Find Academic Supervisors

Samuel James NewcomeView profile

Researcher

• Molecular Dynamics Simulations
• Algorithm Selection
• High Performance Computing
• +3 more

Samuel James Newcome is a Researcher at the Technical University of Munich (TUM), affiliated with the Chair for Scientific Computing in Computer Science within the Department of Computer Science. He has been at TUM since 2022, focusing on performance algorithms for Molecular Dynamics (MD) simulations, teaching, and organizing academic events like the Ferienakademie. Education M.Sc. in Modern Applications in Mathematics, University of Bath (2021) B.Sc. in Mathematical Sciences, University of Bath (2020) Research Interests Molecular Dynamics Simulations (multi-site and 3-body) Algorithm Selection and Auto-Tuning Kernel-Level Optimizations Geometric Numerical Integration Articles Trends Recent work emphasizes algorithm selection strategies and optimization in MD simulations, particularly leveraging AutoPas for dynamic algorithm selection and energy/time efficiency. Research spans interdisciplinary applications in physics, chemistry, and high-performance computing. Advising & Grants Supervised over 20 theses on topics like algorithm selection, kernel optimizations, and molecular dynamics methods. Guides students in developing parallelization, vectorization, and simulation frameworks (e.g., AutoPas, ls1-Mardyn). Labs/Teams Active contributor to the Chair of Scientific Computing in Computer Science, collaborating on projects like MaST and AutoPas. Engaged with initiatives such as BGCE, CSE, and IGSSE at TUM.

• Energy Engineering
• Thermodynamics
• Fluid Mechanics
• +5 more

Prof. Carsten Schönfelder is a Professor at Berlin University of Technology's Department of Mechanical Engineering, specializing in energy systems, thermodynamics, and simulation technologies. His academic roles include advising the Computational Engineering and Design bachelor’s program and teaching courses such as Thermodynamics, Fluid Mechanics, and Hydraulics. He leads research in renewable energy technologies, digital twin development for energy plants, and hydrogen applications. His department operates laboratories KEE, TVT/MVT, PRT, and CAE, focusing on advanced engineering experiments. Education background: Holds a PhD from RWTH Aachen University (2007) with research on fuel cell systems for vehicle applications. His academic career includes roles at DaimlerChrysler AG and the Institut Francais du Pétrole. Research interests span energy storage, hydrogen technology, and simulation methodologies. He actively develops modular simulation environments and digital twins for energy infrastructure. Current projects include hydrogen compressor control systems and sector-coupled energy storage solutions. Publications emphasize practical applications of simulation tools in education and industry, including hydraulic systems training and digital exam methodologies. Theses supervised focus on topics like PEM electrolyzers, compressor efficiency, and eco-friendly HVAC systems. Labs and teams: Collaborates with KEE and MVT/TVT labs on advanced thermal systems and fluid dynamics experiments. Engages in interdisciplinary projects bridging mechanical engineering and environmental technology.

• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• Load Balancing
• +4 more

Florina M. Ciorba is a Professor in the field of High-Performance Computing (HPC), affiliated with the University of Basel. Her research focuses on parallel algorithms, fault-tolerant systems, dynamic load balancing, and energy-efficient computing. She has contributed extensively to HPC workflows, scheduling techniques, and resilient numerical methods for extreme-scale simulations. Key research areas include: Parallel and distributed systems Load balancing and scheduling algorithms Fault tolerance mechanisms GPU and heterogeneous computing Energy-aware HPC applications Recent work emphasizes scalable particle simulations, application classification using ML, and optimizing astrophysics workflows. She collaborates with international teams on standards like SPEChpc 2021 benchmarks and HPC operational autonomy loops. Publications span top venues including IEEE Transactions on Parallel and Distributed Systems, Euro-Par, and Cluster conferences. Her work addresses both algorithmic innovations and software implementations for next-generation HPC platforms.

• Laser Plasma Physics
• High-Performance Computing (HPC)
• Computational Physics
• +3 more

René Widera is a researcher at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), specifically within the Laser Particle Acceleration department of the Institute of Radiation Physics. His work focuses on advancing high-performance computing (HPC) techniques for plasma simulations, particularly leveraging GPU architectures and exascale computing frameworks. He contributes to the development and optimization of the PIConGPU code, a leading particle-in-cell (PIC) simulation tool. His research integrates machine learning for real-time data analysis, parallel algorithms for HPC scalability, and cross-platform visualization strategies. Areas of expertise include laser plasma acceleration, high-energy-density physics, and the design of efficient numerical methods for large-scale simulations. He explores hardware-agnostic solutions for computational challenges, including memory access optimizations and DAG-based parallelism. Collaborations involve international HPC initiatives and open-source software projects like openPMD and alpaka . Key projects include the TWEAC initiative to overcome limitations in laser-wakefield acceleration and the development of in-situ visualization pipelines for real-time simulation insights. He also evaluates modern GPU architectures (e.g., AMD, ARM-based systems) for scientific workloads. His contributions bridge theoretical plasma physics with practical computational advancements, aiming to enable next-generation high-intensity laser experiments.

• Machine Learning
• Computer Vision
• Medical Imaging
• +5 more

Wenwen Zhang is a Professor at the Department of Electronic Engineering, College of Information Science and Electronic Engineering, Zhejiang University. With an extensive publication record spanning from 2016 to 2025, Dr. Zhang has established herself as a prominent researcher in multiple interdisciplinary fields at the intersection of computer vision, machine learning, and sensor systems. Her work demonstrates significant contributions to medical imaging, sensor array systems, wireless communications, and AI-assisted applications. Dr. Zhang's research interests encompass a wide range of topics including medical image analysis, sensor array systems, wireless communications, and AI-assisted applications. Her work demonstrates particular expertise in developing innovative deep learning architectures for medical imaging tasks such as cardiac segmentation and nuclei detection, as well as creating sophisticated models for gas sensing and wireless communication systems. She has made significant contributions to the fields of one-shot object detection, medical image segmentation, and sensor fusion techniques, with her research often bridging theoretical advancements with practical applications in healthcare and engineering. Analysis of Dr. Zhang's recent publications reveals a strong focus on cutting-edge deep learning approaches applied to medical imaging and sensor systems. Her work shows increasing sophistication in model architectures, moving from traditional CNNs to more complex transformer-based and hybrid models. There's a clear trajectory toward more explainable and clinically relevant AI systems, particularly in medical applications. Her research also demonstrates growing interest in multimodal approaches, combining different types of data and sensors to improve system performance. Dr. Zhang maintains active collaborations with researchers at Zhejiang University, particularly with Yuanjin Zheng and Zhiping Lin in the field of electronic engineering and sensor systems. She also collaborates extensively with Fei-Yue Wang from the University of Chinese Academy of Sciences, evidenced by multiple publications on parallel vision frameworks. Her international collaborations include work with researchers from institutions in Canada on intelligent knee sleeves and other biomedical applications. Her publication record shows consistent productivity with 12 publications in 2025 (as of this writing), 25 in 2024, and 26 in 2023, indicating an active and growing research program across multiple high-impact journals and conferences.

Lorenzo MasiaView profile

Professor

• Robotics
• Rehabilitation Engineering
• Wearable Robotics
• +5 more

Prof. Lorenzo Masia is a Full Professor in Intelligent Bio-Robotics Systems and Deputy Director of the Munich Institute for Robotics and Machine Intelligence (MIRMI) at TUM. He holds a Master's from Sapienza University of Rome (2003) and a PhD from the University of Padua (2007). His career includes roles at MIT (2005-2006), the Italian Institute of Technology (2007-2012), Nanyang Technological University (Singapore), the University of Twente (Netherlands), and Heidelberg University (2019-2024). Research Focus: Exosuits, rehabilitation robotics, human-robot interaction, and wearable systems. Leadership Roles: Program Chair of IEEE ICORR 2015, BIOROB 2016, and General Chair of BIOROB 2024. Labs: ARIES Lab (Assistive Robotics and Interactive Ergonomic Systems), MIRMI. His research interests span the design of mechatronic devices for healthcare and human augmentation, with a focus on soft robotics for rehabilitation. Notable contributions include exosuit development for improving mobility in older adults and individuals with neurological disorders. Key Achievements: Winner of multiple IEEE Best Paper/Student Paper Awards at ICORR and BIOROB conferences. Editorial roles at IEEE Transactions on Robotics, Neural Systems and Rehabilitation Engineering, and Wearable Technologies. Grants & Collaborations: Extensive funding in robotics and rehabilitation, leading interdisciplinary projects with clinical and industrial partners.

Fabian HollsteinView profile

Professor

• Empirical Asset Pricing
• Financial Econometrics
• Quantitative Methods in Finance
• +7 more

Prof. Dr. Fabian Hollstein is a Professor of Quantitative Methods in Economics and Finance at Saarland University, affiliated with the Faculty of Human and Business Sciences. He heads the Chair of Quantitative Methods in Economics and Finance and currently serves as Dean of Studies, which has influenced his recent teaching load. His research is centered on empirical asset pricing, financial econometrics, and quantitative finance, with a strong focus on risk modeling and market dynamics. His research interests include empirical asset pricing, financial econometrics, beta and volatility modeling, tail risk measurement, machine learning applications in finance, factor models, and commodity markets. He employs advanced quantitative techniques to analyze financial data, with a particular emphasis on high-frequency data and predictive modeling. The most recent publications highlight a consistent trend in advancing empirical methods in asset pricing, particularly through machine learning, robustness testing of factor models, and deep analysis of risk components such as volatility-of-volatility and tail risk. His work spans global equity, bond, and commodity markets, demonstrating both methodological rigor and broad applicability. Special Prize from the Deutsche Bundesbank for 'Measuring Tail Risk' (2025) Best Paper Award at the Spanish Finance Forum (2023) Editor's Choice Article in Review of Asset Pricing Studies (2023) Lead Article in Financial Management (2024) Ranked 24th under 40 and 66th overall in WirtschaftsWoche ranking (2020–2024) Prof. Hollstein has secured multiple research grants from the German Research Foundation (DFG), including funding for 'Advances in Empirical Asset Pricing' (€176,077) and 'Market Jumps: Causes and Investor Reactions' (€362,386). He supervises doctoral students, as indicated by active job postings for PhD positions, and contributes to academic training through bachelor’s, master’s, and doctoral seminars. His team includes research associates and student assistants working on econometric and financial modeling projects. The Chair of Quantitative Methods in Economics and Finance, under his leadership, conducts cutting-edge research and offers courses in econometrics, statistical programming (R), and asset pricing. The team collaborates with researchers from Leibniz University Hannover and other institutions, fostering a strong interdisciplinary and international research environment.

Tobias BauerView profile

Researcher

• Neuroimaging
• Neurology
• Neuroradiology
• +7 more

Dr. Tobias Bauer, MD, BSc, is a postdoctoral researcher and assistant physician at the University of Bonn, affiliated with the Department of Neuroradiology and the Clinic and Polyclinic for Epileptology. He is a member of the Translational Neuroimaging Working Group led by PD Dr. med. Theodor Rüber, under the directorship of Prof. Dr. med. Alexander Radbruch and Prof. Dr. med. Rainer Surges. His work bridges clinical practice and advanced neuroimaging research, focusing on autoimmune encephalitis and epilepsy. University: University of Bonn School: Medical Faculty Department: Department of Neuroradiology Position: Researcher Email: tobias.bauer@ukbonn.de Dr. Bauer studied nautical science at Wismar University, mathematics at FernUniversität Hagen, and human medicine at the University of Bonn, where he earned his doctorate on structural connectivity in limbic encephalitis. His research focuses on image-guided phenotyping of autoimmune encephalitis using 7T MRI and data-driven modeling. He aims to develop clinically relevant biomarkers for improved diagnosis and treatment planning in both adults and children. The recent publications highlight a strong trend in computational neuroimaging, machine learning for lesion detection, and multimodal MRI analysis in epilepsy. His work spans from fundamental brain connectivity studies to translational applications, including portable MRI and AI benchmarks. This reflects a cohesive research program integrating clinical neurology, advanced imaging, and data science. Scientific awards include: Merit Award, Organization for Human Brain Mapping (2022) Medical Excellence Award, Manfred-Lautenschläger-Foundation (2021) BONFOR SciMed Grant (2019) Cusanuswerk Scholarship (2019) BonnNI Structured MD Program (2018) Dr. Bauer has been supported by multiple grants and is actively involved in national and international collaborations. He supervises no students explicitly mentioned, but contributes to a vibrant research environment in the Translational Neuroimaging Group. His lab focuses on developing imaging biomarkers and disease models in autoimmune encephalitis, leveraging ultrahigh-field MRI and computational tools.

• High-Performance Computing
• Parallel Programming
• Compiler Optimization
• +3 more

Richard Schulze is a Researcher at the University of Münster, contributing to projects such as SkelCL, PACXX, and dOpenCL. His work focuses on parallel computing, compiler optimization, and auto-tuning frameworks for high-performance and distributed systems. He explores portable code generation for heterogeneous architectures using Multi-Dimensional Homomorphisms (MDH) and develops abstractions for OpenCL/CUDA programming. His research interests include advancing scheduling languages and systematic composition models, alongside probabilistic data linkage techniques. Recent publications emphasize auto-tuning methodologies for Python and interdependent parallel program parameters. Publications since 2018 highlight contributions to portable compiler design, performance optimization, and cross-platform parallelism. No scientific awards are explicitly mentioned. Consultation hours are by appointment, and he is affiliated with the university's computer science research groups.

Kürsat YurtView profile

Research Fellow

• High Performance Computing
• Performance Portable Programming
• Rotor and Wake Aerodynamics
• +3 more

Kürsat Yurt, M.Sc., serves as a Research Associate at the Institute for Rotorcraft and Vertical Flight, Technical University of Munich (TUM), based at Boltzmannstr. 15, 85748 Garching, Germany. His role encompasses advanced computational research in vertical flight systems, thesis supervision, and participation in multiple national and international projects targeting next-generation rotorcraft technologies and urban air mobility solutions. Yurt's research spans High Performance Computing, Performance Portable Programming, Rotor and Wake Aerodynamics, and Aeroelasticity. He develops GPU-accelerated meshless methods for large eddy simulations, creates real-time guidance algorithms for urban obstacle avoidance, and investigates morphing airfoil dynamics through fluid-structure interaction frameworks. His work bridges computational mechanics with practical rotorcraft design challenges, emphasizing energy efficiency and flight safety in complex environments. His 2022-2025 publications reveal a cohesive trajectory in computational rotorcraft aerodynamics, featuring innovations in meshless GPU simulations, partitioned coupling techniques, and urban air mobility guidance systems. These works integrate computational fluid dynamics, high-performance computing, and aerospace engineering to address vertical flight challenges including wake modeling, morphing rotor blades, and rotor-airframe interactions. Yurt actively supervises Master's and Bachelor's theses on vortex particle methods, rotorcraft simulation frameworks, and morphing rotor technologies. His research is funded through key projects: ENGEL - Energy Efficient Flight Guidance VARI-SPEED II ARCTIS LaBouR complemented by completed initiatives like InteReSt II and TEMA-UAV. The Institute provides critical infrastructure for his work through specialized facilities: Whirl Tower for rotor dynamics testing Flight Simulator Facilities for pilot-in-loop studies Unmanned Rotorcraft Testbed (AREA) High-performance GPU/CPU clusters for computational workloads These resources enable experimental validation of his computational models and support the institute's mission in vertical flight innovation.

Philipp GreteView profile

Researcher

• Computational Astrophysics
• Magnetohydrodynamic Turbulence
• High Performance Computing
• +4 more

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.