جست‌وجوی استاد و پژوهشگر

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.

Kurt Rothermelمشاهده پروفایل

استاد

• Distributed Systems
• Time-Sensitive Networking
• Networked Control Systems
• +۷ مورد دیگر

Prof. Kurt Rothermel is a faculty member at the University of Stuttgart, specifically affiliated with the Institute for Parallel and Distributed Systems (IPVS) within the Faculty of Computer Science, Electrical Engineering, and Information Technology. His research focuses on distributed systems with particular expertise in time-sensitive networking, networked control systems, and complex event processing. Prof. Rothermel's research interests span multiple areas in distributed computing and networking: Distributed Systems and Networked Control Systems Time-Sensitive Networking and Deterministic Networking Quality of Service (QoS) Management and Optimization Complex Event Processing and Stream Analytics Edge Computing and Real-Time Video Analytics Digital Twin Systems and Proximity-based Services His recent publications (2022-2025) demonstrate a strong focus on time-sensitive networking, with numerous papers addressing scheduling algorithms, conflict graph creation, and latency management for time-triggered communication. There's also significant work on load shedding techniques for resource-constrained environments, particularly for real-time video analytics at the edge. His research increasingly incorporates digital twin systems and explores novel approaches for distributed mobile simulation, as evidenced by his "Persival" framework for handling 3D meshes on AR devices. Prof. Rothermel has made substantial contributions to the field of complex event processing, developing multiple shedding strategies (gspice, hSPICE, pspice) to manage resource constraints while maintaining utility. His work bridges theoretical networking concepts with practical applications in industrial settings, particularly evident in his research on networked control systems and time-sensitive software-defined networks.

• 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 .

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.

Manuel Behrendtمشاهده پروفایل

پژوهشگر ارشد

• Astrophysics
• Computational Astrophysics
• Galaxy Formation and Evolution
• +۳ مورد دیگر

Dr. Manuel Behrendt is a Research Fellow at the Ludwig-Maximilians-Universität München, where he serves as a staff scientist at the University Observatory in the CAST-group led by Prof. Andreas Burkert. He also holds a position with the Physics of Galactic Nuclei (PGN) group at the Max-Planck-Institute for Extraterrestrial Physics in Garching, Bavaria. His primary research focuses on the structure-formation and evolution of galactic discs in the early universe, utilizing high-resolution hydrodynamic simulations within the framework of gravitational disc instability. Dr. Behrendt's work addresses fundamental questions about: The formation and evolution mechanisms of giant clumps and small-scale structures in high-redshift galaxies The hierarchical organization of clumps, including whether observed kpc-scale clumps are composed of sub-clump clusters The role of stellar feedback in shaping galactic structure formation and kinematics The energetic sources driving high random motions observed in young galaxies A significant contribution to the field is his development of MERA.jl, a high-performance Julia package designed for analyzing large-scale astrophysical simulation data. This tool provides: Efficient numerical performance through Julia's JIT compilation Unified API for handling multi-resolution AMR grids and particle datasets Interactive development capabilities that scale from notebooks to production scripts Memory-conscious design for processing large datasets Multi-threaded I/O operations for improved performance Dr. Behrendt's publication record demonstrates a consistent research trajectory focused on galaxy formation processes, particularly examining clump structures in high-redshift galaxies. His work bridges theoretical astrophysics with practical computational methods, making important contributions both to our understanding of galaxy evolution and to the development of advanced analysis tools for the broader astrophysics community. As an educator, Dr. Behrendt actively supervises students, teaches tutorials and astrophysical laboratory courses, and serves as a substitute lecturer for Prof. Burkert's courses. His dual commitment to research excellence and educational mentorship highlights his comprehensive contribution to the academic community.

• Computational Mechanics
• Finite Element Methods
• Structural Analysis
• +۷ مورد دیگر

Professor Michael Kaliske is a leading researcher at Dresden University of Technology's Institute of Statics and Dynamics of Structures, where he conducts cutting-edge research in computational mechanics and structural engineering. His work bridges theoretical developments with practical applications across civil, mechanical, and materials engineering disciplines. Professor Kaliske's research focuses on computational mechanics with particular expertise in finite element methods, multiscale modeling, and material behavior under various loading conditions. His work spans diverse application areas including concrete technology, wooden structures, elastomeric materials (particularly tires), and uncertainty quantification in structural design. He has developed sophisticated numerical frameworks for analyzing complex structural behaviors, especially in contexts where traditional analytical methods fall short. His research demonstrates strong interdisciplinary connections between civil engineering, mechanical engineering, and computational science with practical applications in infrastructure durability and material design. His publication record shows consistent advancement in computational methods for structural analysis, with recent work emphasizing multiscale approaches, uncertainty quantification, and experimental validation. The research themes demonstrate progression from fundamental material modeling to increasingly complex system-level analysis, particularly in infrastructure durability and tire-road interaction phenomena. His work increasingly incorporates data-driven approaches and uncertainty modeling to address real-world engineering challenges. Professor Kaliske actively leads multiple research projects funded by the German Research Foundation (DFG), including ongoing Priority Programs and Material Grants. His project portfolio demonstrates significant contributions to both theoretical developments in computational mechanics and practical applications in civil infrastructure and industrial manufacturing. His research has particularly strong connections to industrial applications in tire manufacturing and road infrastructure design.