جست‌وجوی استاد و پژوهشگر

AHMET İHSAN KUTLARمشاهده پروفایل

استاد

• Mechanical Engineering
• Fluid Mechanics
• Aerodynamics
• +۵ مورد دیگر

Professor Ahmet İhsan Kutlar is a distinguished academic in the Department of Mechanical Engineering at Gaziantep University's Faculty of Engineering, Turkey, with a career spanning over 30 years. His expertise lies in Fluid Mechanics, Aerodynamics, Thermodynamics , and Computational Fluid Dynamics (CFD) , alongside significant contributions to biomedical engineering through glaucoma drainage device research. Academic Roles: Professor (2018–present), Associate Professor (2012–2018), Assistant Professor (1993–2012), Research Assistant (1987–1993). Education: Doctorate in Mechanical Engineering (Thermofluids) from the University of Liverpool (1993), Master's and Bachelor's from Middle East Technical University. Kutlar's research integrates CFD simulations with experimental work, focusing on compressible flows, multiphase systems, and biomedical applications. His projects include glaucoma valve analysis and renewable energy thermodynamic studies. The 15 most recent articles highlight his work in Biomedical Engineering (e.g., glaucoma device testing), Computational Fluid Dynamics (adaptive solvers), and Thermodynamic Systems (power plants, energy recovery). These publications reflect a multidisciplinary approach combining theoretical and experimental methods. Kutlar has led and participated in TÜBİTAK and TEKMER projects, and contributed to academic leadership as Vice Dean and Faculty Board Member. He has supervised numerous PhD and Master's theses, emphasizing innovative engineering education and applied research .

Kapil Ahujaمشاهده پروفایل

استاد

• Machine Learning
• Network Science
• Computational Optimization
• +۴ مورد دیگر

Prof. Kapil Ahuja is a Full Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Indore (IIT Indore), where he heads the Mathematics of Data Science and Simulation (MODSS) research lab. After completing dual Master's degrees and a Ph.D. from Virginia Tech (USA) followed by postdoctoral work at the Max Planck Institute in Germany, he has held visiting positions at UT Austin, IMT Atlantique, Sandia National Labs, TU Dresden, and TU Braunschweig. His administrative roles include founding Dean of International Affairs and former Head of Computer Science & Engineering at IIT Indore. Education: Ph.D. in Mathematics, Virginia Tech (2011) M.S. in Mathematics, Virginia Tech (2009) M.S. in Computer Science, Virginia Tech (2007) B.Tech. in Mechanical Engineering, IIT (BHU) Varanasi (2001) Research Focus: Prof. Ahuja's work bridges theoretical advances with real-world applications, emphasizing machine learning algorithms for plant/cancer studies, game-theoretic poverty reduction models, exascale climate modeling solvers, and drone trajectory optimization. His interdisciplinary approach integrates numerical linear algebra with network science to solve complex systems problems across healthcare, agriculture, and climate science, supported by 4.85 Crores INR in external funding. Publication Trends: Recent work demonstrates growing emphasis on AI-driven optimization for physical systems (drones, climate models) and biomedical applications (cancer classification). His publications increasingly feature cross-disciplinary collaborations between computer science, biology, and economics, with notable contributions in explainable AI for healthcare and resource allocation algorithms for social networks. Scientific Recognition: National Teacher's Award (2024) from the President of India Five-time recipient of IIT Indore's Best Teacher Award (2013-2023) Best Poster Award at International Workshop on Game Theory & Networks (2019) Steeneck Graduate Research Fellowship (Virginia Tech, 2011) Multiple SIAM travel awards for international conferences Mentorship & Service: Prof. Ahuja has graduated 5 Ph.D. and 4 M.S. (Research) students while mentoring 75 B.Tech. projects. He serves as Associate Editor for Applied Intelligence Journal (Springer Nature) and Knowledge and Information Systems, organizes international conferences, and reviews for 35+ academic sources. His administrative leadership significantly expanded IIT Indore's global partnerships through the Research Park initiative. Research Infrastructure: The MODSS lab maintains active collaborations with Oak Ridge National Lab, Sandia National Labs, and European institutions. Current projects include AI-optimized drone swarms for agricultural monitoring and game-theoretic models for poverty intervention, utilizing high-performance computing resources for large-scale simulations.

Yi Liuمشاهده پروفایل

استادیار

• geometric deep learning
• AI for Science
• LLMs for Science
• +۳ مورد دیگر

Yi Liu is an Assistant Professor of Data Science jointly appointed in the Department of Applied Mathematics & Statistics and the Department of Computer Science at Stony Brook University. Previously, he served as an Assistant Professor in the Department of Computer Science at Florida State University. His academic background includes a Ph.D. in Computer Science from Texas A&M University (2022), an M.E. in Biomedical Engineering (2015), and a B.E. in Electronic Engineering (2012), both from the University of Science and Technology of China. Dr. Liu's research focuses on cutting-edge developments in artificial intelligence and computational science, with particular emphasis on: Geometric deep learning architectures for molecular and material systems AI-driven scientific discovery (AI4Science) Large language models for scientific applications 3D graph neural networks for molecular modeling Neural operators for physical system simulations His recent publications demonstrate a strong focus on developing novel AI methodologies for scientific applications, particularly in computational chemistry and materials science. Research trends include geometric deep learning for 3D molecular graphs, neural operators for physical modeling, explainable AI for chemical systems, and transformer-based approaches for material property prediction. The work consistently bridges fundamental AI research with applications in drug discovery, materials design, and scientific computing.

• Fluid Engineering
• Computational Fluid Dynamics
• Isogeometric Analysis
• +۲ مورد دیگر

Kenji Takizawa is a Professor at the Faculty of Science and Engineering, School of Creative Science and Engineering at Waseda University. He holds a PhD from Tokyo Institute of Technology (2005) and specializes in computational mechanics with a focus on fluid-structure interaction problems. His educational background includes: PhD in Energy Sciences from Tokyo Institute of Technology (2005) Master's degree in Energy Sciences from Tokyo Institute of Technology (2002) Bachelor's degree in Mechanо-Aerospace Engineering from Tokyo Institute of Technology (2001) Takizawa's research focuses on computational fluid dynamics, isogeometric analysis, and fluid-structure interaction. His work centers on developing advanced computational methods for complex engineering problems involving moving boundaries, contact mechanics, and turbulent flows. He has pioneered space-time variational multiscale (ST-VMS) methods that enable high-fidelity simulations of challenging problems such as heart valve flows, tire aerodynamics, and wind turbine wake dynamics. His recent publications reveal a strong focus on space-time computational methods with applications spanning aerospace engineering, biomedical devices, automotive systems, and renewable energy. The research demonstrates consistent innovation in handling complex geometries, moving boundaries, and multi-scale phenomena through integrated computational frameworks that combine isogeometric analysis with topology change capabilities. His significant scientific achievements have been recognized through numerous prestigious awards: 2022 APACM Computational Mechanics Award 2018 JSPS Prize Multiple Highly Cited Researcher designations (2016-2018) Thomas J.R. Hughes Young Investigator Award (2012) Computational Mechanics Achievement Award from JSME (2014) Takizawa has made substantial contributions to computational mechanics through his development of innovative numerical methods that address previously intractable problems involving moving boundaries and interfaces. His work bridges theoretical advancements with practical applications across multiple engineering disciplines.

• Knowledge Representation and Reasoning
• Geometry Processing
• Machine Learning
• +۱ مورد دیگر

Dr. Stavros Nousias is a researcher at the Chair of Computing in Civil and Building Engineering at the Technical University of Munich , focusing on applications of Artificial Intelligence in the Built Environment . His work bridges Knowledge Representation and Reasoning , Geometry Processing , and Machine Learning to advance construction informatics and digital twinning. Research Interests: AI for building evacuation prediction, technical drawing segmentation, BIM optimization, and respiratory disease modeling. Publications: 15+ peer-reviewed articles on topics including graph neural networks for construction simulations, pulmonary airflow analysis, and heritage site monitoring. Supervised Theses: Guided projects on AI-based BIM command prediction and robotized construction simulation . Labs: Active in the BIM-Lab and Robotic Fabrication Lab . Teaching: Co-instructor for courses like Artificial Intelligence in Engineering and Computation in Engineering 1 .

Jennyمشاهده پروفایل

استاد

• Turbulent Combustion Modeling
• Computational Fluid Dynamics
• Rarefied Gas Kinetics
• +۴ مورد دیگر

Professor Jenny leads the Jenny Research Group at ETH Zürich, specializing in turbulent reactive flows, rarefied gas kinetics, and biomedical fluid dynamics. Her work bridges fundamental research with industrial applications in energy systems and fluid mechanics. Develops advanced turbulence models (TDDM, hybrid LES/RANS) for multi-scale flows Pioneers data assimilation frameworks for RANS simulations using adjoint methods Advances particle-based stochastic algorithms for fractured porous media transport Her recent publications emphasize adaptive time integration techniques, probabilistic modeling of non-linear transport phenomena, and optimized simulation tools for hydrogen storage systems. The group's methodological innovations focus on reducing computational costs while maintaining physical accuracy through novel regularization strategies. Key applications include combustion device optimization, high-pressure tank filling analysis, and fractured reservoir simulations. Current projects integrate machine learning with traditional CFD methods to address challenges in droplet clustering, flame surface density propagation, and supersonic spray dynamics. The research framework spans from direct numerical simulations of fundamental flow physics to industrial-scale hybrid modeling implementations.

Philipp Greteمشاهده پروفایل

پژوهشگر

• Computational Astrophysics
• Magnetohydrodynamic Turbulence
• High Performance Computing
• +۴ مورد دیگر

Dr. Philipp Grete is a postdoctoral research associate at the Hamburg Observatory (University of Hamburg), previously holding a Marie Skłodowska-Curie Fellowship at the same institution and a postdoctoral position at the Department of Physics & Astronomy, Michigan State University . His interdisciplinary research bridges astrophysics and computational methods , focusing on: Magnetohydrodynamic turbulence in astrophysical systems Performance-portable exascale simulation frameworks (Parthenon, AthenaPK) Cosmic ray transport mechanisms Anisotropic transport processes in weakly collisional plasmas Supercomputer-driven AGN feedback analysis He leads the XMAGNET project using DOE INCITE allocations on exascale systems and recently secured DFG funding for three years. His work has been recognized with the Postdoctoral Excellence in Research Award (MSU), SC23 Best Paper nomination, and CUG23 Best Paper Runner-up award.

Albert Cerroneمشاهده پروفایل

استادیار

• Digital Twin
• Coastal Hydraulics
• Materials Durability
• +۴ مورد دیگر

Albert Cerrone serves as a Research Assistant Professor in the Department of Civil & Environmental Engineering & Earth Sciences at the University of Notre Dame's College of Engineering and holds a concurrent appointment as Senior Research Fellow at the Oden Institute, University of Texas at Austin. His work bridges academic research and industrial applications through Digital Twin technologies. Dr. Cerrone earned his PhD in 2014 from Cornell University under Tony Ingraffea, specializing in multiscale modeling of microcrack nucleation in superalloys. Prior to Notre Dame, he conducted durability research on ceramic matrix composites at GE Research's Lifing Technologies Laboratory. His research integrates machine learning with computational mechanics to advance Digital Twin frameworks across geospatio-temporal domains and materials systems. Key applications include probabilistic storm surge forecasting using high-fidelity hydrodynamics models, durability assessment of additively manufactured metals, and ultrasound-mediated biofilm disruption for inflammatory disease therapies. He employs transformer networks for real-time hydrodynamic error correction and develops geometric modeling tools for viral and biofilm structures. Recent publications demonstrate a clear trajectory toward operationalizing Digital Twin concepts, with increasing emphasis on machine learning integration for coastal inundation modeling and cross-disciplinary materials characterization. His work spans fundamental mechanics to deployable operational systems like NOAA's STOFS-2D-Global. Dr. Cerrone actively collaborates with NASA Langley on additive manufacturing projects and contributes to cystic fibrosis treatment research with Trinity College Dublin. His laboratory work includes computational modeling using ABAQUS, DREAM.3D, and custom Python frameworks for mesh generation and geometric reconstruction. Developed Polycrystal Volume Mesher for microstructure meshing Created Virion to Shell code for virus geometric modeling Teaches Solid Mechanics, Statics, and Engineering Programming