروفایل

استاد

• Theoretical Nuclear Physics
• Particle Physics
• Quark-Gluon Plasma
• +۷ مورد دیگر

Steffen A. Bass is the Arts and Sciences Distinguished Professor of Physics at Duke University, affiliated with the Department of Physics under Trinity College of Arts & Sciences. His research focuses on theoretical nuclear and particle physics, particularly the study of quark-gluon plasma through heavy-ion collisions, aiming to recreate and analyze the early Universe’s primordial state of matter. He is a principal investigator on multiple grants related to QCD matter, jet dynamics, and nuclear collisions at extreme energy densities. Education: Ph.D. (1997) and M.S. (1993) from Goethe Universitat Frankfurt Am Main (Germany). His research spans computational modeling of quark-gluon plasma using frameworks like JETSCAPE and PHSD, integrating Bayesian inference, viscous hydrodynamics, and multi-fidelity Gaussian processes. He investigates nonequilibrium effects, parton energy loss, and hadronization mechanisms in high-energy collisions, contributing to understanding QCD phase structure and transport coefficients. The 15 most recent publications highlight his work on jet quenching, quark-gluon plasma properties, and multi-scale modeling, with keywords spanning nuclear physics, computational methods, and QCD. His grants include DOE and NSF funding for heavy-flavor theory and hybrid modeling. While specific scientific awards and student details are not listed, his professional activities include conference organization and service to Duke University.

Elizabeth Qianمشاهده پروفایل

استادیار

• Scientific machine learning
• Model reduction
• Multifidelity methods
• +۵ مورد دیگر

Elizabeth Qian is a tenure-track Assistant Professor at Georgia Tech with joint appointments in the Schools of Aerospace Engineering and Computational Science and Engineering. She holds a visiting Hans Fischer Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) since 2023. Her research develops computational methods for scientific machine learning, model reduction, and multifidelity approaches to accelerate engineering decision-making. Education : PhD (2021), SM (2017), and SB (2014) in Aerospace Engineering from MIT Her work focuses on enabling efficient simulations for complex systems through model reduction , scientific machine learning , and multifidelity methods . Recent publications address Bayesian inverse problems, neural operator learning trade-offs, and ensemble Kalman methods. Scientific awards include the 2025 NSF CAREER Award, 2024 AFOSR YIP Award, 2023 TUM-IAS Fellowship, 2022 SIAM Student Paper Prize, and 2014 Hertz/NSF/Fulbright fellowships. She mentors GT PhD researchers and contributes to diversity initiatives in engineering communities.