جست‌وجوی استاد و پژوهشگر

• Big Data Analytics
• Particle Physics
• Deep Learning
• +۷ مورد دیگر

Johannes Erdmann is a University Professor and leads the Research and Teaching Area of Big Data Analytics in Physics at RWTH Aachen University. He is affiliated with the Department of Physics within the Faculty of Mathematics, Computer Science and Natural Sciences. His research integrates advanced data science techniques with fundamental physics investigations. His research focuses on leveraging big data and machine learning methods to solve complex problems in particle and astroparticle physics. Key areas include the physics of the Higgs boson, data analysis at the CMS experiment at CERN, and the development of deep learning techniques for high energy physics. He also contributes to gravitational wave physics, particularly in noise suppression strategies for the future Einstein Telescope. The recent publications highlight a strong trend toward methodological innovation, especially in deep learning applications for particle physics simulations and data analysis. His team is actively developing novel neural network architectures, fast simulation tools, and automated quality assurance systems, all aimed at enhancing the precision and efficiency of large-scale physics experiments. Heisenberg-Professur funded by the Deutsche Forschungsgemeinschaft (DFG) Prof. Erdmann supervises research projects and leads a research group focused on data-intensive physics. He is actively involved in securing and managing research grants, particularly those supporting computational and methodological developments in experimental physics. His group collaborates with international teams at CERN and within the Einstein Telescope consortium. His research group, part of the Big Data Analytics in Physics unit, works on cutting-edge challenges in data processing for particle and gravitational wave physics. The team develops deep learning models, simulation frameworks, and data quality tools, contributing to major international collaborations such as CMS and the Einstein Telescope project.

Shravan Veerapaneniمشاهده پروفایل

استاد

• Scientific computing
• Fast algorithms
• Potential theory
• +۱۰ مورد دیگر

Shravan Veerapaneni is a Professor in the Department of Mathematics at the University of Michigan, within the College of Literature, Science, and the Arts. His research focuses on developing large-scale computational tools for solving differential and integral equations on complex moving geometries that arise in engineering and biophysics. His work spans multiple interdisciplinary areas connecting mathematics, computational science, and applied physics. Education: B.S. from Indian Institute of Technology (2003), Ph.D. from University of Pennsylvania (2008) Previous Position: Research Scientist at Courant Institute of Mathematical Sciences (NYU), 2008-2011 Teaching: Courses include Math 671, Math 371 (Numerical Methods for Engineers), and Math 156 (Applied Honors Calculus II) Professor Veerapaneni's research interests encompass scientific computing, fast algorithms, potential theory, complex fluids, microfluidics, soft-matter, and biomechanics. His core application areas include biomembrane mechanics, blood flow modeling, cilia-driven flows, and microfluidic-chip design. More recently, he has expanded his research to include scalable solvers and machine learning techniques for autonomous vehicle mobility in off-road settings. His work demonstrates a strong emphasis on developing high-order accurate numerical methods with practical applications in biomedical engineering and fluid dynamics. His publications reveal a consistent focus on boundary integral methods, Stokes flow simulations, and optimization problems in complex geometries. The research shows progression from fundamental mathematical methods to increasingly complex applications in biophysics and engineering. His work on vesicle dynamics, microswimmers, and particulate suspensions demonstrates expertise in computational fluid dynamics at microscales. NSF CAREER Award (2015) for project 'Fast Algorithms for Particulate Flows' Professor Veerapaneni has developed computational frameworks for simulating complex fluid-structure interactions, with applications ranging from biological systems (vesicles, cilia) to engineering problems (microfluidic chips, autonomous vehicles). His group has produced significant software contributions including visualization tools for fluid dynamics simulations, as evidenced by the animations of vesicle flows on his website. His collaborative work spans mathematics, engineering, and computer science departments, reflecting the interdisciplinary nature of his research.