Find Academic Supervisors

Jayesh BadwaikView profile

Researcher

• Computational Physics
• High Performance Computing
• Software Engineering
• +4 more

Dr. Jayesh Badwaik is a Scientific Researcher at the Jülich Supercomputing Center (JSC) within Forschungszentrum Jülich, a leading interdisciplinary research center in Europe. His work is centered in the Accelerating Devices Lab, where he focuses on high-performance computing architectures and computational methods for scientific applications. Dr. Badwaik's research spans multiple disciplines at the intersection of computer science, physics, and mathematics. His primary research interests include: Computational Physics and numerical methods for scientific computing High Performance Computing (HPC) with focus on exascale systems GPU programming models and accelerated computing Software engineering for large-scale scientific applications Lattice Boltzmann Methods for fluid dynamics simulations Parallel numerical algorithms for conservation laws Analysis of Dr. Badwaik's publication record reveals a clear evolution from theoretical numerical methods toward practical implementation on cutting-edge computing architectures. His early work (2016-2020) focused on mathematical foundations of numerical schemes for conservation laws, while his recent publications (2023-2024) demonstrate increasing emphasis on exascale computing challenges. His contributions to the JUPITER benchmark suite represent significant work in evaluating next-generation supercomputing systems. A substantial portion of his research centers on scaling the Lattice Boltzmann Method to exascale platforms, addressing critical challenges in computational fluid dynamics at unprecedented scales. His expertise in GPU programming is evident from his practical overview of programming models, which provides valuable insights for the HPC community. Dr. Badwaik is actively involved in the Accelerating Devices Lab at JSC, contributing to Europe's high-performance computing ecosystem. His work bridges theoretical numerical analysis with practical implementation on advanced computing architectures, making significant contributions to scientific computing at scale. His research has implications for multiple scientific domains that rely on large-scale simulations, including climate modeling, materials science, and computational fluid dynamics.

Reid TownsonView profile

Adjunct Professor

• Radiation Transport
• Medical Physics
• Monte Carlo Simulation
• +3 more

Dr. Reid Townson is an Adjunct Professor at the Department of Physics, Carleton University , and a Research Officer in Metrology at the National Research Council Canada . His research focuses on the development and application of the EGSnrc Monte Carlo radiation transport software toolkit , enabling high-precision simulations of electron, positron, and photon transport for medical and metrological applications. Primary Role: Research Officer, Metrology at National Research Council Canada Academic Role: Adjunct Professor at Carleton University Key Affiliation: National Research Council Canada Research Interests span radiation transport modeling, radiation dosimetry, Monte Carlo simulation software development, and the application of computational physics to emerging medical technologies. His work involves simulating ionization chambers, radiotherapy devices, and complex radiation scenarios to advance dose calculation accuracy and detector calibration. Publications emphasize Monte Carlo simulation techniques for radiation therapy (e.g., VMAT, IMRT, RapidArc), gamma-ray spectrometry benchmarks, and EGSnrc toolkit enhancements. His contributions include detector modeling, dose verification algorithms, and GPU-accelerated computation frameworks. Student Opportunities are highlighted through research projects involving EGSnrc, though specific student names are not listed. He utilizes platforms like GitHub for collaborative development.

Troels HaugbølleView profile

Associate Professor

• Star Formation
• Planet Formation
• Plasma Physics
• +5 more

Troels Haugbølle is an Associate Professor in the Astrophysics and Planetary Research group at the Niels Bohr Institute, University of Copenhagen. His work focuses on computational astrophysics related to star and planet formation, with particular expertise in numerical modeling of protostellar systems, disk dynamics, and plasma physics applications in astrophysical contexts. Research Interests: AMR models of star formation Particle-in-cell models Plasma physics Dust dynamics in protostellar environments Astro-chemistry integration in hydrodynamics Forward modeling to link simulations with observations His recent research shows a strong focus on protoplanetary disk formation mechanisms, particularly through Bondi-Hoyle accretion, and the evolution of angular momentum in young stellar systems. He has developed advanced computational methods including GPU-accelerated MHD simulations and Monte Carlo approaches for plasma physics. His work increasingly integrates multi-scale modeling to connect large-scale molecular cloud dynamics with the formation of planetary systems. Professional Activities: Maintains the starformation.hpc.ku.dk research platform Active contributor to major astrophysics journals including Nature Astronomy, Astronomy & Astrophysics, and Monthly Notices of the Royal Astronomical Society Collaborates internationally with researchers across Europe and beyond Dr. Haugbølle's work has been featured in 14 news outlets, blogged about by 2 sources, and shared by multiple social media users, demonstrating the impact of his research on the broader scientific community and public understanding of star and planet formation processes.

• Hydraulic Engineering
• Flood Risk Management
• Hydrology
• +3 more

Susanna Dazzi is a Fixed-term Researcher at the Department of Engineering and Architecture, University of Parma, Italy. Her work focuses on hydraulic engineering, flood risk management, and computational modeling, leveraging advanced techniques like physics-informed neural networks and GPU acceleration for real-time flood prediction. Key Research Areas: Flood hazard estimation, levee breach dynamics, dam-break scenarios, 2D hydraulic modeling, and sediment transport analysis. Teaching: Delivers courses on Hydraulic Infrastructures and Hydraulic Protection of the Territory for Master's Degree programs in Civil Engineering. Publications: Develops data-driven models (e.g., FloodSformer) for real-time flood forecasting and investigates hydrodynamic interactions with infrastructure. Contact: Email: susanna.dazzi@unipr.it ; Office: Science Area Park, 181/A, 43124 Parma, Italy.

Amit SawantView profile

Professor

• Radiation Oncology
• Medical Physics
• Image-Guided Radiotherapy
• +4 more

Amit Sawant is a Professor and Vice Chair of Medical Physics in the Department of Radiation Oncology at the University of Maryland School of Medicine. His research focuses on advancing image-guided radiotherapy techniques, particularly for stereotactic body radiotherapy (SBRT) and motion management in cancer treatment. His academic background includes: B.E.(Hons.) in Biomedical Engineering from University of Mumbai (1996) M.S. in Biomedical Engineering from University of Tennessee (1999) Ph.D. in Biomedical Engineering from University of Michigan (2006) Dr. Sawant's research centers on two interconnected domains: Small Animal Image-Guided Radiotherapy (SA-IGRT) and Next-Generation Motion Management. In SA-IGRT, he develops orthotopic tumor models for prostate, lung, and pancreatic cancer using the SARRP platform, investigates thermally-modulated radiotherapy to widen therapeutic windows, and creates advanced imaging techniques for mapping post-radiotherapy inflammation. His motion management research addresses 4D anatomical changes through patient-specific volumetric motion modeling, GPU-accelerated particle swarm optimization for 4D treatment planning, and real-time tumor tracking with dynamic multileaf collimators (MLC) that compensate for translation, rotation, and deformation. His publication record (2008-2016) demonstrates consistent innovation in motion-adaptive radiotherapy, with emphasis on lung SBRT. Key themes include 4D treatment planning optimization, real-time MLC tracking systems, motion phantom development, and MRI-based guidance – all converging toward submillimeter targeting accuracy for moving tumors. His scientific recognition includes: John R. Cameron Young Investigator Award (AAPM 2004) ASTRO Basic Science Research Grant Award (2008) Multiple 'Best in Physics' selections at AAPM/ASTRO meetings (2010, 2014, 2015, 2016) ICCR Young Investigator Award (2007) Dr. Sawant has secured over $4.5 million in research funding as Principal Investigator, including two NIH R01 grants on personalized motion management and radiation injury to pulmonary structures. His industry collaborations with VisionRT and Varian Medical Systems have translated theoretical advances into clinical motion management solutions. He leads active research teams developing orthotopic tumor models and implementing 4D radiotherapy workflows for precision cancer treatment.

Michael Schlottke-LakemperView profile

Professor

• High-Performance Scientific Computing
• Adaptive Multi-Physics Simulations
• Research Software Engineering
• +4 more

Dr. Michael Schlottke-Lakemper is a Professor of High-Performance Scientific Computing at the University of Augsburg, Faculty of Mathematics, Natural Sciences, and Materials Engineering. He previously held positions as an Interim Professor of Computational Mathematics at RWTH Aachen University (2022–2024) and led a research group at the High-Performance Computing Center Stuttgart (HLRS) from 2021 to 2024. His career includes postdoctoral roles at the University of Cologne and RWTH Aachen University/FZ Jülich. Education: Ph.D. in Mechanical Engineering, RWTH Aachen University (2017) Diplom in Aerospace Engineering, University of Stuttgart (2011) His research focuses on adaptive multi-physics simulations, research software engineering for high-performance computing (HPC), and scientific machine learning. Applications span fluid mechanics, aeroacoustics, and astrophysics, with recent work emphasizing robust high-order summation-by-parts methods and Julia-based computational frameworks like Trixi.jl and TrixiParticles.jl. His publications highlight advancements in discontinuous Galerkin methods, entropy stable schemes, and HPC optimization for compressible flows. Scientific contributions include Developing dynamic load balancing algorithms for multiphysics simulations Creating hybrid computational aeroacoustics methods Advancing Julia's adoption in HPC communities Improving error-based step size control in numerical solvers Current teaching activities include graduate seminars on Maschinelles Lernen in Theorie und Praxis and undergraduate courses in Numerische Lineare Algebra . He leads a research team at the University of Augsburg with collaborators across Germany, including Simon Candelaresi, Valentin Churavy, and Niklas Neher.

• Computer architecture and parallel systems
• Power-aware computing
• Process and resource management in HPC systems
• +5 more

Dr. Eishi Arima is a researcher at the Chair of Computer Architecture and Parallel Systems within the Department of Informatics at the Technical University of Munich (TUM). His work focuses on cutting-edge computer architecture and high-performance computing systems, with particular expertise in power-aware computing, resource management, and heterogeneous systems. He actively contributes to numerous international conferences and collaborative research projects addressing challenges in modern computing infrastructure. Dr. Arima's research spans multiple critical areas in computer architecture including memory and storage systems, performance modeling and optimization, hardware/software codesign, and processor microarchitectures. His work demonstrates particular strength in addressing energy efficiency challenges in high-performance computing environments, with numerous publications on power capping, resource partitioning, and sustainable computing approaches. His research bridges theoretical concepts with practical implementations, often incorporating machine learning techniques to optimize system performance under various constraints. Analysis of Dr. Arima's publication record reveals a strong focus on addressing the energy efficiency challenges in modern computing systems. His work consistently targets the intersection of hardware architecture and system-level resource management, with particular emphasis on heterogeneous computing platforms combining CPUs, GPUs, and emerging memory technologies. Over time, his research has evolved from traditional cache and memory system optimizations toward more holistic approaches incorporating machine learning for resource management in power-constrained environments. Recent publications demonstrate increasing attention to sustainability aspects of computing, reflecting broader industry trends toward greener computing solutions. Dr. Arima has served in various organizational capacities for major international conferences including as Program Committee member for SC, IPDPS, and Cluster conferences, and as Program Co-Chair for ACM CF'20. His journal review activities span multiple prestigious publications including IEEE TPDS and Elsevier FGCS. This extensive service demonstrates his recognition as a respected member of the international computer architecture research community. Dr. Arima has mentored numerous students through bachelor's theses, master's theses, and guided research projects. His students have produced research on topics including reinforcement learning for resource management, job scheduling optimization, memory system improvements, and power-aware computing techniques. Several student projects have resulted in publications at reputable conferences, indicating the high quality of research conducted under his supervision. His mentoring covers both theoretical aspects of computer architecture and practical implementation challenges in real-world systems. Dr. Arima is actively involved in multiple research projects including SEANERGYS (EuroHPC), PlasmaPEPS, OpenCUBE, DaREXA-F, ScalNEXT, PDexa, MUNIQC-ATOMS, BB-KI_Chips, QuaST, and Q-DESSI. These projects address various aspects of high-performance computing, from energy efficiency to quantum computing integration. His work contributes to the development of next-generation computing infrastructure that balances performance requirements with sustainability concerns.

• Computer Architecture
• Deep Learning Accelerators
• Heterogeneous Computing
• +4 more

Pedro Petersen Moura Trancoso is a Full Professor at the Department of Computer Engineering , Chalmers University of Technology, Sweden. His research focuses on deep learning accelerators , heterogeneous computing , energy-efficient architectures , and memory system optimization for IoT and edge devices. Key projects: AutoPIM (autonomous vehicle accelerators), VEDLIoT (efficient AIoT), eProcessor (European processor ecosystem), PRIME (PIM systems) Collaborations: European Commission, Swedish Research Council, Swedish Foundation for Strategic Research Research Trends : Hybrid CNN/GPU/FPGA Acceleration On-Chip/Scratchpad Memory Optimization Adaptive Resource Allocation for Energy Efficiency Hardware-Software Co-Design for AIoT Publications demonstrate leadership in deep learning hardware , heterogeneous memory systems , and edge computing architectures . Key journals: IEEE ISPASS, ACM Computing Frontiers, DATE Conference.

• Computer Architecture
• High Performance Computing
• Parallel Programming
• +5 more

Andrea Marongiu is an Associate Professor at the Department of Physical, Computer and Mathematical Sciences at the University of Modena and Reggio Emilia, specifically affiliated with the Mathematics department. He maintains an active research profile while teaching multiple courses in computer architecture and parallel systems. His research interests span computer architecture, high performance computing, parallel programming, and embedded systems. Marongiu focuses particularly on memory systems, heterogeneous computing architectures, FPGA-based acceleration, and real-time performance analysis. His work bridges theoretical foundations with practical implementation challenges in modern computing systems, with special attention to predictable execution models and quality of service guarantees. Analysis of his recent publications reveals a strong emphasis on memory bandwidth management in heterogeneous systems, particularly focusing on FPGA-based architectures and multicore SoCs. His research trajectory shows consistent work in memory interference analysis, PREM (Predictable Execution Model) scheduling techniques, and fine-grained QoS control mechanisms. The publications demonstrate a progression from general parallel programming concepts toward increasingly specialized techniques for resource-constrained environments like autonomous vehicles and edge computing devices. Marongiu teaches several advanced computer science courses including Computer Architecture I & II, Compilers, High Performance Computing, and Electronic Calculators across multiple degree programs. His teaching approach emphasizes both theoretical foundations and practical implementation, with a focus on RISC-V architecture and modern parallel programming techniques. The course materials indicate he incorporates hands-on laboratory work as an essential component of his pedagogy, particularly in areas like compiler construction and parallel programming.

Klaus MuellerView profile

Professor

• Visual Analytics
• Explainable Machine Learning and AI
• Algorithmic Fairness and Transparency
• +7 more

Klaus Mueller is a Professor in the Computer Science Department at Stony Brook University , with additional appointments in Biomedical Engineering and Radiology. He serves as Director of the Visual Analytics and Imaging (VAI) Lab, Liaison for the SUNY Korea CS Program, and Interim Chair of the Department of Technology and Society . His career spans roles at Brookhaven National Lab and leadership positions at SUNY Korea. Dr. Mueller earned his PhD in Computer and Information Science (1998), MS in Computer and Information Science (1996), and MS in Biomedical Engineering (1990) from The Ohio State University , alongside a BS in Electrical Engineering (1987) from the Polytechnic University of Ulm, Germany. His research focuses on visual analytics , explainable AI , algorithmic fairness , computational imaging , and medical imaging . He has pioneered GPU-accelerated CT reconstruction techniques, bias mitigation frameworks (e.g., D-BIAS), and tools like DOMINO for causal reasoning. His work bridges data science , human-computer interaction , and medical applications , often integrating large language models for visualization tasks. Recent publications highlight advances in multivariate volume rendering , LLM-driven bias detection , and mDDPM-based medical image synthesis . His articles span IEEE Transactions , Nature Machine Intelligence , and conferences like IEEE VIS and ACM CHI . Award highlights include NSF CAREER (2000), SUNY Chancellor Award (2011), IEEE Golden Core Award (2016, 2022), induction into the National Academy of Inventors (2018), and elevation to IEEE Fellow (2024). He has chaired major conferences and served as Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics (2019-2022). He teaches graduate and undergraduate courses in visualization , medical imaging , and GPGPU programming , and leads the Visual Analytics Seminar (CSE 648). His lab ( VAI Lab ) fosters interdisciplinary research in GPU-accelerated analytics and ethical AI.

Erin Claire CarsonView profile

Assistant Professor

• Numerical Linear Algebra
• Parallel Computing
• High-Performance Computing
• +7 more

Erin Claire Carson is an Assistant Professor at the Department of Numerical Mathematics, Faculty of Mathematics and Physics, Charles University, Prague. A specialist in numerical linear algebra and high-performance computing, she leads the ERC Starting Grant project InEXASCALE focused on exascale algorithms. Her research explores mixed precision arithmetic, communication-avoiding Krylov subspace methods, and stability analysis in finite precision. Ph.D., University of California, Berkeley (2015) Courant Instructor, New York University (2015-2018) Postdoctoral Researcher and PRIMUS Fellow, Charles University (2018-2022) Dr. Carson's work bridges theoretical analysis with practical implementations for supercomputers. Her recent publications include advancements in low-synchronization orthogonalization, silent error detection, and multilevel sampling techniques. She received the 2025 Wilkinson Prize from SIAM for outstanding contributions to numerical analysis. Current trends in her research involve: Exploiting mixed precision arithmetic for algorithm acceleration Developing stable communication-avoiding Krylov methods Optimizing numerical stability in GPU-based solvers Understanding error propagation in multistage refinements Scientific honors: 2025 SIAM Wilkinson Prize in Numerical Analysis and Scientific Computing 2023 ERC Starting Grant recipient 2019-2022 PRIMUS Research Fellow She supervises PhD and Master’s students while teaching advanced courses in numerical linear algebra and high-performance computing. Her work has been featured in WIRED and Forbes Czech Republic for improving supercomputer algorithms.

• Radiation Dosimetry
• Medical Physics
• Radiotherapy
• +5 more

Dagmara Kulig, PhD, Eng., serves as a Lecturer at the Department of Particle Interactions and Detection within the Faculty of Physics and Applied Computer Science at AGH University of Science and Technology in Kraków, Poland. Her research focuses on advancing radiation measurement technologies for medical applications, particularly in radiotherapy quality assurance and dosimetry innovation. Her primary research domains include Radiation Dosimetry, Medical Physics, and Radiotherapy, with specialized expertise in Optically Stimulated Luminescence (OSL) and Thermoluminescence (TL) phenomena. Kulig investigates novel luminescent materials—especially LiMgPO 4 -based compounds—and develops 3D-printed scintillators for precise dose measurement. Her work bridges experimental physics with clinical oncology through computational modeling and deep learning applications for treatment planning optimization. Analysis of her 15 most recent publications (2016-2025) reveals a consistent trajectory in radiation monitoring systems, with increasing emphasis on modular detector architectures and AI-driven medical imaging. The Dose-3D project represents a significant computational contribution, while her material science work on LiMgPO 4 dosimeters addresses critical challenges in signal stability and sensitivity. Recent publications demonstrate growing integration of 3D printing and deep learning for personalized radiotherapy solutions. Scientific awards: No awards documented in provided information Advising and grants: No student supervision details available No external funding sources specified Laboratory engagement: Core contributor to Dose-3D project developing Monte Carlo simulation platforms Experimental work on radiation detector systems at Department of Particle Interactions and Detection Material synthesis and characterization for luminescent dosimeters

Bingcong LiView profile

Researcher

• Machine Learning
• Optimization
• Signal Processing
• +3 more

Bingcong Li is a postdoctoral researcher at ETH Zurich collaborating with Prof. Niao He and the ODI group. Previously, they completed doctoral studies at the University of Minnesota under Prof. Georgios B. Giannakis, followed by industry experience focused on large language models (LLMs). Education includes a PhD from the University of Minnesota under Prof. Georgios B. Giannakis. Research centers on making computation efficient, accessible, and affordable across heterogeneous resources—from GPU clusters to consumer hardware—through interdisciplinary approaches combining deep learning, optimization, and signal processing. Key research areas address foundational computing architectures, large-scale system sustainability, and personalized AI access. Their work develops theoretically grounded methods for explainable systems, with recent focus on LLM fine-tuning efficiency. Publication trends show consistent contributions to top conferences (NeurIPS, ICML, ICLR) with emphasis on optimization techniques for resource-constrained LLM deployment. Their advising and grant activities aren't explicitly detailed, though they actively participate in academic service through conference talks (EUROPT 2025, ICASSP 2025) and co-organizing events like the Efficient LLMs Fine-tuning Track at AI+X Summit. Lab affiliation centers on ETH Zurich's ODI group under Prof. Niao He, focusing on optimization-driven AI solutions.

Thomas WiemannView profile

Professor

• Autonomous Robotics
• 3D Mapping
• SLAM
• +7 more

Thomas Wiemann is a temporary Professor for Autonomous Robotics at Osnabrück University and a researcher at the German Research Center for Artificial Intelligence (DFKI) , group Kooperative und Autonome Systeme . He has been active since 2007, focusing on 3D mapping, SLAM, and semantic interpretation of sensor data for robotics. He received his Dr. rer. nat. in 2013 and his habilitation in 2020. His work is widely recognized, with awards such as the Karman Innovation Award (2007) and the Intevation Free Software Award (2014). Education: Habilitation in Computer Science, Osnabrück University (2020) Doctorate (Dr. rer. nat.) in Computer Science, Osnabrück University (2013) M.Sc. in Physics and Computer Science, Osnabrück University (2007) B.Sc. in Physics and Computer Science, Osnabrück University (2005) Research Focus: Thomas Wiemann's research is centered on autonomous robotics , particularly in 3D mapping , SLAM , and semantic environment understanding . His work includes the automatic generation of polygonal maps from point cloud data, large-scale 3D reconstruction, hyperspectral data integration, and hardware-accelerated SLAM systems using FPGAs and GPUs. He has developed open-source tools like the Las Vegas Reconstruction Toolkit (LVR) and contributed to ROS packages for 3D mapping and navigation. Scientific Awards: Karman Innovation Award (2007) for his master’s thesis Intevation Free Software Award (2014) for his contributions to open-source software Advising & Grants: He has supervised over 60 bachelor’s and master’s theses, primarily in the areas of 3D mapping, SLAM, and robotics systems. His projects include SOILAssist (2019–2021), focusing on sustainable agriculture using robotics, and 3DinOS (2016–2017) for 3D documentation of historical buildings. He has also worked on DFG-funded projects like RoboRithmics . Labs & Teams: He is part of the Knowledge Based Systems Group at Osnabrück University and collaborates with the DFKI Robotics Innovation Center . His lab focuses on advanced 3D perception, semantic mapping, and energy-efficient robotic systems.

Arne SchmitzView profile

Researcher

• Radio Wave Propagation
• Computer Graphics
• Wireless Network Simulation
• +3 more

Dr. Arne Schmitz is a researcher at the Department of Computer Science, RWTH Aachen University, specializing in computational methods for wireless communications and computer graphics. His work bridges radio wave propagation physics with advanced visualization techniques, focusing on practical applications for mobile network planning and mobile device interfaces. His primary research domains include: Radio wave propagation modeling in urban environments GPU-accelerated beam/ray tracing algorithms Antenna pattern compression using spherical harmonics Real-time 3D visualization for resource-constrained mobile devices Ad-hoc multi-display systems for collaborative mobile applications Analysis of his publication timeline reveals an evolution from foundational radio propagation models (2006) toward increasingly sophisticated urban simulation frameworks (2011-2012), with consistent emphasis on computational efficiency. His most impactful contributions integrate computer graphics rendering techniques with telecommunications engineering, particularly in adapting beam tracing for radio wave simulation and developing novel mobile visualization paradigms that overcome hardware limitations through client-server architectures. Dr. Schmitz maintains a strong collaborative relationship with Professor Leif Kobbelt and colleagues at RWTH Aachen, with co-authorship appearing in 9 of his 10 documented publications. His research demonstrates consistent funding support through participation in IEEE and ACM conferences, though specific grant details are not disclosed in the available materials.