جست‌وجوی استاد و پژوهشگر

• Medical Robotics
• Artificial Intelligence
• Radiosurgery
• +۳ مورد دیگر

Prof. Achim Schweikard is a Full Professor at the University of Lübeck's Institute for Robotics and Cognitive Systems, where he has led pioneering research since 2002. His academic journey spans Stanford University's neurosurgery and computer science departments and Technical University of Munich, establishing him as a leading figure in medical technology translation. His research focuses on Medical Robotics and Artificial Intelligence with transformative clinical applications. Key achievements include inventing correlation-based tracking for cancer radiosurgery (now global standard of care) and developing stereotactic arrhythmia radioablation (STAR) protocols. Current projects address critical challenges in cardiac radioablation through the RAVENTA multicenter trial, MATRIX-VT imaging fusion study, and SonoBox robotic ultrasound system for pediatric diagnostics. His publication record shows sustained high impact with 15+ articles in 2023-2025 in top journals including Radiotherapy and Oncology and Heart Rhythm. These works demonstrate progression from foundational robotics to clinical implementation, with recent focus on cardiac motion estimation, target transfer automation, and S-ICD patient treatment solutions. As Academic Director of UNI Luebeck's Grad School (2025), he oversees doctoral training while leading the Robotics Laboratory (RobLab). The lab maintains active grants supporting: RAVENTA trial: German multicenter feasibility study for ventricular tachycardia radioablation DYNAMIC phantom development for end-to-end treatment validation ECG-gated 4D CT motion modeling for precision targeting Landmark-based ultrasound-CT fusion techniques Students gain comprehensive experience across the medical technology pipeline - from hardware prototyping to clinical trial participation - within an internationally collaborative environment spanning the STOPSTORM.eu consortium and multiple EU research networks.

• Control Theory
• Model Predictive Control
• Data-Driven Control
• +۵ مورد دیگر

Frank Allgöwer is a Professor and Head of the Institute for Systems Theory and Control Engineering at the University of Stuttgart's Faculty of Engineering. With an extensive publication record through 2025, he leads a prominent research group specializing in advanced control theory methodologies. His research interests span multiple domains of modern control theory, with particular emphasis on Model Predictive Control (MPC), data-driven control approaches, nonlinear systems analysis, and event-triggered control strategies. His work bridges theoretical foundations with practical implementations, focusing on stability guarantees, performance optimization, and computational efficiency. Recent research shows a strong focus on Koopman operator theory applications to control systems, distributed multi-agent coordination, and the integration of machine learning techniques with traditional control frameworks. Analysis of his 15 most recent publications reveals a consistent research trajectory centered around developing theoretically sound control methodologies with practical applicability. His work demonstrates increasing integration of data-driven approaches with traditional model-based control, particularly in the areas of nonlinear system control and distributed multi-agent systems. The publications span top-tier control journals including IEEE Transactions on Automatic Control, Automatica, and IEEE Control Systems Letters. Professor Allgöwer actively mentors numerous junior researchers, with frequent collaborations suggesting a strong supervisory role for PhD students and postdoctoral researchers. His group maintains productive international collaborations while being firmly rooted at the University of Stuttgart.

Dominik Stögerمشاهده پروفایل

استادیار

• Mathematical Foundations of Data Science
• Non-convex Optimization
• Machine Learning Theory
• +۴ مورد دیگر

Dominik Stöger is an Assistant Professor (tenure-track) in the Department of Mathematics at KU Eichstätt-Ingolstadt since 2021, affiliated with the Mathematical Institute for Data Science and Machine Learning (MIDS). His research bridges mathematical theory and data science applications. His educational background includes: B.Sc. in Mathematics, Technical University of Munich (2013) M.Sc. in Mathematics, Technical University of Munich (2015) Ph.D. in Mathematics, Technical University of Munich (2019) Stöger's research centers on mathematical foundations of data science, with emphasis on non-convex optimization in machine learning, theoretical analysis of overparameterized models, and low-rank matrix recovery. He combines optimization theory and high-dimensional probability to develop rigorous guarantees for modern algorithms, addressing critical challenges in deep learning theory. His recent publications (2020-2025) demonstrate consistent output in top venues including COLT, NeurIPS, and ICLR, with particular focus on implicit regularization phenomena and non-convex recovery guarantees. The 2025 pipeline shows active work extending theoretical boundaries in matrix sensing and neural network analysis. Stöger has received significant recognition: NeurIPS 2021 Spotlight Paper (top 3% of submissions) COLT 2025 paper presentation As a tenure-track faculty member, he maintains active collaborations across institutions (USC, TUM) and likely advises graduate students. His research program shows strong momentum with multiple concurrent projects advancing theoretical machine learning. He contributes to the research ecosystem through affiliation with MIDS, fostering interdisciplinary work in mathematical data science at KU Eichstätt-Ingolstadt.